This application claims the benefit of priority of U.S. Nonprovisional Application Serial No. 17/695,160, filed on March 15, 2022, and entitled “APPARATUS AND METHOD FOR GENERATING A VIDEO RECORD USING AUDIO” and U.S. Nonprovisional Application Serial No. 18/084,816, filed on December 20, 2022, and entitled “APPARATUS AND METHOD FOR GENERATING A VIDEO RECORD USING AUDIO,” both of which are incorporated by reference herein in their entirety.

FIELD OF THE INVENTION

The present invention generally relates to the field of performance matching. In particular, the present invention is directed to an apparatus and methods for generating a video record using audio.

BACKGROUND

Video content (including short-form video content) has steadily been rising in popularity for many years. Its advantages as a communication medium are manifest. However, present ways of doing business, which are commonly reliant upon the written word, are not capable of making optimal use of this form of media.

SUMMARY OF THE DISCLOSURE

In an aspect, an apparatus for generating a video record using audio is presented. The apparatus comprises at least a processor and a memory communicatively connected to the at least a processor. The memory contains instructions configuring the at least a processor to receive a user input from a user, select a set of record generation questions for the user as a function of the user input, transmit an audio question to the user as a function of the selected set of record generation questions, record a user response as a function of the audio question, and generate a video record as a function of the recorded user responses.

In another aspect, a method for generating a video record using audio is illustrated. The method comprises receiving a user input from a user, selecting a set of record generation questions for the user as a function of the user input, transmitting an audio question to the user as a function of the selected set of record generation questions, recording a user response as a function of the audio question, and generating a video record as a function of the recorded user responses.

These and other aspects and features of non-limiting embodiments of the present invention will become apparent to those skilled in the art upon review of the following description of specific non-limiting embodiments of the invention in conjunction with the accompanying drawings.

DESCRIPTION OF DRAWINGS

For the purpose of illustrating the invention, the drawings show aspects of one or more embodiments of the invention. However, it should be understood that the present invention is not limited to the precise arrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of an apparatus for generating a video record using audio;

FIG. 2 is a block diagram illustrating an exemplary embodiment of a video record database;

FIG. 3 is a block diagram illustrating an exemplary embodiment of an immutable sequential listing;

FIG. 4 is a block diagram illustrating an exemplary embodiment of a machine learning model;

FIG. 5 is a flow diagram illustrating an exemplary embodiment of a method for generating a video record using audio;

FIG. 6 is a block diagram of an exemplary system for parsing and correlating solicitation video content;

FIG. 7 illustrates an exemplary video frame;

FIG. 8 is a graph depicting an exemplary association between a subject and job descriptions;

FIG. 9 is a table representing an exemplary association between multiple candidate subjects and a job description;

FIG. 10 illustrates an exemplary subject database using a block diagram;

FIG. 11 is a block diagram illustrating an exemplary job description database;

FIG. 12 illustrates by way of a block diagram an exemplary training data database; FIG 13 is a flow diagram of an exemplary method of parsing and correlating solicitation video content;

FIG. 14 is a block diagram illustrating an exemplary embodiment of an apparatus for removing bias from an application;

FIG. 15 is a flow diagram illustrating an exemplary embodiment of a method of removing bias in an application; and

FIG. 16 is a block diagram of a computing system that can be used to implement any one or more of the methodologies disclosed herein and any one or more portions thereof.

The drawings are not necessarily to scale and may be illustrated by phantom lines, diagrammatic representations and fragmentary views. In certain instances, details that are not necessary for an understanding of the embodiments or that render other details difficult to perceive may have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed to an apparatus and method for generating a video record using audio. In an embodiment, an apparatus may be configured to receive a user input from a user. An apparatus may be configured to select a set of record generation questions for the user as a function of the user input. An apparatus may be configured to play a question audio as a function of the selected set of record generation questions. An apparatus may be configured to record a user response as a function of the question audio. An apparatus may be configured to generate a video record as a function of the recorded user responses.

In an embodiment, methods and systems described herein may perform or implement one or more aspects of a cryptographic system. In one embodiment, a cryptographic system is a system that converts data from a first form, known as “plaintext,” which is intelligible when viewed in its intended format, into a second form, known as “ciphertext,” which is not intelligible when viewed in the same way. Ciphertext may be unintelligible in any format unless first converted back to plaintext. In one embodiment, a process of converting plaintext into ciphertext is known as “encryption.” Encryption process may involve the use of a datum, known as an “encryption key,” to alter plaintext. Cryptographic system may also convert ciphertext back into plaintext, which is a process known as “decryption.” Decryption process may involve the use of a datum, known as a “decryption key,” to return the ciphertext to its original plaintext form. In embodiments of cryptographic systems that are “symmetric,” decryption key is essentially the same as encryption key: possession of either key makes it possible to deduce the other key quickly without further secret knowledge. Encryption and decryption keys in symmetric cryptographic systems may be kept secret and shared only with persons or entities that the user of the cryptographic system wishes to be able to decrypt the ciphertext. One example of a symmetric cryptographic system is the Advanced Encryption Standard (“AES”), which arranges plaintext into matrices and then modifies the matrices through repeated permutations and arithmetic operations with an encryption key.

In embodiments of cryptographic systems that are “asymmetric,” either encryption or decryption key cannot be readily deduced without additional secret knowledge, even given the possession of a corresponding decryption or encryption key, respectively; a common example is a “public key cryptographic system,” in which possession of the encryption key does not make it practically feasible to deduce the decryption key, so that the encryption key may safely be made available to the public. An example of a public key cryptographic system is RSA, in which an encryption key involves the use of numbers that are products of very large prime numbers, but a decryption key involves the use of those very large prime numbers, such that deducing the decryption key from the encryption key requires the practically infeasible task of computing the prime factors of a number which is the product of two very large prime numbers. Another example is elliptic curve cryptography, which relies on the fact that given two points P and Q on an elliptic curve over a finite field, and a definition for addition where A + B = -R, the point where a line connecting point A and point B intersects the elliptic curve, where “0,” the identity, is a point at infinity in a projective plane containing the elliptic curve, finding a number k such that adding P to itself k times results in Q is computationally impractical, given correctly selected elliptic curve, finite field, and P and Q.

In some embodiments, systems and methods described herein produce cryptographic hashes, also referred to by the equivalent shorthand term “hashes.” A cryptographic hash, as used herein, is a mathematical representation of a lot of data, such as files or blocks in a block chain as described in further detail below; the mathematical representation is produced by a lossy “oneway” algorithm known as a “hashing algorithm.” Hashing algorithm may be a repeatable process; that is, identical lots of data may produce identical hashes each time they are subjected to a particular hashing algorithm. Because hashing algorithm is a one-way function, it may be impossible to reconstruct a lot of data from a hash produced from the lot of data using the hashing algorithm. In the case of some hashing algorithms, reconstructing the full lot of data from the corresponding hash using a partial set of data from the full lot of data may be possible only by repeatedly guessing at the remaining data and repeating the hashing algorithm; it is thus computationally difficult if not infeasible for a single computer to produce the lot of data, as the statistical likelihood of correctly guessing the missing data may be extremely low. However, the statistical likelihood of a computer of a set of computers simultaneously attempting to guess the missing data within a useful timeframe may be higher, permitting mining protocols as described in further detail below.

In an embodiment, hashing algorithm may demonstrate an “avalanche effect,” whereby even extremely small changes to lot of data produce drastically different hashes. This may thwart attempts to avoid the computational work necessary to recreate a hash by simply inserting a fraudulent datum in data lot, enabling the use of hashing algorithms for “tamperproofing” data such as data contained in an immutable ledger as described in further detail below. This avalanche or “cascade” effect may be evinced by various hashing processes; persons skilled in the art, upon reading the entirety of this disclosure, will be aware of various suitable hashing algorithms for purposes described herein. Verification of a hash corresponding to a lot of data may be performed by running the lot of data through a hashing algorithm used to produce the hash. Such verification may be computationally expensive, albeit feasible, potentially adding up to significant processing delays where repeated hashing, or hashing of large quantities of data, is required, for instance as described in further detail below. Examples of hashing programs include, without limitation, SHA256, a NIST standard; further current and past hashing algorithms include Winternitz hashing algorithms, various generations of Secure Hash Algorithm (including "SHA-1," “SHA-2,” and “SHA-3”), “Message Digest” family hashes such as “MD4,” “MD5,” “MD6,” and “RIPEMD,” Keccak, “BLAKE” hashes and progeny (e.g., “BLAKE2,” “BLAKE-256,” “BLAKE-512,” and the like), Message Authentication Code (“MAC”)-family hash functions such as PMAC, OMAC, VMAC, HMAC, and UMAC, Polyl305-AES, Elliptic Curve Only Hash (“ECOH”) and similar hash functions, Fast-Syndrome-based (FSB) hash functions, GOST hash functions, the Grostl hash function, the HAS- 160 hash function, the JH hash function, the RadioGatun hash function, the Skein hash function, the Streebog hash function, the SWTFFT hash function, the Tiger hash function, the Whirlpool hash function, or any hash function that satisfies, at the time of implementation, the requirements that a cryptographic hash be deterministic, infeasible to reverse-hash, infeasible to find collisions, and have the property that small changes to an original message to be hashed will change the resulting hash so extensively that the original hash and the new hash appear uncorrelated to each other. A degree of security of a hash function in practice may depend both on the hash function itself and on characteristics of the message and/or digest used in the hash function. For example, where a message is random, for a hash function that fulfills collision-resistance requirements, a brute-force or “birthday attack” may to detect collision may be on the order of O(2 ^n/2 ) for n output bits; thus, it may take on the order of 2 ²⁵⁶ operations to locate a collision in a 512 bit output “Dictionary” attacks on hashes likely to have been generated from a non-random original text can have a lower computational complexity, because the space of entries they are guessing is far smaller than the space containing all random permutations of bits. However, the space of possible messages may be augmented by increasing the length or potential length of a possible message, or by implementing a protocol whereby one or more randomly selected strings or sets of data are added to the message, rendering a dictionary attack significantly less effective.

In some embodiments, a secure proof is used. A “secure proof,” as used in this disclosure, is a protocol whereby an output is generated that demonstrates possession of a secret, such as device-specific secret, without demonstrating the entirety of the device-specific secret; in other words, a secure proof by itself, is insufficient to reconstruct the entire device-specific secret, enabling the production of at least another secure proof using at least a device-specific secret. A secure proof may be referred to as a “proof of possession” or “proof of knowledge” of a secret. Where at least a device-specific secret is a plurality of secrets, such as a plurality of challenge-response pairs, a secure proof may include an output that reveals the entirety of one of the plurality of secrets, but not all of the plurality of secrets; for instance, secure proof may be a response contained in one challenge-response pair. In an embodiment, proof may not be secure; in other words, proof may include a one-time revelation of at least a device-specific secret, for instance as used in a single challenge-response exchange.

Secure proof may include a zero-knowledge proof, which may provide an output demonstrating possession of a secret while revealing none of the secret to a recipient of the output; zero-knowledge proof may be information-theoretically secure, meaning that an entity with infinite computing power would be unable to determine secret from output. Alternatively, zero-knowledge proof may be computationally secure, meaning that determination of secret from output is computationally infeasible, for instance to the same extent that determination of a private key from a public key in a public key cryptographic system is computationally infeasible. Zero-knowledge proof algorithms may generally include a set of two algorithms, a prover algorithm, or “P,” which is used to prove computational integrity and/or possession of a secret, and a verifier algorithm, or “V” whereby a party may check the validity of P. Zero-knowledge proof may include an interactive zero-knowledge proof, wherein a party verifying the proof must directly interact with the proving party; for instance, the verifying and proving parties may be required to be online, or connected to the same network as each other, at the same time. Interactive zero-knowledge proof may include a “proof of knowledge” proof, such as a Schnorr algorithm for proof on knowledge of a discrete logarithm, in a Schnorr algorithm, a prover commits to a randomness r, generates a message based on r, and generates a message adding r to a challenge c multiplied by a discrete logarithm that the prover is able to calculate; verification is performed by the verifier who produced c by exponentiation, thus checking the validity of the discrete logarithm. Interactive zero-knowledge proofs may alternatively or additionally include sigma protocols. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various alternative interactive zero-knowledge proofs that may be implemented consistently with this disclosure.

Alternatively, zero-knowledge proof may include a non-interactive zero-knowledge, proof, or a proof wherein neither party to the proof interacts with the other party to the proof; for instance, each of a party receiving the proof and a party providing the proof may receive a reference datum which the party providing the proof may modify or otherwise use to perform the proof. As a non-limiting example, zero-knowledge proof may include a succinct non-interactive arguments of knowledge (ZK-SNARKS) proof, wherein a “trusted setup” process creates proof and verification keys using secret (and subsequently discarded) information encoded using a public key cryptographic system, a prover runs a proving algorithm using the proving key and secret information available to the prover, and a verifier checks the proof using the verification key; public key cryptographic system may include RSA, elliptic curve cryptography, ElGamal, or any other suitable public key cryptographic system. Generation of trusted setup may be performed using a secure multiparty computation so that no one party has control of the totality of the secret information used in the trusted setup; as a result, if any one party generating the trusted setup is trustworthy, the secret information may be unrecoverable by malicious parties. As another non-limiting example, non-interactive zero-knowledge proof may include a Succinct Transparent Arguments of Knowledge (ZK-STARKS) zero-knowledge proof. In an embodiment, a ZK-STARKS proof includes a Merkle root of a Merkle tree representing evaluation of a secret computation at some number of points, which may be 1 billion points, plus Merkle branches representing evaluations at a set of randomly selected points of the number of points; verification may include determining that Merkle branches provided match the Merkle root, and that point verifications at those branches represent valid values, where validity is shown by demonstrating that all values belong to the same polynomial created by transforming the secret computation. In an embodiment, ZK-STARKS does not require a trusted setup.

Zero-knowledge proof may include any other suitable zero-knowledge proof. Zeroknowledge proof may include, without limitation bulletproofs. Zero-knowledge proof may include a homomorphic public-key cryptography (hPKC)-based proof. Zero-knowledge proof may include a discrete logarithmic problem (DLP) proof. Zero-knowledge proof may include a secure multi-party computation (MPC) proof. Zero-knowledge proof may include, without limitation, an incrementally verifiable computation (IVC). Zero-knowledge proof may include an interactive oracle proof (IOP). Zero-knowledge proof may include a proof based on the probabilistically checkable proof (PCP) theorem, including a linear PCP (LPCP) proof. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various forms of zero-knowledge proofs that may be used, singly or in combination, consistently with this disclosure.

In an embodiment, secure proof is implemented using a challenge-response protocol. In an embodiment, this may function as a one-time pad implementation; for instance, a manufacturer or other trusted party may record a series of outputs (“responses”) produced by a device possessing secret information, given a series of corresponding inputs (“challenges”), and store them securely. In an embodiment, a challenge-response protocol may be combined with key generation. A single key may be used in one or more digital signatures as described in further detail below, such as signatures used to receive and/or transfer possession of crypto-currency assets; the key may be discarded for future use after a set period of time. In an embodiment, varied inputs include variations in local physical parameters, such as fluctuations in local electromagnetic fields, radiation, temperature, and the like, such that an almost limitless variety of private keys may be so generated. Secure proof may include encryption of a challenge to produce the response, indicating possession of a secret key. Encryption may be performed using a private key of a public key cryptographic system, or using a private key of a symmetric cryptographic system; for instance, trusted party may verify response by decrypting an encryption of challenge or of another datum using either a symmetric or public-key cryptographic system, verifying that a stored key matches the key used for encryption as a function of at least a device-specific secret. Keys may be generated by random variation in selection of prime numbers, for instance for the purposes of a cryptographic system such as RSA that relies prime factoring difficulty. Keys may be generated by randomized selection of parameters for a seed in a cryptographic system, such as elliptic curve cryptography, which is generated from a seed. Keys may be used to generate exponents for a cryptographic system such as Diffie-Helman or ElGamal that are based on the discrete logarithm problem.

In some embodiments, a “digital signature,” as used herein, includes a secure proof of possession of a secret by a signing device, as performed on provided element of data, known as a “message.” A message may include an encrypted mathematical representation of a file or other set of data using the private key of a public key cryptographic system. Secure proof may include any form of secure proof as described above, including without limitation encryption using a private key of a public key cryptographic system as described above. Signature may be verified using a verification datum suitable for verification of a secure proof; for instance, where secure proof is enacted by encrypting message using a private key of a public key cryptographic system, verification may include decrypting the encrypted message using the corresponding public key and comparing the decrypted representation to a purported match that was not encrypted; if the signature protocol is well-designed and implemented correctly, this means the ability to create the digital signature is equivalent to possession of the private decryption key and/or device-specific secret. Likewise, if a message making up a mathematical representation of file is well-designed and implemented correctly, any alteration of the file may result in a mismatch with the digital signature; the mathematical representation may be produced using an alteration-sensitive, reliably reproducible algorithm, such as a hashing algorithm as described above. A mathematical representation to which the signature may be compared may be included with signature, for verification purposes; in other embodiments, the algorithm used to produce the mathematical representation may be publicly available, permitting the easy reproduction of the mathematical representation corresponding to any file.

In some embodiments, digital signatures may be combined with or incorporated in digital certificates. In one embodiment, a digital certificate is a file that conveys information and links the conveyed information to a “certificate authority” that is the issuer of a public key in a public key cryptographic system. Certificate authority in some embodiments contains data conveying the certificate authority’s authorization for the recipient to perform a task. The authorization may be the authorization to access a given datum. The authorization may be the authorization to access a given process. In some embodiments, the certificate may identify the certificate authority. The digital certificate may include a digital signature.

In some embodiments, a third party such as a certificate authority (CA) is available to verify that the possessor of the private key is a particular entity; thus, if the certificate authority may be trusted, and the private key has not been stolen, the ability of an entity to produce a digital signature confirms the identity of the entity and links the file to the entity in a verifiable way. Digital signature may be incorporated in a digital certificate, which is a document authenticating the entity possessing the private key by authority of the issuing certificate authority and signed with a digital signature created with that private key and a mathematical representation of the remainder of the certificate. In other embodiments, digital signature is verified by comparing the digital signature to one known to have been created by the entity that purportedly signed the digital signature; for instance, if the public key that decrypts the known signature also decrypts the digital signature, the digital signature may be considered verified. Digital signature may also be used to verify that the file has not been altered since the formation of the digital signature.

Referring now to FIG. 1, an exemplary embodiment of an apparatus 100 for generating a video record using audio is illustrated. Video record may be any video record or video as described further below in this disclosure. Apparatus 100 may include a computing device. Computing device may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Computing device may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. Computing device may include a single computing device operating independently or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices 104 may be included together in a single computing device or in two or more computing devices 104. Computing device may interface or communicate with one or more additional devices as described below in further detail via a network interface device. Network interface device may be utilized for connecting computing device to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g, a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g, the Internet, an enterprise network), a local area network (e.g, a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g, a mobile communications provider data and/or voice network), a direct connection between two computing devices 104, and any combinations thereof. A network may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g, data, software etc.) may be communicated to and/or from a computer and/or a computing device. Computing device may include but is not limited to, for example, a computing device or cluster of computing devices 104 in a first location and a second computing device or cluster of computing devices 104 in a second location. Computing device may include one or more computing devices 104 dedicated to data storage, security, distribution of traffic for load balancing, and the like. Computing device may distribute one or more computing tasks as described below across a plurality of computing devices 104 of computing device, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices 104. Computing device may be implemented using a “shared nothing” architecture in which data is cached at the worker; in an embodiment, this may enable scalability of apparatus 100 and/or computing device. Still referring to FIG. 1, computing device may be designed and/or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, computing device may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Computing device may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and/or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and/or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and/or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and/or parallel processing.

Continuing to refer to FIG. 1, apparatus 100 may be designed and configured to receive a user input 104 from a user. A “user input” is an element of data containing information concerning the user’s interest in certain job positions or careers. User input 104 may include any information about possible careers a user is interested in working in. User input 104 may include any information about job openings that a user is interested in applying to or currently working in. User input 104 may include any information about any credentials a user may have that may make them eligible to work in certain roles or positions. For example, but without limitation, user input 104 may be that the user is looking for positions in the legal field. Another example, without limitation, may be that the user is currently working in tech but is interested in looking for accounting roles instead. User input 104 may also include the user’s name, gender, past work experience, social cues, language, location, skills, awards, e-mail address, or any other piece of information that may be relevant to a career. User input 104 may be retrieved from a user input database, which may be implemented in any manner suitable for implementation of a database as described in this disclosure, and/or may be received from a user interface device. “User interface device” may include without limitation, a display in communication with apparatus 100, wherein a display may include any display as described herein. User interface device may include an additional apparatus or computing device, such as a mobile device, laptop, desktop, computer, and the like. In an embodiment, user interface device may have a touch screen to interact with the user. User may input user input 104 through user interface device. User input 104 may also be received as a function of a user application. A “user application” is any document relaying information about the user, such as an application for employment is a standard business document that is prepared with questions deemed relevant by employers. User input 104 may be extracted from user application once user submits it to the apparatus. Any of user application and/or user inputs 104 may be input via user device, and/or retrieved from user input database. User input 104 may alternatively or additionally be modified to prevent bias, as described further below in this disclosure.

Still referring to FIG. 1, apparatus 100 may validate a user’s identity as a function of the user input 104. In order for the user to make a video record, the apparatus must first confirm if user input 104 conveys information concerning matched to the user the video is being generated for. Validating an identity of the user may include apparatus 100 confirming that the user input matches to the user initiating generation of the video record. An “identity” of a user is a collection of characteristics that are unique to the user and cannot be duplicated. For example, a user’s name may be extracted from a user application and must be validated that the user requesting the generation of a video record is the same user who submitted user application. Validation may be done through the user interface device, by the collection of data from a database, or on an immutable sequential listing. An “immutable sequential listing,” as used in this disclosure, is a data structure that places data entries in a fixed sequential arrangement, such as a temporal sequence of entries and/or blocks thereof, where the sequential arrangement, once established, cannot be altered or reordered. An immutable sequential listing may be, include and/or implement an immutable ledger, where data entries that have been posted to the immutable sequential listing cannot be altered; a “blockchain” is a system of recording information in a way that makes it difficult or impossible to change, hack, or cheat the system. Immutable sequential listing and blockchain is further explained herein with reference to FIG. 3. Verification may also be done through the use of a username and password, or any other type of test to ensure identity. Validating an identity of the user may also include a user validation from the user interface device. A “user validation” is a specific input from the user that with affirm the validation of the user’s identity. User validation may be the click of a button, word command, password entry, voice command, thumb print, or the like. Additionally, user validation may involve getting a digital signature from the user; the signature may then be validated by determining if the signature inputted by the user matches the signature linked to the user on a blockchain. User validation is a test to ensure identity of the user generating video record matches the identity of the user that the video record is being made for.

Still referring to FIG. 1, apparatus 100 may also be configured to select a set of resume generation questions 108 for the user as a function of the user input 104. In this disclosure, a “record generation questions” are a plurality of questions that are asked to the user to be used in the generation of the video record. Set of record generation questions 108 may be selected based on possible careers or position openings that a user currently works in or is interested in working in, based on user input 104. For example, user input 104 that indicates the user is qualified to work as a chef may cause the processor to select questions related to cooking experience, recipe generation, and the like. Selection of set of record generation questions 108 may include the use of machine-learning. For example, how a user responds to a question may prompt the processor to select another question that asks for more details or follows up on a particular point based on the user’s response. User input 104 or user historical data may be classified to record generation questions using a classifier. Classifier may be further explained below. Training data for the classifier may include any of the user inputs 104 or user historical data as explained herein. For example, inputs for training data may include that the user is interested in marketing positions, has worked retail in the past, possesses skills in coding, etc. Training data is used to train the classifier to output record generation questions most relevant to the training data inputs. Training data may then output a record generation question relevant to the input or historical data used. For example, without limitation, if training data inputs that user is interested in marketing positions, then classifier will output questions asking more information about why the user is interested in marketing positions. Another example, without limitation, is if the user inputs that they possess skills in coding, then the classifier will output record generation records asking about their skills in coding, such as what software the user may be familiar with. Apparatus 100 may generate classifier using a classification algorithm, defined as a processes whereby an apparatus 100 derives, from training data, a model known as a “classifier” for sorting inputs into categories or bins of data. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher’s linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers.

Additionally, pre-determined set of record generation questions may contain information that is usually found on a user’s resume and other information related to a user’s career or job interests. User information or history could be in the user input database, could be linked in the database to user IDs on a blockchain, or could be recorded in the blockchain, etc. Still referring to FIG. 1, apparatus 100 may select a set of record generation questions 108 by querying a question database 112. In this disclosure, a “question database” is a database containing record generation questions to be asked to users. In an embodiment, question database 112 may include any data structure for ordered storage and retrieval of data, which may be implemented as a hardware or software module. Question database may be implemented, without limitation, as a relational database, a key-value retrieval datastore such as a NOSQL database, or any other format or structure for use as a datastore that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Data entries in a question database 112 may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in a relational database. Classifier may classify questions to query elements, which are labels for categories of questions, etc., that could then be used to query question database 112. Each user input 104 could be matched to a query element that is synonymous or near- synonymous using a language processing module, which is further explained below. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a questions database 1 12 may reflect categories, cohorts, and/or populations of data consistently with this disclosure.

Still referring to FIG. 1, apparatus 100 may be configured to further transmit an audio question 116 to the user as a function of the selected set of record generation questions 108audio question 116. In this disclosure, a “question audio” is a recording of a record generation question that is asked to the user. Audio question 116 may be a recording of someone asking a record generation question from the set of record generation questions 108 concerning the user or may be a computer-generated voice asking a record generation question. Processor may be configured to play question audio through some sort of speaker or interface. Speaker or interface may include the same user interface device explained above. A machine-learning model, as further explained below, may be used to listen to the user response 120 and generate a subsequent record generation question based on the previous user response 120. For example, but without limitation, a user response may be used as user input 104 and then classified to query elements and/or questions in question database 112; other previous inputs pr user history may also by classified to these query elements and/or questions. For example, but without limitation, if the user mentions an interest in a career change in a user response 120, apparatus 100 may then play a audio question 116 of a record generation question asking the user for mor information about that career change. Question audio may contain a plurality of audio elements. In this disclosure, a “plurality of audio elements” are elements of data that comprise the audio question 116 and the user response 120. Plurality of audio elements may be extracted from the user response 120 so apparatus can classify them and help generate the next question in the set of record generation question 108.

Continuing to refer to FIG. 1, apparatus 100 may record a user response 120 as a function of the audio question 116. In this disclosure, a “user response” is a recording of the user responding to the question audio of the record generation question. In other words, user response 120 is an answer to the question asked by the processor in the audio question 116 that is recorded from the pre-determined set of resume questions 108. User response 120 may be recorded using an audio sensor, recorder, microphone or the like. User response 120 may be recorded through a user audio device communicatively connected to apparatus 100. User audio device and user interface device may be the same device. User response 120 may be rerecorded or edited by the user through the user audio device. User response 120 may comprise of a plurality of audio elements.

Still referring to FIG. 1, apparatus 100 may then generate a video record 124 as a function of the recorded user responses. In this disclosure, a “video record” is a visual representation of a user and their relevant information for use in digital contexts, such as for a job posting, dating website, social media, or the like. Video record 124 may include one or more audio fdes. Video record may or may not include images; video record may just be audio recordings of the question audios 116 and user response 120. Video record 124 may then be utilized by a user to resume 124 maybe any form of video or audio fdes that portray user input 104, audio question 116, and user response 120. Video record 124 may be a slideshow, video, or physical replication of the user as a function of the user. Video record 124 may be made using the plurality of audio elements from audio question 116 and user response 120. Once the last user response 120 is recorded in response to the last audio question 116, apparatus 100 may generate a video record 124 using video record training data comprising information from a plurality of pre-existing video records from a video record database 128. Generation of video record 124 may include generation thereof using a machine-learning training algorithm and video record training data, for instance by training a machine-learning model using video record training data and a machine-learning algorithm as described in further detail below. Machine learning algorithms may include unsupervised machine learning algorithms such as clustering models, k-means clustering, hierarchical clustering, anomaly detection, local outlier factor, neural networks and the like. Machine-learning may include supervised machine learning algorithms using video record training data. Machine-learning algorithm may train one or more neural networks such as convolutional and/or deep learning networks and are discussed more herein with reference to FIG. 4. To generate the video record, apparatus 100 extracts a plurality of audio elements from the audio question 116 and user response 120 and generates video record 124 using the plurality of audio elements.

Still referring to FIG. 1, apparatus 100 may generate a video record 124 by querying a video record database 128 using user-entered data and past video records 124. In an embodiment, video record database 128 may include any data structure for ordered storage and retrieval of data, which may be implemented as a hardware or software module. Video record database 128 may be implemented, without limitation, as a relational database, a key-value retrieval datastore such as a NOSQL database, or any other format or structure for use as a datastore that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Data entries in a video record database 128 may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in a relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a video record database 128 may reflect categories, cohorts, and/or populations of data consistently with this disclosure.

Alternatively or additionally, and still referring to FIG. 1, apparatus 100 may input user inputs to a video record classifier and receive an output classifying user inputs to one or more plurality of audio elements. Apparatus 100 may generate video record classifier using a classification algorithm, defined as a processes whereby an apparatus 100 derives, from training data, a model known as a “classifier” for sorting inputs into categories or bins of data. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher’s linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers.

Still referring to FIG. 1, generating a video record includes training, using the plurality of audio elements and video record training data, a machine-learning model. Training data, as used in this disclosure, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements. For instance, and without limitation, training data may include a plurality of data entries, each entry representing a set of data elements that were recorded, received, and/or generated together; data elements may be correlated by shared existence in a given data entry, by proximity in a given data entry, or the like. Multiple data entries in training data may evince one or more trends in correlations between categories of data elements; for instance, and without limitation, a higher value of a first data element belonging to a first category of data element may tend to correlate to a higher value of a second data element belonging to a second category of data element, indicating a possible proportional or other mathematical relationship linking values belonging to the two categories. Multiple categories of data elements may be related in training data according to various correlations; correlations may indicate causative and/or predictive links between categories of data elements, which may be modeled as relationships such as mathematical relationships by machine-learning processes as described in further detail below. Training data may be formatted and/or organized by categories of data elements, for instance by associating data elements with one or more descriptors corresponding to categories of data elements. As a non-limiting example, training data may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data may be linked to descriptors of categories by tags, tokens, or other data elements; for instance, and without limitation, training data may be provided in fixed-length formats, formats linking positions of data to categories such as comma-separated value (CSV) formats and/or self-describing formats such as extensible markup language (XML), enabling processes or devices to detect categories of data. Generating a video record includes training a machine-learning model using video record training data. “Video record training data” is data collected from a plurality of past, pre-generated video records that is currently used to help classify and distinguish audio elements received to generate the current video record.

Alternatively or additionally, and still referring to FIG. 1, training data may include one or more elements that are not categorized; that is, training data may not be formatted or contain descriptors for some elements of data. Machine-learning algorithms and/or other processes may sort training data according to one or more categorizations using, for instance, natural language processing algorithms, tokenization, detection of correlated values in raw data and the like; categories may be generated using correlation and/or other processing algorithms. As a non-limiting example, in a corpus of text, phrases making up a number “n” of compound words, such as nouns modified by other nouns, may be identified according to a statistically significant prevalence of n-grams containing such words in a particular order; such an n-gram may be categorized as an element of language such as a “word” to be tracked similarly to single words, generating a new category as a result of statistical analysis. Similarly, in a data entry including some textual data, a person’s name may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine-learning algorithms, and/or automated association of data in the data entry with descriptors or into a given format. The ability to categorize data entries automatedly may enable the same training data to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data used by computing device 104 may correlate any input data as described in this disclosure to any output data as described in this disclosure.

Still referring to FIG. 1, video record training data, used herein as training data used to generate video record classifier, may include, without limitation, a plurality of data entries, each data entry including one or more themes and/or objects and one or more video records represented thereby and/or associated therewith. Video record training data and/or elements thereof may be entered by users, for instance via graphical user interface forms; as a non-limiting example, each such form may present to a user a geometric form, word, image, audio file or the like, and a user may select a label of a audio element for each such geometric form, word, image, or the like from a list of labels provided to the user and/or may enter one or more words in a text entry element, which may be mapped to labels using language processing as described below.

Still referring to FIG. 1, computing device 104 may be configured to generate classifier using a Naive Bayes classification algorithm. Naive Bayes classification algorithm generates classifiers by assigning class labels to problem instances, represented as vectors of element values. Class labels are drawn from a finite set. Naive Bayes classification algorithm may include generating a family of algorithms that assume that the value of a particular element is independent of the value of any other element, given a class variable. Naive Bayes classification algorithm may be based on Bayes Theorem expressed as P(A/B)= P(B/A) P(A)-HP(B), where P(A/B) is the probability of hypothesis A given data B also known as posterior probability; P(B/A) is the probability of data B given that the hypothesis A was true; P(A) is the probability of hypothesis A being true regardless of data also known as prior probability of A; and P(B) is the probability of the data regardless of the hypothesis. A naive Bayes algorithm may be generated by first transforming training data into a frequency table. Computing device 104 may then calculate a likelihood table by calculating probabilities of different data entries and classification labels. Computing device 104 may utilize a naive Bayes equation to calculate a posterior probability for each class. A class containing the highest posterior probability is the outcome of prediction. Naive Bayes classification algorithm may include a gaussian model that follows a normal distribution. Naive Bayes classification algorithm may include a multinomial model that is used for discrete counts. Naive Bayes classification algorithm may include a Bernoulli model that may be utilized when vectors are binary.

With continued reference to FIG. 1, apparatus 100 may be configured to generate classifier using a K-nearest neighbors (KNN) algorithm. A “K-nearest neighbors algorithm” as used in this disclosure, includes a classification method that utilizes feature similarity to analyze how closely out-of-sample- features resemble training data to classify input data to one or more clusters and/or categories of features as represented in training data; this may be performed by representing both training data and input data in vector forms, and using one or more measures of vector similarity to identify classifications within training data, and to determine a classification of input data. K-nearest neighbors algorithm may include specifying a K-value, or a number directing the classifier to select the k most similar entries training data to a given sample, determining the most common classifier of the entries in the database, and classifying the known sample; this may be performed recursively and/or iteratively to generate a classifier that may be used to classify input data as further samples. For instance, an initial set of samples may be performed to cover an initial heuristic and/or “first guess” at an output and/or relationship, which may be seeded, without limitation, using expert input received according to any process as described herein. As a non-limiting example, an initial heuristic may include a ranking of associations between inputs and elements of training data. Heuristic may include selecting some number of highest-ranking associations and/or training data elements.

With continued reference to FIG. 1, generating k-nearest neighbors algorithm may generate a first vector output containing a data entry cluster, generating a second vector output containing an input data, and calculate the distance between the first vector output and the second vector output using any suitable norm such as cosine similarity, Euclidean distance measurement, or the like. Each vector output may be represented, without limitation, as an n- tuple of values, where n is at least two values. Each value of n-tuple of values may represent a measurement or other quantitative value associated with a given category of data, or attribute, examples of which are provided in further detail below; a vector may be represented, without limitation, in n-dimensional space using an axis per category of value represented in n-tuple of values, such that a vector has a geometric direction characterizing the relative quantities of attributes in the n-tuple as compared to each other. Two vectors may be considered equivalent where their directions, and/or the relative quantities of values within each vector as compared to each other, are the same; thus, as a non-limiting example, a vector represented as [5, 10, 15] may be treated as equivalent, for purposes of this disclosure, as a vector represented as [1, 2, 3], Vectors may be more similar where their directions are more similar, and more different where their directions are more divergent; however, vector similarity may alternatively or additionally be determined using averages of similarities between like attributes, or any other measure of similarity suitable for any n-tuple of values, or aggregation of numerical similarity measures for the purposes of loss functions as described in further detail below. Any vectors as described herein may be scaled, such that each vector represents each attribute along an equivalent scale of values. Each vector may be “normalized,” or divided by a “length” attribute, such as a length attribute I as derived using a Pythagorean norm: I = where ch is attribute number i of the vector. Scaling and/or normalization may function to make vector comparison independent of absolute quantities of attributes, while preserving any dependency on similarity of attributes; this may, for instance, be advantageous where cases represented in training data are represented by different quantities of samples, which may result in proportionally equivalent vectors with divergent values. As a non-limiting example, K-nearest neighbors algorithm may be configured to classify an input vector including a plurality of user-entered words and/or phrases, a plurality of attributes of a user data item, such as spoken or written text, objects depicted in images, metadata, or the like, to clusters representing themes.

Continuing to refer to FIG. 1, machine-learning algorithms may be implemented using techniques for development of linear regression models. Linear regression models may include ordinary least squares regression, which aims to minimize the square of the difference between predicted outcomes and actual outcomes according to an appropriate norm for measuring such a difference (e.g. a vector-space distance norm); coefficients of the resulting linear equation may be modified to improve minimization. Linear regression models may include ridge regression methods, where the function to be minimized includes the least-squares function plus term multiplying the square of each coefficient by a scalar amount to penalize large coefficients. Linear regression models may include least absolute shrinkage and selection operator (LASSO) models, in which ridge regression is combined with multiplying the leastsquares term by a factor of 1 divided by double the number of samples. Linear regression models may include a multi-task lasso model wherein the norm applied in the least-squares term of the lasso model is the Frobenius norm amounting to the square root of the sum of squares of all terms. Linear regression models may include the elastic net model, a multi-task elastic net model, a least angle regression model, a LARS lasso model, an orthogonal matching pursuit model, a Bayesian regression model, a logistic regression model, a stochastic gradient descent model, a perceptron model, a passive aggressive algorithm, a robustness regression model, a Huber regression model, or any other suitable model that may occur to persons skilled in the art upon reviewing the entirety of this disclosure. Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher-order equation) providing a best predicted output/actual output fit is sought; similar methods to those described above may be applied to minimize error functions, as will be apparent to persons skilled in the art upon reviewing the entirety of this disclosure.

Still referring to FIG. 1, machine-learning algorithms may include, without limitation, linear discriminant analysis. Machine-learning algorithm may include quadratic discriminate analysis. Machine-learning algorithms may include kernel ridge regression. Machine-learning algorithms may include support vector machines, including without limitation support vector classification-based regression processes. Machine-learning algorithms may include stochastic gradient descent algorithms, including classification and regression algorithms based on stochastic gradient descent. Machine-learning algorithms may include nearest neighbors algorithms. Machine-learning algorithms may include Gaussian processes such as Gaussian Process Regression. Machine-learning algorithms may include cross-decomposition algorithms, including partial least squares and/or canonical correlation analysis. Machinelearning algorithms may include naive Bayes methods. Machine-learning algorithms may include algorithms based on decision trees, such as decision tree classification or regression algorithms. Machine-learning algorithms may include ensemble methods such as bagging metaestimator, forest of randomized tress, AdaBoost, gradient tree boosting, and/or voting classifier methods. Machine-learning algorithms may include neural net algorithms, including convolutional neural net processes. With continued reference to FIG. 1 , models may be generated using alternative or additional artificial intelligence methods, including without limitation by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of "training" the network, in which elements from a training dataset are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning. This network may be trained using training data.

Still referring to FIG. 1, machine-learning algorithms may include supervised machine-learning algorithms. Supervised machine learning algorithms, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised machine-learning process may include a scoring function representing a desired form of relationship to be detected between inputs and outputs; scoring function may, for instance, seek to maximize the probability that a given input and/or combination of elements inputs is associated with a given output to minimize the probability that a given input is not associated with a given output. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various possible variations of supervised machine learning algorithms that may be used to determine relation between inputs and outputs. Machine learning model is further described herein with reference to FIG. 4.

With continued reference to FIG. 1, supervised machine-learning processes may include classification algorithms, defined as processes whereby an apparatus 100 derives, from training data, a model for sorting inputs into categories or bins of data. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers including without limitation k-nearest neighbors classifiers, support vector machines, decision trees, boosted trees, random forest classifiers, and/or neural network-based classifiers.

Still referring to FIG.l, machine learning processes may include unsupervised processes. An unsupervised machine-learning process, as used herein, is a process that derives inferences in datasets without regard to labels; as a result, an unsupervised machine-learning process may be free to discover any structure, relationship, and/or correlation provided in the data. Unsupervised processes may not require a response variable; unsupervised processes may be used to find interesting patterns and/or inferences between variables, to determine a degree of correlation between two or more variables, or the like. Unsupervised machine-learning algorithms may include, without limitation, clustering algorithms and/or cluster analysis processes, such as without limitation hierarchical clustering, centroid clustering, distribution clustering, clustering using density models, subspace models, group models, graph-based models, signed graph models, neural models, or the like. Unsupervised learning may be performed by neural networks and/or deep learning protocols as described above.

Continuing to refer to FIG. 1, machine-learning processes as described in this disclosure may be used to generate machine-learning models. A machine-learning model, as used herein, is a mathematical representation of a relationship between inputs and outputs, as generated using any machine-learning process including without limitation any process as described above and stored in memory; an input is submitted to a machine-learning model once created, which generates an output based on the relationship that was derived. For instance, and without limitation, a linear regression model, generated using a linear regression algorithm, may compute a linear combination of input data using coefficients derived during machine-learning processes to calculate an output datum. As a further non-limiting example, a machine-learning model may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of "training" the network, in which elements from a training dataset are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning.

Still referring to FIG. 1, apparatus 100 may be configured to identify inputs and correlated outputs for video record training set, which correlated inputs and outputs may be used to train video record, using a first feature learning algorithm that inputs video record training data. For instance, and without limitation, video clips, animations, sound files, or the like corresponding to particular elements of audio elements may be matched thereto using a feature learning algorithm, which matching may cause such clips to be added to video record based thereon. A “feature learning algorithm,” as used herein, is a machine-learning algorithm that identifies associations between elements of data in a training data set, where particular outputs and/or inputs are not specified. For instance, and without limitation, a feature learning algorithm may detect co-occurrences of sets of audio elements, as defined above, with each other and with video records. Apparatus 100 may perform a feature learning algorithm by dividing audio elements from a given user into various sub-combinations of such data to create audio sets as described above and evaluate which audio sets tend to co-occur with which other audio sets, and video records. In an embodiment, first feature learning algorithm may perform clustering of data; for instance, a number of clusters into which data from training data sets may be sorted using feature learning may be set as a number of video records and/or elements thereof such as video clips.

Continuing refer to FIG. 1, a feature learning and/or clustering algorithm may be implemented, as a non-limiting example, using a k-means clustering algorithm. A “k-means clustering algorithm” as used in this disclosure, includes cluster analysis that partitions n observations or unclassified cluster data entries into k clusters in which each observation or unclassified cluster data entry belongs to the cluster with the nearest mean, using, for instance video record training set as described above. “Cluster analysis” as used in this disclosure, includes grouping a set of observations or data entries in way that observations or data entries in the same group or cluster are more similar to each other than to those in other groups or clusters. Cluster analysis may be performed by various cluster models that include connectivity models such as hierarchical clustering, centroid models such as k-means, distribution models such as multivariate normal distribution, density models such as density -based spatial clustering of applications with nose (DBSCAN) and ordering points to identify the clustering structure (OPTICS), subspace models such as biclustering, group models, graph-based models such as a clique, signed graph models, neural models, and the like. Cluster analysis may include hard clustering whereby each observation or unclassified cluster data entry belongs to a cluster or not. Cluster analysis may include soft clustering or fuzzy clustering whereby each observation or unclassified cluster data entry belongs to each cluster to a certain degree such as for example a likelihood of belonging to a cluster; for instance, and without limitation, a fuzzy clustering algorithm may be used to identify clustering of gene combinations with multiple disease states, and multimedia versa. Cluster analysis may include strict partitioning clustering whereby each observation or unclassified cluster data entry belongs to exactly one cluster. Cluster analysis may include strict partitioning clustering with outliers whereby observations or unclassified cluster data entries may belong to no cluster and may be considered outliers. Cluster analysis may include overlapping clustering whereby observations or unclassified cluster data entries may belong to more than one cluster. Cluster analysis may include hierarchical clustering whereby observations or unclassified cluster data entries that belong to a child cluster also belong to a parent cluster.

With continued reference to FIG. 1, apparatus 100 may generate a k-means clustering algorithm receiving unclassified user data items and/or combinations thereof with video records as inputs and outputs a definite number of classified data entry cluster wherein the data entry clusters each contain cluster data entries. K-means algorithm may select a specific number of groups or clusters to output, identified by a variable “k” Generating a k-means clustering algorithm includes assigning inputs containing unclassified data to a “k-group ” or “k- cluster ” based on feature similarity. Centroids of k-groups or k-clusters may be utilized to generate classified data entry cluster. K-means clustering algorithm may select and/or be provided “A” variable by calculating k-means clustering algorithm for a range of k values and comparing results. K-means clustering algorithm may compare results across different values of k as the mean distance between cluster data entries and cluster centroid. K-means clustering algorithm may calculate mean distance to a centroid as a function of k value, and the location of where the rate of decrease starts to sharply shift, this may be utilized to select a k value. Centroids of k-groups or k-cluster include a collection of feature values which are utilized to classify data entry clusters containing cluster data entries. K-means clustering algorithm may act to classify a given video record to one or more user data sets, enabling apparatus 100 to identify user data sets correlated with video records.

With continued reference to FIG. 1, generating a k-means clustering algorithm may include generating initial estimates for k centroids which may be randomly generated or randomly selected from unclassified data input. K centroids may be utilized to define one or more clusters. K-means clustering algorithm may assign unclassified data to one or more k- centroids based on the squared Euclidean distance by first performing a data assigned step of unclassified data. K-means clustering algorithm may assign unclassified data to its nearest centroid based on the collection of centroids ciof centroids in set C. Unclassified data may be assigned to a cluster based on argmin _{ci 3 c} dist(ci, x) ² , where argmin includes argument of the minimum, ci includes a collection of centroids in a set C, and dist includes standard Euclidean distance. K-means clustering module may then recompute centroids by taking mean of all cluster data entries assigned to a centroid’s cluster. This may be calculated based on ci = l/| i | xi 5 Si ^Xl . K-means clustering algorithm may continue to repeat these calculations until a stopping criterion has been satisfied such as when cluster data entries do not change clusters, the sum of the distances have been minimized, and/or some maximum number of iterations has been reached.

Still referring to FIG. 1, k-means clustering algorithm may be configured to calculate a degree of similarity index value. A “degree of similarity index value” as used in this disclosure, includes a distance measurement indicating a measurement between each data entry cluster generated by k-means clustering algorithm and a selected user data sets and video records. Degree of similarity index value may indicate how close a particular combination of video records is to being classified by k-means algorithm to a particular cluster. K-means clustering algorithm may evaluate the distances of the combination of video records to the k-number of clusters output by k-means clustering algorithm. Short distances between a video record and a cluster may indicate a higher degree of similarity between video records and a particular cluster. Longer distances between video records and a cluster may indicate a lower degree of similarity between video records and a particular cluster. With continued reference to FIG. 1 , k-means clustering algorithm selects a classified data entry cluster as a function of the degree of similarity index value. In an embodiment, k- means clustering algorithm may select a classified data entry cluster with the smallest degree of similarity index value indicating a high degree of similarity between user data sets, video records and a particular data entry cluster. Alternatively or additionally k-means clustering algorithm may select a plurality of clusters having low degree of similarity index values to user data sets, video records, indicative of greater degrees of similarity. Degree of similarity index values may be compared to a threshold number indicating a minimal degree of relatedness suitable for inclusion of user data sets, video records in a cluster, where degree of similarity indices falling under the threshold number may be included as indicative of high degrees of relatedness. The above-described illustration of feature learning using k-means clustering is included for illustrative purposes only and should not be construed as limiting potential implementation of feature learning algorithms; persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various additional or alternative feature learning approaches that may be used consistently with this disclosure.

With continued reference to FIG. 1, video record training data may be stored in and/or retrieved from one or more databases; for instance, a video record training set may be stored in and/or retrieved from a video record training database. Video record training database may include any data structure suitable for use as video record database 128 as described above. Data entries in a video record training database may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in a relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a user database may reflect categories of data consistently with this disclosure. Video record training database may include one or more tables from which data records may be retrieved with linking data. One or more tables may include a question table and a video record table, which are further described below with reference to FIG. 2.

Alternatively or additionally, and still referring to FIG. 1, video record training set and/or data used therefor, including without limitation audio elements and/or data describing video records, may be stored in a video record training database, which may be implemented in any way suitable for implementation of a video record database 128 as described above. Referring now to FIG. 2, an exemplary embodiment 200 of video record training database 128 is illustrated. Video record training database 128 may be implemented as any data structure suitable for use as described above in reference to FIG. 1. Video record training database 128 may store one or more video records. One or more tables contained within video record training database 128 may include question table 204; question table 204 may store one or more record generation questions a questions studio 116 relating to the user. One or more tables contained within video record training database 128 may include video record table 208; video record table 208 may store one or more video records obtained from an application.

Referring again to FIG. 1, apparatus 100 may be configured to generate, using the machine learning model, a video record as a function of the video record training data. Video record training data comprises a plurality of audio elements that are classified with question audios 116 and user responses 120 to create the video record.

Still referring to FIG. 1, apparatus 100 may be configured to generate a video record classifier using video record training data. Video record classifier inputs user input 104 and outputs a plurality of audio elements associated to the user input. Video record training data may be populated by receiving a plurality of user inputs, for instance via graphical user interface forms; as a non-limiting example, each such form may present to a user at least a audio element and a user may select a label for each such audio element from a list of labels provided to the user and/or may enter one or more words in a text entry element, which may be mapped to labels using language processing as described below; label selected by user may correspond to a user- entered identification of a audio element of a question audio or user response. Video record classifier may input question audios and user responses and output audio elements.

Continuing to refer to FIG. I, apparatus 100 is configured to generate video record classifier using a classification algorithm, which may be implemented, without limitation, using any classification algorithm suitable for generating a multimedia classifier as described above. As a non-limiting example, video record classifier may use a K-nearest neighbors algorithm that may be configured to classify an input vector including a plurality of attributes of a audio element, such as spoken or written text, objects depicted in images, metadata, etc., to clusters representing themes. Video record classifier may alternatively or additionally be created using a naive-Bayes classification algorithm as described above. Video record classifier may enable apparatus 100 to identify a single theme represented by the best-matching cluster and/or some number of best-matching clusters, such as the K best matching clusters; in the latter case, matching a theme as described below may include matching any of the K best themes, or the most probable theme may be treated as the main theme and the remaining matching clusters may be treated as identifying themes of secondary importance.

Still referring to FIG. 1, generating a video record also may include using an object classifier. Apparatus 100 may modify video record training data, for instance to replace an audio element with plurality of audio element objects; plurality of audio element objects may be used as attributes of a vector associated with an audio element in video record training data, for instance for use in KNN or other classification algorithms as described above. Objects of plurality of audio elements objects may include, without limitation, objects depicted in images or frames of application, objects described in textual data extracted from audios, and/or converted from spoken words in application, or the like. In an embodiment, apparatus 100 may be configured to extract, from each question audio or user response, a plurality of audio elements, such as without limitation geometric forms extracted from images and/or video frames, words or phrases of textual data, or the like. Apparatus 100 may be configured to classify each audio element of the plurality of audio elements to an object of a plurality of audio element objects using an object classifier, where the object classifier may be generated using any classification algorithm as described above. Object classifier may classify words, phrases, and/or geometrical forms to clusters corresponding to labels of objects, enabling a vector representing presence or relative frequency of objects to be created, for instance by populating a vector index corresponding to each of a list of objects with a number indicating presence or absence of an object corresponding to an index and/or a number indicating a number of occurrences of an object corresponding to an index. In the latter case, as a non-limiting example, a higher number may indicate a greater prevalence of a given object in the audio element, which may, as a nonlimiting example, cause video record classifier to classify audio element to a theme consistent with a higher prevalence of a given object; prevalence and/or relative frequency of an object in audio element may also be used, as described below, to determine a degree to which the object is presented in the audio element for additional processing. Tn an embodiment, apparatus 100 may replace audio element with a plurality of objects as described above in video record training data; for instance, a separate instance of video record training data in which audio elements are replaced with plurality of objects may be generated, permitting use thereof in place of the original video record training data. Where object classifier is updated, for instance by adding to a list of objects corresponding to clusters and rerunning object classifier to classify to the updated list, audio elements stored in memory may be subjected to object classifier again to update each plurality of audio element objects; each of these actions, including without limitation rerunning object classifier to classify to the updated list and/or updating plurality of objects, may be performed by apparatus 100. Video record classifier may likewise be updated by rerunning classification algorithms on updated video element data.

Still referring to FIG. 1, object classifier and/or classifiers may be run against one or more sets of object training data, where object training data may include any form of object training data as described above. Object training data may include, without limitation, a plurality of data entries, each data entry including one or more content elements and one or more objects represented thereby. Object training data and/or elements thereof may be entered by users, for instance via graphical user interface forms; as a non-limiting example, each such form may present to a user a geometric form, word, image, or the like, and a user may select a label for each such geometric form, word, image, or the like from a list of labels provided to the user and/or may enter one or more words in a text entry element, which may be mapped to labels using language processing as described below.

Still referring to FIG. 1, generating a video record may include classifying the plurality of audio elements to a video record using a linguistic object classifier. Apparatus 100 may be configured to classify textual elements to objects using a linguistic object classifier; that is, object classifier may include a linguistic object classifier. Textual elements may include words or phrases, as described in further detail below, extracted from textual data such as documents or the like. Textual elements may include other forms of data converted into textual data, such as without limitation textual data converted from audio data using speech-to-text algorithms and/or protocols, textual data extracted from images using optical character recognition (OCR), or the like. Linguistic object classifier may include any classifier described above; linguistic object classifier may generate an output classifying an element of textual data to an object according to any classification algorithm as described above. In an embodiment, apparatus 100 may train linguistic object classifier using a linguistic classification training set, which may, as a non-limiting example, include elements of textual data and identifications of one or more objects associated therewith. Linguistic classification training set may, for instance, be populated by user entries of textual data along with corresponding user entries identifying and/labeling objects as described above. Apparatus 100 may be configured to extract the plurality of audio elements by extracting a plurality of textual elements from a verbal component of the question audio or user response and classify the plurality of textual elements using linguistic object classifier.

Generation of linguistic classification training set, mapping of user entries to object labels, and/or classification of textual objects to labels may alternatively or additionally be performed using a language processing algorithm. As used herein, language processing algorithm may operate to produce a language processing model. Language processing model may include a program automatically generated by language processing algorithm to produce associations between one or more words and/or phrases, including without limitation mathematical associations, between such words. Associations between language elements, where language elements include for purposes herein extracted words and/or object labels, without limitation, mathematical associations, including without limitation statistical correlations between any language element and any other language element and/or language elements. Statistical correlations and/or mathematical associations may include probabilistic formulas or relationships indicating, for instance, a likelihood that a given word and/or phrase indicates a given object label and/or a given additional word and/or phrase. As a further example, statistical correlations and/or mathematical associations may include probabilistic formulas or relationships indicating a positive and/or negative association between at least a word and/or phrase and an object label and/or an additional word.

Still referring to FIG. 1, language processing algorithm may generate the language processing model by any suitable method, including without limitation a natural language processing classification algorithm; language processing model may include a natural language process classification model that enumerates and/or derives statistical relationships between input term and output terms. Algorithm to generate language processing model may include a stochastic gradient descent algorithm, which may include a method that iteratively optimizes an objective function, such as an objective function representing a statistical estimation of relationships between terms, including relationships between input terms and output terms, in the form of a sum of relationships to be estimated. In an alternative or additional approach, sequential tokens may be modeled as chains, serving as the ob servations in a Hidden Markov Model (HMM). HMMs as used herein are statistical models with inference algorithms that that may be applied to the models. In such models, a hidden state to be estimated may include an association between at least a word and/or phrase and an object label and/or an additional word. There may be a finite number of labels, words and/or phrases, and/or relationships therebetween; an HMM inference algorithm, such as the forward-backward algorithm or the Viterbi algorithm, may be used to estimate the most likely discrete state given a word or sequence of words. Language processing algorithm may combine two or more approaches. For instance, and without limitation, machine-learning program may use a combination of Naive-Bayes, Stochastic Gradient Descent (SGD), and parameter grid-searching classification techniques; the result may include a classification algorithm that returns ranked associations.

Continuing to refer to FIG. 1, generating language processing model may include generating a vector space, which may be a collection of vectors, defined as a set of mathematical objects that can be added together under an operation of addition following properties of associativity, commutativity, existence of an identity element, and existence of an inverse element for each vector, and can be multiplied by scalar values under an operation of scalar multiplication compatible with field multiplication, and that has an identity element is distributive with respect to vector addition, and is distributive with respect to field addition. Each vector in an n-dimensional vector space may be represented by an n-tuple of numerical values. Each unique extracted word and/or language element as described above may be represented by a vector of the vector space. In an embodiment, each unique extracted and/or other language element may be represented by a dimension of vector space; as a non-limiting example, each element of a vector may include a number representing an enumeration of co-occurrences of the word and/or language element represented by the vector with another word and/or language element. Vectors may be normalized, scaled according to relative frequencies of appearance and/or file sizes. Tn an embodiment associating language elements to one another as described above may include computing a degree of vector similarity between a vector representing each language element and a vector representing another language element; vector similarity may be measured according to any norm for proximity and/or similarity of two vectors, including without limitation cosine similarity, which measures the similarity of two vectors by evaluating the cosine of the angle between the vectors, which can be computed using a dot product of the two vectors divided by the lengths of the two vectors. Degree of similarity may include any other geometric measure of distance between vectors.

Still referring to FIG. 1, language processing algorithm may use a corpus of documents to generate associations between language elements in a language processing algorithm, and apparatus 100 may then use such associations to analyze words extracted from one or more documents and determine that the one or more documents indicate a given relationship between at least a word and/or phrase and an object label and/or an additional word. In an embodiment, apparatus 100 may perform this analysis using a selected set of significant documents, such as documents identified by one or more users and/or expert users, and/or a generalized body of documents and/or co-occurrence data, which may be compiled by one or more third parties. Documents and/or co-occurrence data may be received by apparatus 100 by being uploaded by an expert or other persons using, without limitation, file transfer protocol (FTP) or other suitable methods for transmission and/or upload of documents; alternatively or additionally, where a document is identified by a citation, a uniform resource identifier (URI), uniform resource locator (URL) or other datum permitting unambiguous identification of the document, computing device 104 may automatically obtain the documents, co-occurrence data, or the like by downloading and/or navigating to one or more centralized and/or distributed collections thereof. Apparatus 100 may alternatively or additionally receive any language processing model from one or more remote devices or third-party devices and utilize such language processing model as described above.

Now referring to FIG. 3, an exemplary embodiment of an immutable sequential listing 300 is illustrated. Data elements may be listed in an immutable sequential listing 300; data elements may include any form of data, including textual data, image data, encrypted data, cryptographically hashed data, and the like. Data elements may include, without limitation, one or more at least a digitally signed assertions. Tn one embodiment, a digitally signed assertion 304 is a collection of textual data signed using a secure proof as described in further detail below; secure proof may include, without limitation, a digital signature as described above. Collection of textual data may contain any textual data, including without limitation American Standard Code for Information Interchange (ASCII), Unicode, or similar computer-encoded textual data, any alphanumeric data, punctuation, diacritical mark, or any character or other marking used in any writing system to convey information, in any form, including any plaintext or cyphertext data; in an embodiment, collection of textual data may be encrypted, or may be a hash of other data, such as a root or node of a Merkle tree or hash tree, or a hash of any other information desired to be recorded in some fashion using a digitally signed assertion 304. In an embodiment, collection of textual data states that the owner of a certain transferable item represented in a digitally signed assertion 304 register is transferring that item to the owner of an address. A digitally signed assertion 304 may be signed by a digital signature created using the private key associated with the owner's public key, as described above.

Still referring to FIG. 3, a digitally signed assertion 304 may describe a transfer of virtual currency, such as crypto-currency as described below. The virtual currency may be a digital currency. Item of value may be a transfer of trust, for instance represented by a statement vouching for the identity or trustworthiness of the first entity. Item of value may be an interest in a fungible negotiable financial instrument representing ownership in a public or private corporation, a creditor relationship with a governmental body or a corporation, rights to ownership represented by an option, derivative financial instrument, commodity, debt-backed security such as a bond or debenture or other security as described in further detail below. A resource may be a physical machine e.g. a ride share vehicle or any other asset. A digitally signed assertion 304 may describe the transfer of a physical good; for instance, a digitally signed assertion 304 may describe the sale of a product. In some embodiments, a transfer nominally of one item may be used to represent a transfer of another item; for instance, a transfer of virtual currency may be interpreted as representing a transfer of an access right; conversely, where the item nominally transferred is something other than virtual currency, the transfer itself may still be treated as a transfer of virtual currency, having value that depends on many potential factors including the value of the item nominally transferred and the monetary value attendant to having the output of the transfer moved into a particular user's control. The item of value may be associated with a digitally signed assertion 304 by means of an exterior protocol, such as the COLORED COINS created according to protocols developed by The Colored Coins Foundation, the MASTERCOIN protocol developed by the Mastercoin Foundation, or the ETHEREUM platform offered by the Stiftung Ethereum Foundation of Baar, Switzerland, the Thunder protocol developed by Thunder Consensus, or any other protocol.

Still referring to FIG. 3, in one embodiment, an address is a textual datum identifying the recipient of virtual currency or another item of value in a digitally signed assertion 304. In some embodiments, address is linked to a public key, the corresponding private key of which is owned by the recipient of a digitally signed assertion 304. For instance, address may be the public key. Address may be a representation, such as a hash, of the public key. Address may be linked to the public key in memory of a computing device, for instance via a "wallet shortener" protocol. Where address is linked to a public key, a transferee in a digitally signed assertion 304 may record a subsequent a digitally signed assertion 304 transferring some or all of the value transferred in the first a digitally signed assertion 304 to a new address in the same manner. A digitally signed assertion 304 may contain textual information that is not a transfer of some item of value in addition to, or as an alternative to, such a transfer. For instance, as described in further detail below, a digitally signed assertion 304 may indicate a confidence level associated with a distributed storage node as described in further detail below.

In an embodiment, and still referring to FIG. 3 immutable sequential listing 300 records a series of at least a posted content in a way that preserves the order in which the at least a posted content took place. Temporally sequential listing may be accessible at any of various security settings; for instance, and without limitation, temporally sequential listing may be readable and modifiable publicly, may be publicly readable but writable only by entities and/or devices having access privileges established by password protection, confidence level, or any device authentication procedure or facilities described herein, or may be readable and/or writable only by entities and/or devices having such access privileges. Access privileges may exist in more than one level, including, without limitation, a first access level or community of permitted entities and/or devices having ability to read, and a second access level or community of permitted entities and/or devices having ability to write; first and second community may be overlapping or non-overlapping. Tn an embodiment, posted content and/or immutable sequential listing 300 may be stored as one or more zero knowledge sets (ZKS), Private Information Retrieval (PIR) structure, or any other structure that allows checking of membership in a set by querying with specific properties. Such database may incorporate protective measures to ensure that malicious actors may not query the database repeatedly in an effort to narrow the members of a set to reveal uniquely identifying information of a given posted content.

Still referring to FIG. 3, immutable sequential listing 300 may preserve the order in which the at least a posted content took place by listing them in chronological order; alternatively or additionally, immutable sequential listing 300 may organize digitally signed assertions 304 into sub-listings 308 such as “blocks” in a blockchain, which may be themselves collected in a temporally sequential order; digitally signed assertions 304 within a sub-listing 308 may or may not be temporally sequential. The ledger may preserve the order in which at least a posted content took place by listing them in sub-listings 308 and placing the sub-listings 308 in chronological order. The immutable sequential listing 300 may be a distributed, consensus-based ledger, such as those operated according to the protocols promulgated by Ripple Labs, Inc., of San Francisco, Calif., or the Stellar Development Foundation, of San Francisco, Calif, or of Thunder Consensus. In some embodiments, the ledger is a secured ledger; in one embodiment, a secured ledger is a ledger having safeguards against alteration by unauthorized parties. The ledger may be maintained by a proprietor, such as a system administrator on a server, that controls access to the ledger; for instance, the user account controls may allow contributors to the ledger to add at least a posted content to the ledger, but may not allow any users to alter at least a posted content that have been added to the ledger. In some embodiments, ledger is cryptographically secured; in one embodiment, a ledger is cryptographically secured where each link in the chain contains encrypted or hashed information that makes it practically infeasible to alter the ledger without betraying that alteration has taken place, for instance by requiring that an administrator or other party sign new additions to the chain with a digital signature. Immutable sequential listing 300 may be incorporated in, stored in, or incorporate, any suitable data structure, including without limitation any database, datastore, file structure, distributed hash table, directed acyclic graph or the like. In some embodiments, the timestamp of an entry is cryptographically secured and validated via trusted time, either directly on the chain or indirectly by utilizing a separate chain. Tn one embodiment the validity of timestamp is provided using a time stamping authority as described in the RFC 3161 standard for trusted timestamps, or in the ANSI ASC x9.95 standard. In another embodiment, the trusted time ordering is provided by a group of entities collectively acting as the time stamping authority with a requirement that a threshold number of the group of authorities sign the timestamp.

In some embodiments, and with continued reference to FIG. 3, immutable sequential listing 300, once formed, may be inalterable by any party, no matter what access rights that party possesses. For instance, immutable sequential listing 300 may include a hash chain, in which data is added during a successive hashing process to ensure non-repudiation. Immutable sequential listing 300 may include a block chain. In one embodiment, a block chain is immutable sequential listing 300 that records one or more new at least a posted content in a data item known as a sub-listing 308 or “block.” An example of a block chain is the BITCOIN block chain used to record BITCOIN transactions and values. Sub-listings 308 may be created in a way that places the sub-listings 308 in chronological order and link each sub-listing 308 to a previous sub-listing 308 in the chronological order so that any computing device may traverse the sub-listings 308 in reverse chronological order to verify any at least a posted content listed in the block chain. Each new sub-listing 308 may be required to contain a cryptographic hash describing the previous sublisting 308. In some embodiments, the block chain contains a single first sub-listing 308 sometimes known as a "genesis block."

Still referring to FIG. 3, the creation of a new sub-listing 308 may be computationally expensive; for instance, the creation of a new sub-listing 308 may be designed by a “proof of work” protocol accepted by all participants in forming the immutable sequential listing 300 to take a powerful set of computing devices a certain period of time to produce. Where one sublisting 308 takes less time for a given set of computing devices to produce the sub-listing 308 protocol may adjust the algorithm to produce the next sub-listing 308 so that it will require more steps; where one sub-listing 308 takes more time for a given set of computing devices to produce the sub-listing 308 protocol may adjust the algorithm to produce the next sub-listing 308 so that it will require fewer steps. As an example, protocol may require a new sub-listing 308 to contain a cryptographic hash describing its contents; the cryptographic hash may be required to satisfy a mathematical condition, achieved by having the sub-listing 308 contain a number, called a nonce, whose value is determined after the fact by the discovery of the hash that satisfies the mathematical condition. Continuing the example, the protocol may be able to adjust the mathematical condition so that the discovery of the hash describing a sub-listing 308 and satisfying the mathematical condition requires more or less steps, depending on the outcome of the previous hashing attempt. Mathematical condition, as an example, might be that the hash contains a certain number of leading zeros and a hashing algorithm that requires more steps to find a hash containing a greater number of leading zeros, and fewer steps to find a hash containing a lesser number of leading zeros. In some embodiments, production of a new sublisting 308 according to the protocol is known as "mining." The creation of a new sub-listing 308 may be designed by a “proof of stake” protocol as will be apparent to those skilled in the art upon reviewing the entirety of this disclosure.

Continuing to refer to FIG. 3, in some embodiments, protocol also creates an incentive to mine new sub-listings 308. The incentive may be financial; for instance, successfully mining a new sub-listing 308 may result in the person or entity that mines the sub-listing 308 receiving a predetermined amount of currency. The currency may be fiat currency. Currency may be cryptocurrency as defined below. In other embodiments, incentive may be redeemed for particular products or services; the incentive may be a gift certificate with a particular business, for instance. In some embodiments, incentive is sufficiently attractive to cause participants to compete for the incentive by trying to race each other to the creation of sub-listings 308 Each sub-listing 308 created in immutable sequential listing 300 may contain a record or at least a posted content describing one or more addresses that receive an incentive, such as virtual currency, as the result of successfully mining the sub-listing 308.

With continued reference to FIG. 3, where two entities simultaneously create new sublistings 308, immutable sequential listing 300 may develop a fork; protocol may determine which of the two alternate branches in the fork is the valid new portion of the immutable sequential listing 300 by evaluating, after a certain amount of time has passed, which branch is longer. "Length" may be measured according to the number of sub-listings 308 in the branch. Length may be measured according to the total computational cost of producing the branch. Protocol may treat only at least a posted content contained the valid branch as valid at least a posted content. When a branch is found invalid according to this protocol, at least a posted content registered in that branch may be recreated in a new sub-listing 308 in the valid branch; the protocol may reject "double spending" at least a posted content that transfer the same virtual currency that another at least a posted content in the valid branch has already transferred. As a result, in some embodiments the creation of fraudulent at least a posted content requires the creation of a longer immutable sequential listing 300 branch by the entity attempting the fraudulent at least a posted content than the branch being produced by the rest of the participants; as long as the entity creating the fraudulent at least a posted content is likely the only one with the incentive to create the branch containing the fraudulent at least a posted content, the computational cost of the creation of that branch may be practically infeasible, guaranteeing the validity of all at least a posted content in the immutable sequential listing 300.

Still referring to FIG. 3, additional data linked to at least a posted content may be incorporated in sub-listings 308 in the immutable sequential listing 300; for instance, data may be incorporated in one or more fields recognized by block chain protocols that permit a person or computer forming a at least a posted content to insert additional data in the immutable sequential listing 300. In some embodiments, additional data is incorporated in an unspendable at least a posted content field. For instance, the data may be incorporated in an OP RETURN within the BITCOIN block chain. In other embodiments, additional data is incorporated in one signature of a multi-signature at least a posted content. In an embodiment, a multi-signature at least a posted content is at least a posted content to two or more addresses. In some embodiments, the two or more addresses are hashed together to form a single address, which is signed in the digital signature of the at least a posted content. In other embodiments, the two or more addresses are concatenated. In some embodiments, two or more addresses may be combined by a more complicated process, such as the creation of a Merkle tree or the like. In some embodiments, one or more addresses incorporated in the multi-signature at least a posted content are typical cryptocurrency addresses, such as addresses linked to public keys as described above, while one or more additional addresses in the multi-signature at least a posted content contain additional data related to the at least a posted content; for instance, the additional data may indicate the purpose of the at least a posted content, aside from an exchange of virtual currency, such as the item for which the virtual currency was exchanged. In some embodiments, additional information may include network statistics for a given node of network, such as a distributed storage node, e.g. the latencies to nearest neighbors in a network graph, the identities or identifying information of neighboring nodes in the network graph, the trust level and/or mechanisms of trust (e.g. certificates of physical encryption keys, certificates of software encryption keys, (in non-limiting example certificates of software encryption may indicate the firmware version, manufacturer, hardware version and the like), certificates from a trusted third party, certificates from a decentralized anonymous authentication procedure, and other information quantifying the trusted status of the distributed storage node) of neighboring nodes in the network graph, IP addresses, GPS coordinates, and other information informing location of the node and/or neighboring nodes, geographically and/or within the network graph. In some embodiments, additional information may include history and/or statistics of neighboring nodes with which the node has interacted. In some embodiments, this additional information may be encoded directly, via a hash, hash tree or other encoding.

With continued reference to FIG. 3, in some embodiments, virtual currency is traded as a crypto-currency. In one embodiment, a crypto-currency is a digital, currency such as Bitcoins, Peercoins, Namecoins, and Litecoins. Crypto-currency may be a clone of another cryptocurrency. The crypto-currency may be an "alt-coin." Crypto-currency may be decentralized, with no particular entity controlling it; the integrity of the crypto-currency may be maintained by adherence by its participants to established protocols for exchange and for production of new currency, which may be enforced by software implementing the crypto-currency. Cryptocurrency may be centralized, with its protocols enforced or hosted by a particular entity. For instance, crypto-currency may be maintained in a centralized ledger, as in the case of the XRP currency of Ripple Labs, Inc., of San Francisco, Calif. In lieu of a centrally controlling authority, such as a national bank, to manage currency values, the number of units of a particular cryptocurrency may be limited; the rate at which units of crypto-currency enter the market may be managed by a mutually agreed-upon process, such as creating new units of currency when mathematical puzzles are solved, the degree of difficulty of the puzzles being adjustable to control the rate at which new units enter the market. Mathematical puzzles may be the same as the algorithms used to make productions of sub-listings 308 in a block chain computationally challenging; the incentive for producing sub-listings 308 may include the grant of new crypto- currency to the miners. Quantities of crypto-currency may be exchanged using at least a posted content as described above.

Referring now to FIG. 4, an exemplary embodiment of a machine-learning module 400 that may perform one or more machine-learning processes as described in this disclosure is illustrated. Machine-learning module may perform determinations, classification, and/or analysis steps, methods, processes, or the like as described in this disclosure using machine learning processes. A “machine learning process,” as used in this disclosure, is a process that automatedly uses training data 404 to generate an algorithm that will be performed by a computing device/module to produce outputs 408 given data provided as inputs 412; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language.

Still referring to FIG. 4, “training data,” as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements. For instance, and without limitation, training data 404 may include a plurality of data entries, each entry representing a set of data elements that were recorded, received, and/or generated together; data elements may be correlated by shared existence in a given data entry, by proximity in a given data entry, or the like, as previously described in FIG. 1.

Alternatively or additionally, and continuing to refer to FIG. 4, training data 404 may include one or more elements that are not categorized; that is, training data 404 may not be formatted or contain descriptors for some elements of data. Machine-learning algorithms and/or other processes may sort training data 404 according to one or more categorizations using, for instance, natural language processing algorithms, tokenization, detection of correlated values in raw data and the like; categories may be generated using correlation and/or other processing algorithms. As a non-limiting example, in a corpus of text, phrases making up a number “n” of compound words, such as nouns modified by other nouns, may be identified according to a statistically significant prevalence of n-grams containing such words in a particular order; such an n-gram may be categorized as an element of language such as a “word” to be tracked similarly to single words, generating a new category as a result of statistical analysis. Similarly, in a data entry including some textual data, a person’s name may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine- learning algorithms, and/or automated association of data in the data entry with descriptors or into a given format. The ability to categorize data entries automatedly may enable the same training data 404 to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data 404 used by machine-learning module 400 may correlate any input data as described in this disclosure to any output data as described in this disclosure. As a non-limiting illustrative example, inputs of the current disclosure include user data items, while outputs include video avatars.

Further referring to FIG. 4, training data may be filtered, sorted, and/or selected using one or more supervised and/or unsupervised machine-learning processes and/or models as described in further detail below; such models may include without limitation a training data classifier 416. Training data classifier 416 may include a “classifier,” which as used in this disclosure is a machine-learning model as defined below, such as a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. Machine-learning module 400 may generate a classifier using a classification algorithm, defined as a processes whereby a computing device and/or any module and/or component operating thereon derives a classifier from training data 404. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher’s linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers. As a nonlimiting example, training data classifier 416 may classify elements of training data to be associated with categories of video avatars.

Still referring to FIG. 4, machine-learning module 400 may be configured to perform a lazy- learning process 420 and/or protocol, which may alternatively be referred to as a “lazy loading” or “call-when-needed” process and/or protocol, may be a process whereby machine learning is conducted upon receipt of an input to be converted to an output, by combining the input and training set to derive the algorithm to be used to produce the output on demand. For instance, an initial set of simulations may be performed to cover an initial heuristic and/or “first guess” at an output and/or relationship. As a non-limiting example, an initial heuristic may include a ranking of associations between inputs and elements of training data 404. Heuristic may include selecting some number of highest-ranking associations and/or training data 404 elements. Lazy learning may implement any suitable lazy learning algorithm, including without limitation a K-nearest neighbors algorithm, a lazy naive Bayes algorithm, or the like; persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various lazy-leaming algorithms that may be applied to generate outputs as described in this disclosure, including without limitation lazy learning applications of machine-learning algorithms as described in further detail below.

Alternatively or additionally, and with continued reference to FIG. 4, machine-learning processes as described in this disclosure may be used to generate machine-learning models 424. A “machine-learning model,” as used in this disclosure, is a mathematical and/or algorithmic representation of a relationship between inputs and outputs, as generated using any machinelearning process including without limitation any process as described above and stored in memory; an input is submitted to a machine-learning model 424 once created, which generates an output based on the relationship that was derived. For instance, and without limitation, a linear regression model, generated using a linear regression algorithm, may compute a linear combination of input data using coefficients derived during machine-learning processes to calculate an output datum. As a further non-limiting example, a machine-learning model 424 may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of "training" the network, in which elements from a training data 404 set are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning.

Still referring to FIG. 4, machine-learning algorithms may include at least a supervised machine-learning process 428. At least a supervised machine-learning process 428, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include user data items as described above as inputs, video avatar outputs, and a scoring function representing a desired form of relationship to be detected between inputs and outputs; scoring function may, for instance, seek to maximize the probability that a given input and/or combination of elements inputs is associated with a given output to minimize the probability that a given input is not associated with a given output. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data 404. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various possible variations of at least a supervised machine-learning process 428 that may be used to determine relation between inputs and outputs. Supervised machine-learning processes may include classification algorithms as defined above.

Further referring to FIG. 4, machine learning processes may include at least an unsupervised machine-learning processes 432. An unsupervised machine-learning process, as used herein, is a process that derives inferences in datasets without regard to labels; as a result, an unsupervised machine-learning process may be free to discover any structure, relationship, and/or correlation provided in the data. Unsupervised processes may not require a response variable; unsupervised processes may be used to find interesting patterns and/or inferences between variables, to determine a degree of correlation between two or more variables, or the like. Still referring to FIG. 4, machine-learning module 400 may be designed and configured to create a machine-learning model 424 using techniques for development of linear regression models. Linear regression models may include ordinary least squares regression, which aims to minimize the square of the difference between predicted outcomes and actual outcomes according to an appropriate norm for measuring such a difference (e.g. a vector-space distance norm); coefficients of the resulting linear equation may be modified to improve minimization. Linear regression models may include ridge regression methods, where the function to be minimized includes the least-squares function plus term multiplying the square of each coefficient by a scalar amount to penalize large coefficients. Linear regression models may include least absolute shrinkage and selection operator (LASSO) models, in which ridge regression is combined with multiplying the least-squares term by a factor of 1 divided by double the number of samples. Linear regression models may include a multi-task lasso model wherein the norm applied in the least-squares term of the lasso model is the Frobenius norm amounting to the square root of the sum of squares of all terms. Linear regression models may include the elastic net model, a multitask elastic net model, a least angle regression model, a LARS lasso model, an orthogonal matching pursuit model, a Bayesian regression model, a logistic regression model, a stochastic gradient descent model, a perceptron model, a passive aggressive algorithm, a robustness regression model, a Huber regression model, or any other suitable model that may occur to persons skilled in the art upon reviewing the entirety of this disclosure. Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher-order equation) providing a best predicted output/actual output fit is sought; similar methods to those described above may be applied to minimize error functions, as will be apparent to persons skilled in the art upon reviewing the entirety of this disclosure.

Continuing to refer to FIG. 4, machine-learning algorithms may include, without limitation, linear discriminant analysis. Machine-learning algorithm may include quadratic discriminate analysis. Machine-learning algorithms may include kernel ridge regression. Machine-learning algorithms may include support vector machines, including without limitation support vector classification-based regression processes. Machine-learning algorithms may include stochastic gradient descent algorithms, including classification and regression algorithms based on stochastic gradient descent. Machine-learning algorithms may include nearest neighbors algorithms. Machine-learning algorithms may include various forms of latent space regularization such as variational regularization. Machine-learning algorithms may include various forms of latent space regularization such as variational regularization. Machine-learning algorithms may include Gaussian processes such as Gaussian Process Regression. Machinelearning algorithms may include cross-decomposition algorithms, including partial least squares and/or canonical correlation analysis. Machine-learning algorithms may include naive Bayes methods. Machine-learning algorithms may include algorithms based on decision trees, such as decision tree classification or regression algorithms. Machine-learning algorithms may include ensemble methods such as bagging meta-estimator, forest of randomized tress, AdaBoost, gradient tree boosting, and/or voting classifier methods. Machine-learning algorithms may include neural net algorithms, including convolutional neural net processes.

Referring now to FIG. 5, a flow diagram illustrating an exemplary embodiment of a method for generating a video record using audio. Video record may be any of the resumes describe herein with reference to FIGS. 1 and 2.

Still referring to FIG. 5, at step 505, method 500 includes receiving a user input 104 from a user. Identity of the user is validated on an immutable sequential listing. User input 104 is received from a user interface device.

Still referring to FIG. 5, at step 510, method 500 includes selecting a set of record generation questions 108 for the user as a function of the user input 104. Set of record generation questions 108 are selected based on job openings that the user currently works in or is interested in working in. Selecting a set of record generation questions 108 involves the use of a machinelearning model. Set of record generation questions may be any of the questions describe herein with reference to FIG. 1. User input may be any of the inputs describe herein with reference to FIG. 1.

Still referring to FIG. 5, at step 515, method 500 includes playing a audio question 116 as a function of the selected set of record generation questions 108. Audio question 116 is configured to play through the user interface device. Question audio may be any of the question audios describe herein with reference to FIG. 1. Set of record generation questions may be any of the questions describe herein with reference to FIG. 1.

Still referring to FIG. 1, at step 520, method 500 includes recording a user response 120 as a function of the audio question 116. User response 120 is recorded using an audio sensor on the user interface device. User response 120 has a set time limit for the user to respond in. User response may be any of the user responses describe herein with reference to FIG. 1. Question audio may be any of the question audios describe herein with reference to FIG. 1.

Still referring to FIG. 5, at step 525, method 500 includes generating a video record 124 as a function of the recorded user responses 120. Generating a video record 124 further involves the use of an object classifier. Video record 124 comprises a plurality of audio elements extracted from the user response 120. Video record may be any of the resumes describe herein with reference to FIGS. 1 and 2. User response may be any of the user responses describe herein with reference to FIG. 1.

Referring now to FIG. 6, an exemplary embodiment of a system 600 for parsing and correlating solicitation video content is illustrated. System includes a computing device 604; computing device 604 may implemented in any manner described in this disclosure.

With continued reference to FIG. 6, computing device 604 may be designed and/or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, computing device 604 may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Computing device 604 may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and/or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and/or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and/or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and/or parallel processing.

With continued reference to FIG. 6, computing device 604 may be configured to receive a solicitation video 608. As used in this disclosure, a “solicitation video” is an item of media, including both an image component 612 and an audible component 616, related to a subject 620. In some cases, solicitation video 608 may include a digital video, which may be communicated by way of digital signals, for example between computing devices which are communicatively connected with at least a network. Digital video may be compressed in order to optimize speed and/or cost of transmission of video 608. Videos may be compressed according to a video compression coding format (i.e., codec). Exemplary video compression codecs include without limitation H.26x codecs, MPEG formats, VVC, SVT-AV1, and the like. In some cases, compression of a digital video 608 may be lossy, in which some information may be lost during compression. Alternatively or additionally, in some cases, compression of a digital video 608 may be substantially lossless, where substantially no information is lost during compression.

In some cases, a solicitation video 608 may include content that is representative or communicative of at least an attribute 624a-n of a subject 620. As used in this disclosure, a “subject” is a person, for example an ostensibly competent person or jobseeker. Subject 620 may be represented directly, i.e., in person, by solicitation video 608. For example, in some cases, an image component 612 may include an image of subject 620 and/or an audible component 616 may include audio of subject 620, for instance the subject 620 speaking. Attributes 624a-n may include subjects skills, competencies, credentials, talents, and the like. In some cases, attributes 624a-n may be explicitly conveyed within solicitation video 608. Alternatively or additionally, in some cases, attributes 624a-n may be conveyed implicitly with solicitation video 608.

With continued reference to FIG. 6, solicitation video 608 includes at least an image component 612. As used in this disclosure, an “image component” is a visual representation. Image component 612 may include animations, still imagery, recorded video, and the like. Image component may include visual information of solicitation video 608. In some cases, image component 612 may include a plurality of temporally sequential frames. In some cases, each frame may be encoded (e.g., bitmap or vector-based encoding). Each frame may be configured to be displayed by way of a display. Exemplary displays include without limitation light emitting diode (LED) displays, cathode ray tube (CRT) displays, liquid crystal displays (LCDs), organic LEDs (OLDs), quantum dot displays, projectors (e.g., scanned light projectors), and the like. In some cases, image component 612 may digitally represent (i.e., encode) visual information, for instance a pixel (luma and/or chroma) mapping.

With continued reference to FIG. 6, solicitation video 608 includes at least an audio component 616. As used in this disclosure, an “audio component” is a representation of audio, for example a sound, a speech, and the like. In some cases, audio component 616 may be represented digitally. In some cases, audio component 616 may be temporally associated with image component 612 video. For example, in some cases, audio component 616 may be synchronized with image component 612. Audio component 616 may be compressed and/or encoded, for example by way of a digital audio coding format (i.e., codec). Exemplary audio codecs include without limitation free lossless audio codec (FLAC), MPEG-4 audio lossless coding, Dolby digital, MP3, Vorbis, and the like. In some cases, an audio codec may be lossless. Alternatively or additionally, in some cases, an audio codec may be lossy.

With continued reference to FIG. 6, solicitation video may include, for example by way of representation with one or both of image component 612 and audio component 616, verbal content 628. As used in this disclosure, “verbal content” is comprehensible language-based communication. For example, in some cases, verbal content 628 may include visual verbal content. As used in this disclosure, “visual verbal content” is literal (e.g., written) verbal content. In some cases, visual verbal content may be included within solicitation video 608 substantially by way of image component 612. Alternatively or additionally, in some cases, verbal content 628 may include audible verbal content. As used in this disclosure, “audible verbal content” is oral (e.g., spoken) verbal content. In some cases, audible verbal content may be included within solicitation video 608 substantially by way of audio component 616. In some cases, verbal content 628 may be related to at least an attribute of subject. For example, verbal content 628 may explicitly communicative at least an attribute 624a-n of subject 620.

Still referring to FIG. 6, in some embodiments, solicitation video 608 may include, for example by way of representation with one or both of image component 612 and audio component 616, non-verbal content 632. As used in this disclosure, “non-verbal content” is all communication that is not characterized as verbal content. As such, non-verbal content includes all subtle (and non-subtle) forms of communication which are not conveyed with use of language. Exemplary non-verbal content may include change in intonation and/or stress in a speakers voice, expression of emotion, interjection, and the like. For example, in some cases, non-verbal content 632 may include visual non-verbal content. As used in this disclosure, “visual non-verbal content” is non-verbal content that is visually represented. In some cases, visual nonverbal content 632 may be included within solicitation video 608 substantially by way of image component 612. Alternatively or additionally, in some cases, non-verbal content 632 may include audible non-verbal content. As used in this disclosure, “visual non-verbal content” is non-verbal content that is audibly represented Tn some cases, audible non-verbal content 632 may be included in solicitation video 608 substantially by way of audio component 616. Solicitation video 608 is representative of a form of a subject-specific data. As used in this disclosure, “subject-specific data” is any element of information that is associated with a specific subject 620. Exemplary forms of subject-specific data include image component 612, audio component 616, verbal content 628, non-verbal content 632, solicitation video 608, as well as any information derived directly or indirectly from solicitation video or any other subjectspecific data. In some cases, subject-specific may represent attributes 624a-n associated with a specific subject 620. In some cases, a non-verbal classifier 644 may classify non-verbal content present in one or more of audio component 616 and image component 612 to one or more of a job description 652a-n, a feature 648, and a keyword 636. Non-verbal classifier 644 may include a number of classifiers, for example each being tasked with classifying a particular attribute 624a-n or form of non-verbal content 632. For example, in some cases, non-verbal classifier 644 may classify a solicitation video 608 and related subject 620 as associated with a feature 648 or keyword 636 representative of ‘personable.’ Non-verbal classifier 644 may include a specialized audible non-verbal classifier to classify audible non-verbal content 632 within audio component 616 as sounding ‘personable’ that is, for example, as having appropriate levels of intonation, timber, melody, stress, and the like. Likewise, non-verbal classifier 644 may include another specialized visual non-verbal classifier to classify visual non-verbal content 632 as appearing ‘personable’ that is, for example, as having appropriate posture, facial expressions, manner of dress, and the like. In some cases, classifier 644 may include or a constituent part of tree structure, for making associations based upon solicitation video 608.

With continued reference to FIG. 6, computing device 604 may transcribe at least a keyword 636. Computing device 604 may transcribe at least a keyword as a function of one or more of image component 612 and audio component 616. Computing device 604 may transcribe at least a keyword as a function of verbal content 628. As used in this disclosure, a “keyword” is any meaningful word or syntax. In some cases, computing device 604 may transcribe much or even substantially all verbal content 628 from solicitation video 608. In some cases, computing device 604 may transcribe audible verbal content 628, for example by way of speech to text or speech recognition technologies. Exemplary automatic speech recognition technologies include, without limitation, dynamic time warping (DTW)-based speech recognition, end-to-end automatic speech recognition, hidden Markov models, neural networks, including deep feedforward and recurrent neural networks, and the like. Generally, automatic speech recognition may include any machine-learning process 640 described in this disclosure.

Still referring to FIG. 6, in some embodiments, automatic speech recognition may require training (i.e., enrollment). In some cases, training an automatic speech recognition model may require an individual speaker to read text or isolated vocabulary. In some cases, a solicitation video 608 may include an audio component 616 having an audible verbal content 628, the contents of which are known a priori by computing device 604. Computing device 604 may then train an automatic speech recognition model according to training data which includes audible verbal content 628 correlated to known content. In this way, computing device 604 may analyze a person's specific voice and train an automatic speech recognition model to the person's speech, resulting in increased accuracy. Alternatively or additionally, in some cases, computing device 604 may include an automatic speech recognition model that is speaker-independent. As used in this disclosure, a “speaker independent” automatic speech recognition process does not require training for each individual speaker. Conversely, as used in this disclosure, automatic speech recognition processes that employ individual speaker specific training are “speaker dependent.”

Still referring to FIG. 6, in some embodiments, an automatic speech recognition process may perform voice recognition or speaker identification. As used in this disclosure, “voice recognition” refers to identifying a speaker, from audio content 616, rather than what the speaker is saying. In some cases, computing device 604 may first recognize a speaker of verbal audio content 628 and then automatically recognize speech of the speaker, for example by way of a speaker dependent automatic speech recognition model or process. In some embodiments, an automatic speech recognition process can be used to authenticate or verify an identity of a speaker. In some cases, a speaker may or may not include subject 620. For example, subject 620 may speak within solicitation video 608, but others may speak as well.

Still referring to FIG. 6, in some embodiments, an automatic speech recognition process may include one or all of acoustic modeling, language modeling, and statistically-based speech recognition algorithms. In some cases, an automatic speech recognition process may employ hidden Markov models (HMMs). As discussed in greater detail below, language modeling such as that employed in natural language processing applications like document classification or statistical machine translation, may also be employed by an automatic speech recognition process.

Still referring to FIG. 6, an exemplary algorithm employed in automatic speech recognition may include or even be based upon hidden Markov models. Hidden Markov models (HMMs) may include statistical models that output a sequence of symbols or quantities. HMMs can be used in speech recognition because a speech signal can be viewed as a piecewise stationary signal or a short-time stationary signal. For example, over a short time scale (e.g., 60 milliseconds), speech can be approximated as a stationary process. Speech (i.e., audible verbal content) can be understood as a Markov model for many stochastic purposes.

Still referring to FIG. 6, in some embodiments HMMs can be trained automatically and may be relatively simple and computationally feasible to use. In an exemplary automatic speech recognition process, a hidden Markov model may output a sequence of n-dimensional real- valued vectors (with n being a small integer, such as 60), at a rate of about one vector every 60 milliseconds. Vectors may consist of cepstral coefficients. A cepstral coefficient requires using a spectral domain. Cepstral coefficients may be obtained by taking a Fourier transform of a short time window of speech yielding a spectrum, decorrelating the spectrum using a cosine transform, and taking first (i.e., most significant) coefficients. In some cases, an HMM may have in each state a statistical distribution that is a mixture of diagonal covariance Gaussians, yielding a likelihood for each observed vector. In some cases, each word, or phoneme, may have a different output distribution; an HMM for a sequence of words or phonemes may be made by concatenating an HMMs for separate words and phonemes.

Still referring to FIG. 6, in some embodiments, an automatic speech recognition process may use various combinations of a number of techniques in order to improve results. In some cases, a large-vocabulary automatic speech recognition process may include context dependency for phonemes. For example, in some cases, phonemes with different left and right context may have different realizations as HMM states. In some cases, an automatic speech recognition process may use cepstral normalization to normalize for different speakers and recording conditions. In some cases, an automatic speech recognition process may use vocal tract length normalization (VTLN) for male-female normalization and maximum likelihood linear regression (MLLR) for more general speaker adaptation. In some cases, an automatic speech recognition process may determine so-called delta and delta-delta coefficients to capture speech dynamics and might use heteroscedastic linear discriminant analysis (HLDA). In some cases, an automatic speech recognition process may use splicing and a linear discriminate analysis (LDA)-based projection, which may include heteroscedastic linear discriminant analysis or a global semi-tied covariance transform (also known as maximum likelihood linear transform [MLLT]). In some cases, an automatic speech recognition process may use discriminative training techniques, which may dispense with a purely statistical approach to HMM parameter estimation and instead optimize some classification-related measure of training data; examples may include maximum mutual information (MMI), minimum classification error (MCE), and minimum phone error (MPE).

Still referring to FIG. 6, in some embodiments, an automatic speech recognition process may be said to decode speech (i.e., audible verbal content). Decoding of speech may occur when an automatic speech recognition system is presented with a new utterance and must compute a most likely sentence. In some cases, speech decoding may include a Viterbi algorithm. A Viterbi algorithm may include a dynamic programming algorithm for obtaining a maximum a posteriori probability estimate of a most likely sequence of hidden states (i.e., Viterbi path) that results in a sequence of observed events. Viterbi algorithms may be employed in context of Markov information sources and hidden Markov models. A Viterbi algorithm may be used to find a best path, for example using a dynamically created combination hidden Markov model, having both acoustic and language model information, using a statically created combination hidden Markov model (e.g., finite state transducer [FST] approach).

Still referring to FIG. 6, in some embodiments, speech (i.e., audible verbal content) decoding may include considering a set of good candidates and not only a best candidate, when presented with a new utterance. In some cases, a better scoring function (i.e., re-scoring) may be used to rate each of a set of good candidates, allowing selection of a best candidate according to this refined score. In some cases, a set of candidates can be kept either as a list (i.e., N-best list approach) or as a subset of models (i.e., a lattice). In some cases, re-scoring may be performed by optimizing Bayes risk (or an approximation thereof). In some cases, re-scoring may include optimizing for sentence (including keywords) that minimizes an expectancy of a given loss function with regards to all possible transcriptions. For example, re-scoring may allow selection of a sentence that minimizes an average distance to other possible sentences weighted by their estimated probability. In some cases, an employed loss function may include Levenshtein distance, although different distance calculations may be performed, for instance for specific tasks. In some cases, a set of candidates may be pruned to maintain tractability.

Still referring to FIG. 6, in some embodiments, an automatic speech recognition process may employ dynamic time warping (DTW)-based approaches. Dynamic time warping may include algorithms for measuring similarity between two sequences, which may vary in time or speed. For instance, similarities in walking patterns would be detected, even if in one video the person was walking slowly and if in another he or she were walking more quickly, or even if there were accelerations and deceleration during the course of one observation. DTW has been applied to video, audio, and graphics - indeed, any data that can be turned into a linear representation can be analyzed with DTW. In some cases, DTW may be used by an automatic speech recognition process to cope with different speaking (i.e., audible verbal content) speeds. In some cases, DTW may allow computing device 604 to find an optimal match between two given sequences (e.g., time series) with certain restrictions. That is, in some cases, sequences can be "warped" non-linearly to match each other. In some cases, a DTW-based sequence alignment method may be used in context of hidden Markov models.

Still referring to FIG. 6, in some embodiments, an automatic speech recognition process may include a neural network. Neural network may include any neural network, for example those disclosed with reference to FIGS. 10 - 7. In some cases, neural networks may be used for automatic speech recognition, including phoneme classification, phoneme classification through multi-objective evolutionary algorithms, isolated word recognition, audiovisual speech recognition, audiovisual speaker recognition and speaker adaptation. In some cases, neural networks employed in automatic speech recognition may make fewer explicit assumptions about feature statistical properties than HMMs and therefore may have several qualities making them attractive recognition models for speech recognition. When used to estimate the probabilities of a speech feature segment, neural networks may allow discriminative training in a natural and efficient manner. In some cases, neural networks may be used to effectively classify audible verbal content over short-time interval, for instance such as individual phonemes and isolated words. In some embodiments, a neural network may be employed by automatic speech recognition processes for pre-processing, feature transformation and/or dimensionality reduction, for example prior to HMM-based recognition. In some embodiments, long short-term memory (LSTM) and related recurrent neural networks (RNNs) and Time Delay Neural Networks (TDNN's) may be used for automatic speech recognition, for example over longer time intervals for continuous speech recognition.

With continued reference to FIG. 6, computing device 604 may recognize verbal content not only from speech (i.e., audible verbal content). For example, in some cases, audible verbal content 628 recognition may be aided in analysis of image component 612. For instance, in some cases, computing device 604 may image content 612 to aid in recognition of audible verbal content 628 as a viewing a speaker (e.g., lips) as they speak aids in comprehension of his or her speech. In some cases computing device 604 may include audiovisual speech recognition processes.

Still referring to FIG. 6, in some embodiments, audio visual speech recognition (AVSR) may include techniques employing image processing capabilities in lip reading to aid speech recognition processes. In some cases, AVSR may be used to decode (i.e., recognize) indeterministic phonemes or help in forming a preponderance among probabilistic candidates. In some cases, AVSR may include an audio-based automatic speech recognition process and an image-based automatic speech recognition process. AVSR may combine results from both processes with feature fusion. Audio-based speech recognition process may analysis audio according to any method described herein, for instance using a Mel-frequency cepstrum coefficients (MFCCs) and/or log-Mel spectrogram derived from raw audio samples. Image-based speech recognition may perform feature recognition to yield an image vector. In some cases, feature recognition may include any feature recognition process described in this disclosure, for example a variant of a convolutional neural network. In some cases, AVSR employs both an audio datum and an image datum to recognize verbal content 628. For instance, audio vector and image vector may each be concatenated and used to predict speech made by a subject 620, who is ‘on camera.’ With continued reference to FIG. 6, in some embodiments, image component 612 may include or otherwise represent verbal content 628. For instance, written or visual verbal content 628 may be included within image component 612. In some cases, computing device 604 may be configured to recognize at least a keyword 636 as a function of visual verbal content 628. In some cases, recognizing at least a keyword 636 may include optical character recognition.

Still refereeing to FIG. 6, in some embodiments, optical character recognition or optical character reader (OCR) includes automatic conversion of images of written (e.g., typed, handwritten or printed text) into machine-encoded text. In some cases, recognition of at least a keyword from an image component may include one or more processes, including without limitation optical character recognition (OCR), optical word recognition, intelligent character recognition, intelligent word recognition, and the like. In some cases, OCR may recognize written text, one glyph or character at a time. In some cases, optical word recognition may recognize written text, one word at a time, for example, for languages that use a space as a word divider. In some cases, intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine-learning processes 640. In some cases, intelligent word recognition (IWR) may recognize written text, one word at a time, for instance by employing machine-learning processes 640.

Still referring to FIG. 6, in some cases OCR may be an "offline" process, which analyses a static document or image frame. In some cases, handwriting movement analysis can be used as input to handwriting recognition. For example, instead of merely using shapes of glyphs and words, this technique may capture motions, such as the order in which segments are drawn, the direction, and the pattern of putting the pen down and lifting it. This additional information can make handwriting recognition more accurate. In some cases, this technology may be referred to as “online” character recognition, dynamic character recognition, real-time character recognition, and intelligent character recognition.

Still referring to FIG. 6, in some cases, OCR processes may employ preprocessing of image component 612. Pre-processing process may include without limitation deskew, de-speckle, binarization, line removal, layout analysis or “zoning,” line and word detection, script recognition, character isolation or “segmentation,” and normalization. In some cases, a de-skew process may include applying a transform (e.g., homography or affine transform) to image component 612 to align text. Tn some cases, a de-speckle process may include removing positive and negative spots and/or smoothing edges. In some cases, a binarization process may include converting an image from color or greyscale to black-and-white (i.e., a binary image). Binarization may be performed as a simple way of separating text (or any other desired image component) from a background of image component. In some cases, binarization may be required for example if an employed OCR algorithm only works on binary images. In some cases, a line removal process may include removal of non-glyph or noncharacter imagery (e.g., boxes and lines). In some cases, a layout analysis or “zoning” process may identify columns, paragraphs, captions, and the like as distinct blocks. In some cases, a line and word detection process may establish a baseline for word and character shapes and separate words, if necessary. In some cases, a script recognition process may, for example in multilingual documents, identify script allowing an appropriate OCR algorithm to be selected. In some cases, a character isolation or “segmentation” process may separate signal characters, for example character-based OCR algorithms. In some cases, a normalization process may normalize aspect ratio and/or scale of image component 612.

Still referring to FIG. 6, in some embodiments an OCR process will include an OCR algorithm. Exemplary OCR algorithms include matrix matching process and/or feature extraction processes. Matrix matching may involve comparing an image to a stored glyph on a pixel-by-pixel basis. In some case, matrix matching may also be known as “pattern matching,” “pattern recognition,” and/or “image correlation.” Matrix matching may rely on an input glyph being correctly isolated from the rest of the image component 612. Matrix matching may also rely on a stored glyph being in a similar font and at a same scale as input glyph. Matrix matching may work best with typewritten text.

Still referring to FIG. 6, in some embodiments, an OCR process may include a feature extraction process. As used in this disclosure, a “feature” is an individual measurable property or characteristic. In some cases, feature extraction may decompose a glyph into at least a feature 648. Exemplary non-limiting features 648 may include corners, edges, lines, closed loops, line direction, line intersections, and the like. In some cases, feature extraction may reduce dimensionality of representation and may make the recognition process computationally more efficient. In some cases, extracted feature 648 can be compared with an abstract vector-like representation of a character, which might reduce to one or more glyph prototypes. General techniques of feature detection in computer vision are applicable to this type of OCR. In some embodiments, machine-learning process 640 like nearest neighbor classifiers 644 (e.g., k-nearest neighbors algorithm) can be used to compare image features 648 with stored glyph features and choose a nearest match. OCR may employ any machine-learning process 640 described in this disclosure, for example machine-learning processes 640 described with reference to FIGS. 10-8. Exemplary non-limiting OCR software includes Cuneiform and Tesseract. Cuneiform is a multilanguage, open-source optical character recognition system originally developed by Cognitive Technologies of Moscow, Russia. Tesseract is free OCR software originally developed by Hewlett-Packard of Palo Alto, California, United States.

Still referring to FIG. 6, in some cases, OCR may employ a two-pass approach to character recognition. Second pass may include adaptive recognition and use letter shapes recognized with high confidence on a first pass to recognize better remaining letters on the second pass. In some cases, two-pass approach may be advantageous for unusual fonts or low- quality image components 612 where visual verbal content 628 may be distorted. Another exemplary OCR software tool include OCRopus. OCRopus development is led by German Research Centre for Artificial Intelligence in Kaiserslautern, Germany. In some cases, OCR software may employ neural networks.

Still referring to FIG. 6, in some cases, OCR may include post-processing. For example, OCR accuracy can be increased, in some cases, if output is constrained by a lexicon. A lexicon may include a list or set of words that are allowed to occur in a document. In some cases, a lexicon may include, for instance, all the words in the English language, or a more technical lexicon for a specific field. In some cases, an output stream may be a plain text stream or file of characters. In some cases, an OCR process may preserve an original layout of visual verbal content 628. In some cases, near-neighbor analysis can make use of co-occurrence frequencies to correct errors, by noting that certain words are often seen together. For example, “Washington, D.C.” is generally far more common in English than “Washington DOC.” In some cases, an OCR process may make us of a priori knowledge of grammar for a language being recognized. For example, grammar rules may be used to help determine if a word is likely to be a verb or a noun. Distance conceptualization may be employed for recognition and classification. For example, a Levenshtein distance algorithm may be used in OCR post-processing to further optimize results.

With continued reference to FIG. 6, in some embodiments, computing device 604 may extract or otherwise recognize at least a feature 648. Feature 648 may be recognized and/or extracted from image component 612 and/or audio component 616 of solicitation video 608. In some cases, features 648 may be recognized, which are associated with non-verbal content 632. For example, in some cases, visual non-verbal content 632 such as expression of a subject’s emotion may be represented by a number of features 648 which are readily extracted from image component 612 of solicitation video 608. In some cases, recognition and/or extraction of features from image component may include use of machine vision techniques.

Still referring to FIG. 6, in some embodiments, system 600 may include a machine vision process. A machine vision process may use image component 612 from solicitation video 608, to make a determination about verbal 628 and/or non-verbal content 632. For example, in some cases a machine vision process may be used for world modeling or registration of objects within a space. In some cases, registration and/or feature 648 recognition may include image processing, such as without limitation object recognition, feature detection, edge/corner detection, and the like. Non-limiting example of feature 648 detection may include scale invariant feature transform (SIFT), Canny edge detection, Shi Tomasi comer detection, and the like. In some cases, a machine vision process may operate image classification and segmentation models, such as without limitation by way of machine vision resource (e.g., OpenMV or TensorFlow Lite). A machine vision process may detect motion, for example by way of frame differencing algorithms. A machine vision process may detect markers, for example blob detection, object detection, face detection, and the like. In some cases, a machine vision process may perform eye tracking (i.e., gaze estimation). In some cases, a machine vision process may perform person detection, for example by way of a trained machine learning model. In some cases, a machine vision process may perform motion detection (e.g., camera motion and/or object motion), for example by way of optical flow detection. In some cases, machine vision process may perform code (e.g., barcode) detection and decoding. In some cases, a machine vision process may additionally perform image capture and/or video recording. Still referring to FIG. 6, in some cases, machine vision process may perform poseestimation for example to ascertain a relative location or movement of objects within solicitation video 608. include one or more transformations, for example to a view of a frame (or an image or solicitation video 608) relative a three-dimensional coordinate system; exemplary transformations include without limitation homography transforms and affine transforms. In an embodiment, registration of first frame to a coordinate system may be verified and/or corrected using object identification and/or computer vision, as described above. For instance, and without limitation, an initial registration to two dimensions, represented for instance as registration to the x and y coordinates, may be performed using a two-dimensional projection of points in three dimensions onto a first frame, however. A third dimension of registration, representing depth and/or a z axis, may be detected by comparison of two frames; image recognition and/or edge detection software may be used to detect a multiple views of images of an object (from subsequent frames) to derive a relative position along a third (z) axis. In some cases, solicitation video 608 may include a stereo image, having two stereoscopic views, which may be compared to derive z-axis values of points on object permitting, for instance, derivation of further z-axis points within and/or around the object using interpolation. Alternatively or additionally, relative movement within image component 612 (e.g., frame to frame) may be used to ascertain positions of objects, even along a z-axis, for instance by way of kinetic parallax. In some cases, relative motion of objects further away may occur at a different speed than objects nearby, this phenomenon may be used to ascertain a position of objects relative a camera, for example when the camera is moving. Object recognition and pose estimation may be repeated with multiple objects in field of view, including without a subject 620. In an embodiment, x and y axes may be chosen to span a plane common to a field of view of a camera used for solicitation video 608 image capturing and/or an xy plane of a first frame; a result, x and y translational components and (|) may be pre-populated in translational and rotational matrices, for affine transformation of coordinates of object, also as described above. Initial x and y coordinates and/or guesses at transformational matrices may alternatively or additionally be performed between first frame and second frame, as described above. For each point of a plurality of points on object and/or edge and/or edges of object as described above, x and y coordinates of a first frame may be populated, with an initial estimate of z coordinates based, for instance, on assumptions about object, such as an assumption that ground is substantially parallel to an xy plane as selected above. Z coordinates, and/or x, y, and z coordinates, registered using image capturing and/or object identification processes as described above may then be compared to coordinates predicted using initial guess at transformation matrices; an error function may be computed using by comparing the two sets of points, and new x, y, and/or z coordinates, may be iteratively estimated and compared until the error function drops below a threshold level.

Still referring to FIG. 6, in some cases, a machine vision process may use a classifier 644, such as any classifier 644 described throughout this disclosure, for example with reference to FIGS. 10 - 7. Feature 648 recognition may include any feature recognition processes described in this disclosure. In some cases, features 648 may be representative of at least an attribute 624a- n of a subject 620. Attributes 624a-n which may be ascertained from features 648 include manner of dress, environment around subject 620, facial expression of subject 620, tone of voice of subject 620, and the like. Recognition and/or extraction of features 640 may include pre- and/or post-procession. Pre- and/or post-processing may include any pre-processing and/or postprocessing processes described in this disclosure. In some cases, classifier 644 may classify one or more of non-verbal content 632, image component 612, audio component 616, and at least a feature 648 to a keyword 636.

Still referring to FIG. 6, keywords 636 and/or features 648, in some cases, are forms of subject-specific data, that is they represent information associated with an individual subject 620, for example attributes 624a-n. In some cases, subject-specific data, such as without limitation video 608, image component 612, audio component 616, keywords 636, and/or features 648 may be represented and/or processed as a signal. As used in this disclosure, a “signal” is any intelligible representation of data, for example used to convey data from one device to another. A signal may include an optical signal, a hydraulic signal, a pneumatic signal, a mechanical, signal, an electric signal, a digital signal, an analog signal and the like. In some cases, a signal may be used to communicate with a computing device 604, for example by way of one or more ports. In some cases, a signal may be transmitted and/or received by a computing device for example by way of an input/output port. An analog signal may be digitized, for example by way of an analog to digital converter. In some cases, an analog signal may be processed, for example by way of any analog signal processing steps described in this disclosure, prior to digitization. In some cases, a digital signal may be used to communicate between two or more devices, including without limitation computing devices. In some cases, a digital signal may be communicated by way of one or more communication protocols, including without limitation internet protocol (IP), controller area network (CAN) protocols, serial communication protocols (e.g., universal asynchronous receiver-transmitter [UART]), parallel communication protocols (e.g., IEEE 628 [printer port]), and the like.

Still referring to FIG. 6, in some cases, system 600 may perform one or more signal processing steps on a signal. For instance, system 600 may analyze, modify, and/or synthesize a signal representative of data in order to improve the signal, for instance by improving transmission, storage efficiency, or signal to noise ratio. Exemplary methods of signal processing may include analog, continuous time, discrete, digital, nonlinear, and statistical. Analog signal processing may be performed on non-digitized or analog signals. Exemplary analog processes may include passive filters, active filters, additive mixers, integrators, delay lines, compandors, multipliers, voltage-controlled filters, voltage-controlled oscillators, and phase-locked loops. Continuous-time signal processing may be used, in some cases, to process signals which varying continuously within a domain, for instance time. Exemplary non-limiting continuous time processes may include time domain processing, frequency domain processing (Fourier transform), and complex frequency domain processing. Discrete time signal processing may be used when a signal is sampled non-continuously or at discrete time intervals (i.e., quantized in time). Analog discrete-time signal processing may process a signal using the following exemplary circuits sample and hold circuits, analog time-division multiplexers, analog delay lines and analog feedback shift registers. Digital signal processing may be used to process digitized discrete-time sampled signals. Commonly, digital signal processing may be performed by a computing device or other specialized digital circuits, such as without limitation an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a specialized digital signal processor (DSP). Digital signal processing may be used to perform any combination of typical arithmetical operations, including fixed-point and floating-point, real- valued and complex-valued, multiplication and addition. Digital signal processing may additionally operate circular buffers and lookup tables. Further non-limiting examples of algorithms that may be performed according to digital signal processing techniques include fast Fourier transform (FFT), wavelet transform, finite impulse response (FTR) filter, infinite impulse response (IIR) filter, and adaptive filters such as the Wiener and Kalman filters. Statistical signal processing may be used to process a signal as a random function (i.e., a stochastic process), utilizing statistical properties. For instance, in some embodiments, a signal may be modeled with a probability distribution indicating noise, which then may be used to reduce noise in a processed signal.

With continued reference to FIG. 6, computing device 604 associate a subject 620 with at least a job description 652a-n. As used in this disclosure, a “job description” is representation of function; a person may be required to perform, oversee, or be involved with the function. In some cases, a job description may include a number of fields, for example a unique identifier, a title, required attributes, desired attributes, explanation of role, explanation of benefits, location of job, organization (e.g., company) associated with the job description, and the like. In some cases, at least a job description 652a-n may be represented by descriptionspecific data. As used in this disclosure, “description-specific data” is information associated with a specific job description. In some cases, description-specific data may include information job description as well as any information derived directly or indirectly from the job description. In some cases, description-specific data may be generated through analysis and/or pre-processing of a job description.

With continued reference to FIG. 6, computing device 604 may associate subject 620 with at least a job description 652a-n, for example as a function of subject- specific data (e.g., one or more of at least a keyword 636 and at least a feature 648). In some cases, associating a subject 620 with at least a job description 652a-n may include use of subject-specific data and description-specific data. For example, in some cases, associating a subject 620 with at least a job description 652a-n may include a text retrieval process comparing subject-specific data with description-specific data. Document retrieval may include matching of some query against a set of records, for instance text-based records. In some cases, records could be any type of structured or unstructured text, such as job descriptions 652a-n. In some cases, document retrieval may be referred to as, or as a branch of, text retrieval. “Text retrieval,” as used in this disclosure, is information retrieval where the information has a text component. Still referring to FIG. 6, in some cases, computing device 604 associate a subject 620 with at least a job description 652a-n by performing a text retrieval process as a function of at least a keyword 636. For example, in some cases, computing device 604 may query at least a job description 652a-n for presence of at least a keyword 652a-n. Querying at least a job description 652a-n may include one or more of word searching, phrase searching, proximity searching, fulltext searching, field (or metadata) searching, and the like. In some cases, each job description 652a-n may include at least a job description keyword. As used in this disclosure, a “job description keyword” is a word that is of particular relevance to an individual job description. In some cases, associating a subject 620 with at least a job description 652a-n may include comparing at least a keyword 636 from solicitation video 608 with at least a job description keyword.

With continued reference to FIG. 6, in some embodiments, associating querying at least a job description 652a-n may be performed with a test search, for example using at least a keyword 636 as a search term. Text search may include techniques for searching a single computer-stored document or a collection of documents, for example in a database. Text search may include fulltext search. Full-text search may be distinguished from searches based on metadata or on fieldbased searching (e g., fields such as titles, abstracts, selected sections, or bibliographical references). In an exemplary full-text search, computing device 604 may examine all words in every stored document 652a-n as it tries to match search criteria (for example, keywords 636). Alternatively, a text search may be limited to fields, such as with field-based searching.

Still referring to FIG. 6, in some embodiments, text searching may include querying. Database may be implemented, without limitation, as a relational database, a key-value retrieval database such as a NOSQL database, or any other format or structure for use as a database that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Database may alternatively or additionally be implemented using a distributed data storage protocol and/or data structure, such as a distributed hash table or the like. Database may include a plurality of data entries and/or records as described above. Data entries in a database may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in a relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a database may store, retrieve, organize, and/or reflect data and/or records as used herein, as well as categories and/or populations of data consistently with this disclosure. In some cases, querying of at least a job description 652a-n may include any number of querying tools, including without limitation keywords (as described above), field-restricted search, Boolean queries, phrase search, concept search, concordance search, proximity search, regular expression, fuzzy search, wildcard search, and the like. In some cases, keywords 636 may be used to perform a query. In some cases, a document 652a-n creator (or trained indexers) may supply a list of words that describe subject of the document, including without limitation synonyms of words that describe the subject. In some cases, keywords may improve recall, for instance if the keyword list includes a keyword that is not in text of a document 652a-n. In some cases, querying tools may include field-restricted search. A field- restricted search may allow a queries scope to be limited to within a particular field within a stored data record, such as "Title" or "Author." In some cases, a query tool may include Boolean queries. Searches that use Boolean operators (for example, "encyclopedia" AND "online" NOT "Encarta") can dramatically increase precision of a search. In some cases, an AND operator may say, in effect, “Do not retrieve any document unless it contains both of these terms.” In some cases, a NOT operator may say, in effect, “Do not retrieve any document that contains this word.” In some cases, a retrieval list retrieving too few documents, may prompt and OR operator to be used in place of an AND operator to increase recall; consider, for example, "encyclopedia" AND "online" OR "Internet" NOT "Encarta". This search will retrieve documents about online encyclopedias that use the term "Internet" instead of "online." In some cases, search precision and recall are interdependent and negatively correlated in text searching. In some cases, a query tool may include phrase search. In some cases, a phrase search may match only those documents that contain a specified phrase. In some cases, a query tool may include a concept search. In some cases, a concept search may be based on multi-word concepts, for example compound term processing. In some cases, a query tool may include a concordance search. In some cases, a concordance search may produce an alphabetical list of all principal words that occur in a text and may include their immediate context. In some cases, a query tool may include a proximity search. In some cases, a proximity search matches only those documents that contain two or more words that are separated by a specified number of words, are in the same sentence, or an in the same paragraph. A query tool may include a regular expression. Tn some cases, a regular expression may employ a complex but powerful querying syntax that can be used to specify retrieval conditions with precision, for instance database syntax. A query tool may include a fuzzy search. In some cases, a fuzzy search may search for a document that matches given terms while allowing for some variation around them. In some cases, a query tool may include a wildcard search. In some cases, a wildcard search may substitute one or more characters in a search query for a wildcard character such as an asterisk. For example, using a wildcard, such as an asterisk, in a search query “s*n” will search for terms inclusive of “sin,” “son,” “sun,” and the like.

Still referring to FIG. 6, in some cases association of a subject 620 with at least a job description may include one or more of a machine-learning process 640 and/or a classifier 644. Machine-learning process 640 may include any machine-learning process described in this disclosure, for example with reference to FIGS. 10 - 7. Classifier may include any classifier described in this disclosure, for example with reference to FIGS. 10 - 7. In some cases, computing device 604 may associate a subject 620 with at least a job description 652a-n as a function of at least a feature 648. As described above, at least a feature 648 may represent nonverbal content 632 from solicitation video 608. At least a feature 608 may be correlated to at least a job description 652a-n, for example by way of one or more models (e.g., machinelearning models). In some cases, computing device 604 associate a subject 620 with at least a job description 652a-n by correlating, matching, or otherwise comparing subject-specific data with description-specific data. In some cases, one or both of subject-specific data and descriptionspecific data may be at least partially represented by features which are inputs and/or outputs of machine-learning processes 640. For example, a machine-learning model 640 may be trained with deterministic or historical subject-specific data correlated to description-specific data. For examplejob descriptions (previously filled) may be correlated with subject-specific data associated with employees who filled the job description, and optionally a measure of their success in the role (e.g., years in position, reason for leaving, and the like).

Still referring to FIG. 6, in some embodiments, associating subject 620 with at least a job description 652a-n may additionally include classifying, using at least a candidate classifier, the subject 620 to the at least a job description 652a-n. As used in this disclosure, a “candidate classifier” is a classifier that classifies subjects 620 to job descriptions 652a-n or vice versa. Candidate classifier may include any classifier described in this disclosure, including with reference to FIGS. 10 - 7. In some cases, candidate classifier may include a trained machinelearning model, which is trained using candidate training data. As used in this disclosure, “candidate training data” is a training data that correlates one or more of subjects 620, subjectspecific data, and subject attributes 624a-n to one or more of job descriptions 652a-n, description-specific data, and job description data. As used in this disclosure, a “job description datum” is an element of information associated with a job description. In some cases, candidate training data may include deterministic and/or historical data. For example, candidate training data may include subject-specific data for a number of subjects who successfully fill positions correlated with description-specific data related to job descriptions for the successfully filled positions. In some cases, a measure of success may be quantitative, for example number of years working in a position, advancement from position, positive compensation changes (e.g., raises), and the like. Alternatively or additionally, in some cases, a measure of success may be qualitative, for example a survey from a supervisor, a self-assessment and the like.

Still referring to FIG. 6, in some embodiments, computing device 604 may be further configured to determine a relevance as a function of association between a subject 620 and at least a job description 652a-n. As used in this disclosure, “relevance” is a measure of closeness of association, for example between a subject 620 and a job description 652a. In some cases, relevance may be determined by one or more processes for associating a subject 620 with a job description 652a. For example, in some cases, candidate classifier 644 may output a confidence metric that is indicate of confidence associated with a classification of a subject 620 to a job description 652a. In some cases, confidence metric may be a measure of relevance. In some cases relevance may be a quantified metric, for example in arbitrary units or relative units (e.g., percent). In some cases, candidate classifier 644 may use a distance-based classification algorithm (e.g., k nearest neighbor, vector similarity, and the like). Where a distance-based classification algorithm is used, distance may be used directly or indirectly as a confidence metric.

Referring now to FIG. 7, an exemplary frame of a solicitation video 608 is illustrated. Frame may include at least a portion of an image component 612 and/or be associated with at least a portion of an audio component 616. Tn some cases, a solicitation video 608 may include many frames 700, for instance at a frame rate (e.g., 24, 30, 60, or 120 frames per second), thereby facilitating video.

With continued reference to FIG. 7, frame 700 may include an image of subject 704. Alternatively or additionally, in some cases solicitation video 608 may include images of other people or no person at all. An image of subject 700 may be included in solicitation video. Likewise audio of subject 704, for instance speaking, may be included in solicitation video. Audio of subject 704 speaking may include audible verbal content 708. Audible verbal content 708 may include any intelligible language-based communication transmissible audibly. Exemplary forms of audible verbal communication 708 include without limitation speech, singing, chanting, yelling, audible signals (e.g., Morse code), and the like. Audible verbal content 708 may be transcribed from solicitation video 608 and used for associating subject with at least a job description, as described above.

With continued reference to FIG. 7, in some cases, a frame 700 may include visual verbal content 712. As shown in FIG. 7, visual verbal content 712 may include images of written text represented by image component 612. Exemplary forms of visual verbal content include without limitation, digitally generated graphics, images of written text (e.g., typewritten, handwritten, and the like), signage, and the like.

Still referring to FIG. 7, in some embodiments, solicitation video may include a video resume and subject 704 may include a jobseeker. For example, it has long been said that “a picture is worth a thousand words.” As fdm-making technology has become ubiquitous, many people, including younger generations of people, are finding video communication practical and even preferred. Present trends in social media have made this clear. For example, Facebook’s popularity with younger generations was largely supplanted by Instagram, which focuses on sharing photos. And at tine of writing, Instagram’s popularity among younger generations appears irreparably affected by TikTok, which focuses on sharing video.

Still referring to FIG. 7, as more potential employees become more literate with video communication, video communication within a work environment may gain in popularity. Sharing one’s accomplishments and advertising one’s services represents an important application of communication for an individual jobseeker. As jobseekers gain in ability and confidence in video communication, they will choose to put their best foot forward using the medium they believe best facilitates advantageous communication. As a result, some embodiments of the present disclosure inventively anticipate that video resumes will gain in popularity and address an unmet need associated with video resumes. Presently, employers are able to screen many written resumes automatically. However, this technology cannot be used with video resumes. In some case, embodiments described herein improve upon present resume screening systems by allowing the automatic parsing and correlating of video resumes.

Referring now to FIG. 8, a graph 800 depicts an association between a subject 804 and a number of job descriptions 808a-d. Graph 800 illustrates a single subject 804 being associated with a plurality of job descriptions 808a-d, although the inverse relationship may also be considered, for example as described in reference to FIG. 9. In some cases, system 600 may permit a subject-centric perspective, in which a single subject is considered. In some cases, a subject-centric perspective may be useful in serving a subject 804 who is actively looking for a job. In some cases, a subject-centric perspective may allow a subject 804 to consider a plurality of job descriptions 808a-n.

With continued reference to FIG. 8, graph 800 may have a vertical axis 812 disposed to aid in graphical representation of a factor related to association between subject 804 and a job description 808a-d. In some cases, factor may include a subject-centric factor. Factor may include any number of considerations related to association between subject 804 and a job description 808a-d. Exemplary non-limiting considerations include compensation considerations, benefits considerations, professional growth-related considerations, work-life balance considerations, role considerations, title considerations, reputation considerations, location considerations, fit considerations and the like. In some cases, a factor may include relevance of association between a subject 804 and a job description 808a-d. Relevance may be applied to any particular factor, or a general relevance may be considered. In some cases, relevance may be represented on a 0-100 relative scale, for example on vertical axis 812.

Referring now to FIG. 9, a table 900 is depicted that illustrates an association between a job description 904 and a plurality of candidates 908a-d. Table 900 illustrates a single job description 904 being associated with a plurality of candidates 908a-d, although the inverse relationship may also be considered, for example as described in reference to FIG. 8. In some cases, system 600 may permit a description-centric perspective, in which a single job description is considered. In some cases, a description-centric perspective may be useful in serving an employer with an open job who is actively seeking a new hire. In some cases, a descriptioncentric perspective may allow an employer to consider a plurality of candidates 908a-n.

With continued reference to FIG. 9, table may include a number of columns, for example a ranking 912 column, a candidate 908a-d column, and a factor (e g., relevance) 916 column. Ranking 912 may include a ranking of candidates 908a-d, for example from a best candidate 908a to a worst candidate 908d. In some cases, ranking may be based upon a factor, for example a description-centric factor. Factor may include any number of considerations related to association between candidates 908a-d and a job description 904. Exemplary non-limiting considerations include talent consideration, skills considerations, experience considerations, popularity considerations, networking consideration, compensation considerations, benefits considerations, reputation considerations, location considerations, fit considerations and the like. In some cases, a factor may include relevance 916 of association between a job description 904 and a candidate 908a-d. Relevance 916 may be applied to any particular factor or a general relevance 916 may be considered. In some cases, relevance 916 may be represented on a 0-100 relative scale, for example within a relevance 916 column.

Referring now to FIG. 10, an exemplary subject database 1000 is illustrated by way of a block diagram. Computing device 604 may be communicatively connected with subject database 1000. For example, in some cases, subject database 1000 may be local to computing device 604. Alternatively or additionally, in some cases, subject database 1000 may be remote to computing device 604 and communicative with computing device 604 by way of one or more networks. As used in this disclosure, “subject database” is a data structure configured to store data associated with a plurality of subjects 620. In some case, data within subject database 1000 may be labeled to indicate which subject is associated with the data, for instance with a unique subject identification number. Subject database 1000 may store a plurality of solicitation videos 1004. Solicitation videos 1004 may be uploaded to subject database from at least a remote device. Remote device may include any computing device described in this disclosure. As described above with reference to FIG. 6, solicitation videos 1004 may include image components 1008, audio components 1012, non-verbal contents 1016, and/or verbal contents 1020. Additionally, subject database 1000 may include subject keywords 1000. As described above, subject keywords 1024 may be include words that were represented verbally within a solicitation video 1004. Alternatively or additionally, subject keywords 1024 may be associated and/or classified to a subject 620 from non-verbal content or through user entry. Subject database 1000 may additionally store subject features 1028 associated with subjects. As described above, subject features may be recognized or extracted from solicitation videos 1004 associated with a subject by way of a number of processes described in detail in this disclosure.

Referring now to FIG. 11, an exemplary job description database 1100 is illustrated by way of a block diagram. Computing device 604 may be communicatively connected with job description database 1100. For example, in some cases, job description database 1100 may be local to computing device 604. Alternatively or additionally, in some cases, job description database 1100 may be remote to computing device 604 and communicative with computing device 604 by way of one or more networks. As used in this disclosure, “job description database” is a data structure configured to store data associated with a plurality of job descriptions 652a-n. In some case, data within job description database 1100 may be labeled to indicate which job description is associated with the data, for instance with a unique job description identification number. Job description database 1100 may store a plurality of description keywords 1104. As described above, job description keywords may include descriptive words associated with a job description, and thereby facilitate precise association between job descriptions and subjects having similar keywords. Likewise, job description database 1100 may store description features 1108. Description features 1108 may include features associated with a job description and may be used to associate and/or classify a subject with a job description, for example by way of one or more models. In some cases, Job description database 1100 may include job description unstructured data 1112. Job description unstructured data 1112 may include text or other data which may be searched or otherwise associated with a subject. In some cases, job description database 1100 may include job description structured data 1116a-n. Job description structured data 1116a-n may include any data associated with a job description having a particular type, for example data with fields, such as without limitation ‘title,’ ‘role,’ ‘required years of experience,’ ‘salary,’ and the like. In some cases, job description structured data 11 16a-n may include a table with different fields for different data types 1116a-n.

Referring now to FIG. 12, a training data database 1200 is illustrated by way of a block diagram. Computing device 1204 may be communicatively connected with job description database 1200. For example, in some cases, training data database 1200 may be local to computing device 1204. Alternatively or additionally, in some cases, training data database 1200 may be remote to computing device 1204 and communicative with computing device 1204 by way of one or more networks. As used in this disclosure, “training data database” is a data structure configured to store training data. As described in this disclosure, many embodiments may use training data to perform any number of processes. Training data may, in some cases, need to be used, re-used, modified, deleted, and the like. In some cases, training data may need to be conditionally selected. Training data database 1200 may include candidate training data 1204. As described above, candidate training data 1204 may correlated subject-specific data with description-specific data. Candidate training data 1204 may therefore be used to aid with processes that associate or classify a subject and a job description. Additionally, training data sets may be stored in training data database 1200. For example, in some cases, training data that may be used to extract data (e.g., keywords and/or features), for example from solicitation videos 1208 may be stored in training data database 1200; these training sets may include without limitation visual verbal content training data 1208, visual non-verbal content training data 1212, audible verbal content training data 1216, and audible verbal content training data 1220.

Referring to FIGS. 6 - 12, data stored within databases may be organized according to tables. For example, tables with fields and labels. Tables within databases 1000, 1100, and 1200 may be organized in various ways consistent with this disclosure.

Referring now to FIG. 13, an exemplary method 1300 of parsing and correlating solicitation video content is illustrated by way of a flow diagram. At step 1305, method 1300 may include receiving, using a computing device, a solicitation video related to a subject. Computing device may include any computing device described in this disclosure, for example with reference to FIGS. 6 - 12. Solicitation video may include any solicitation video described in this disclosure, for example with reference to FIGS. 6 - 12. Subject may include any subject described in this disclosure, for example including with reference to FIGS. 6 - 12. In some cases, solicitation video may include one or more of at least an image component and at least an audio component. Image component may include any image component described in this disclosure, for example including with reference to FIGS. 6 - 12. Audio component may include any audio component described in this disclosure, for example including with reference to FIGS. 6 - 12. In some case, audio component may include audible verbal content related to at least an attribute of subject. Audible verbal content may include any audible verbal content described in this disclosure, for example including with reference to FIGS. 6 - 12.

With continued reference to FIG. 13, at step 1310, method 1300 may include transcribing, using computing device, at least a keyword as a function of audio component. Keyword may include any keyword described in this disclosure, for example including with reference to FIGS. 6 - 12. In some embodiments, step 1310 may additionally include natural language processing. Natural language processing may include any natural language processing described in this disclosure, for example including with reference to FIGS. 6 - 12.

With continued reference to FIG. 13, at step 1315, method 1300 may include associating, using computing device, subject with at least a job description as a function of at least a keyword. Job description may include any job description described in this disclosure, for example including with reference to FIGS. 6 - 12. In some embodiments, step 1315 may additionally include classifying, using computing device and at least a candidate classifier, subject to at least a job description. Candidate classifier may include any classifier described in this disclosure, for example including with reference to FIGS. 6 - 12. In some embodiments, step 1315 may additionally include querying, using computing device, at least a job description with at least a keyword. In some embodiments, method 1300 may additionally include determining a relevance as a function of association between subject and at least a job description. Relevance may include any relevance described in this disclosure, for example including with reference to FIGS. 6 - 12.

Still referring to FIG. 13, in some embodiments, at least an image component may include visual non-verbal content related to at least an attribute of subject. Visual non-verbal content may include any visual non-verbal content described in this disclosure, for example including with reference to FIGS. 6 - 12. Attribute may include any attribute described in this disclosure, for example including with reference to FIGS. 6 - 12. In some cases, method 1300 may additionally include recognizing, using computing device, at least a feature as a function of visual non-verbal content and associating, using the computing device, subject with at least a job description as a function of the at least a feature. Feature may include any feature described in this disclosure, for example including with reference to FIGS. 6 - 12. In some cases, one or more of recognizing at least a feature and associating subject with at least a job description as a function of the at least a feature uses a machine-learning process. Machine-learning process may include any machine-learning process described in this disclosure, for example including with reference to FIGS. 6 - 12.

Still referring to FIG. 13, in some embodiments, one or more of transcribing at least a keyword (step 1310) and associating subject with at least a job description as a function of the at least a keyword (step 1315) uses a machine-learning process.

Still referring to FIG. 13, in some embodiments at least an image component may include visual verbal content. In some cases, method 1300 may additionally include recognizing, using computing device, at least a keyword as a function of visual verbal content and associating, using the computing device, subject with at least a job description as a function of the at least a keyword. Visual verbal content may include any visual verbal content described in this disclosure, for example including with reference to FIGS. 6 - 12.

Still referring to FIG. 13, in some embodiments at least an audio component may include audible non-verbal content related to at least an attribute of subject. Audible non-verbal content may include any audible non-verbal content described in this disclosure, for example including with reference to FIGS. 6 - 12. In some cases, method 1300 may additionally include recognizing, using computing device, at least a feature as a function of audible non-verbal content and associating, using the computing device, subject with at least a job description as a function of the at least a feature.

Referring now to FIG. 14, an exemplary embodiment of an apparatus 1400 for removing bias in an application is illustrated. Apparatus 1400 includes a computing device 1404; computing device 1404 may implemented in any manner described in this disclosure.

Still referring to FIG. 14, computing device 1404 may be designed and/or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, computing device 1404 may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Computing device 1404 may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and/or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and/or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and/or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and/or parallel processing.

Continuing to refer to FIG. 14, computing device 1404 may be designed and configured to receive candidate data items 1464 as a function of a candidate application. A “candidate data item” is specific data concerning the candidate that is extracted from the application. Candidate data items 1464 may be, without limitation, the candidate’s name, gender, past work experience, social cues, language, location, skills, awards, e-mail address, or any other piece of information given in an application. Candidate application may be validated on an immutable sequential listing. An “immutable sequential listing,” as used in this disclosure, is a data structure that places data entries in a fixed sequential arrangement, such as a temporal sequence of entries and/or blocks thereof, where the sequential arrangement, once established, cannot be altered or reordered. An immutable sequential listing may be, include and/or implement an immutable ledger, where data entries that have been posted to the immutable sequential listing cannot be altered; a “blockchain” is a system of recording information in a way that makes it difficult or impossible to change, hack, or cheat the system. Immutable sequential listing and blockchain is further explained herein with reference to FIG. 3.

Still referring to FIG. 14, computing device 1404 is further configured to identify a potential bias of the candidate data items 1464. A “bias” is prejudice in favor of or against one thing, person, or group compared with another in a way considered to be unfair. A “potential bias,” in this disclosure, is any piece of information found in a candidate data item 1464 that may convey a bias. Potential bias may be detected in a candidate data item 1464 if it is prejudicial in any way. Identifying a potential bias may further include identifying a potential bias field, which is a category of potential biases. Potential bias fields may include ageism, classism, lookism, racism, sexism, bribery, academic bias, gatekeeping, or the like. Computing device 1404 identifies the potential bias from a predetermined plurality of potential biases, which is further explained below.

Still referring to FIG. 14, computing device may identify a plurality of potential biases and classify these biases to a potential bias of the plurality of potential biases. Alternatively or additionally, computing device 1404 may identify plurality of potential biases by querying a bias database 1408 using user-entered data. In an embodiment, bias database 1408 may include any data structure for ordered storage and retrieval of data, which may be implemented as a hardware or software module, bias database 1408 may be implemented, without limitation, as a relational database, a key-value retrieval datastore such as a NOSQL database, or any other format or structure for use as a datastore that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Data entries in a bias database 1408 may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in a relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a bias database 1408 may reflect categories, cohorts, and/or populations of data consistently with this disclosure.

Still referring to FIG. 14, bias database 1408 may be populated with one or more relationships between labels, objects, themes, or the like, as introduced in further detail below, and potential bias; such relationships may be entered in bias database 1408 by users, where user entry may include entry by one or more expert users such as psychologists, medical experts, or the like, “crowd-sourced” entry by large numbers of users, which may be aggregated, or the like. Where user entries are aggregated, aggregated results may include comparison of aggregated values to threshold numbers; for instance, a relationship between a given label and a potential bias may be recorded where more than a threshold percentage of user entries have identified the two as linked. Relationships between labels, objects, themes, or the like, as introduced in further detail below, and candidate data items may alternatively or additionally be entered by computing device from a bias classifier as described below; for instance, a label may be entered in bias database 1408 with a potential bias most probably associated therewith as identified by a bias classifier.

Alternatively or additionally, and still referring to FIG. 14, computing device 1404 may input user inputs to a bias classifier 1412 and receive an output classifying user inputs to one or more candidate data items 1464. Computing device 1404 may generate bias classifier 1412 using a classification algorithm, defined as a processes whereby a computing device 1404 derives, from training data, a model known as a “classifier” for sorting inputs into categories or bins of data. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher’s linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers.

With continued reference to FIG. 14, training data, as used in this disclosure, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements, as described previously in this disclosure.

Still referring to FIG. 14, bias training data 1416, used herein as training data used to generate bias classifier 1412, may include, without limitation, a plurality of data entries, each data entry including one or more themes and/or objects and one or more potential biases represented thereby and/or associated therewith, bias training data 1416 and/or elements thereof may be entered by users, for instance via graphical user interface forms; as a non-limiting example, each such form may present to a user a geometric form, word, image, or the like, and a user may select a label of a candidate data item for each such geometric form, word, image, or the like from a list of labels provided to the user and/or may enter one or more words in a text entry element, which may be mapped to labels using language processing as described below. As a non-limiting example, a candidate data item that portrays user’s email address may be inputted, and computing device may “tag” or identify one or more numbers and or words in the email address as associated with racism or sexism, such as a racial slur, graphic images, sexual language, anti-Semitic language, or the like; a user may further label a name of a restaurant in their work experience to convey prejudiced opinions against a certain ethnicity. As a further example a candidate data item that may be received is a skill, but the skill may insinuate harmful language or actions against a certain gender.

Still referring to FIG. 14, computing device 1404 may be configured to generate bias classifier 1412 using a Naive Bayes classification algorithm. Naive Bayes classification algorithm generates classifiers by assigning class labels to problem instances, represented as vectors of element values. Class labels are drawn from a finite set. Naive Bayes classification algorithm may include generating a family of algorithms that assume that the value of a particular element is independent of the value of any other element, given a class variable. Naive Bayes classification algorithm may be based on Bayes Theorem expressed as P(A/B)= P(B/A) P(A)-KP(B), where P(AZB) is the probability of hypothesis A given data B also known as posterior probability; P(B/A) is the probability of data B given that the hypothesis A was true; P(A) is the probability of hypothesis A being true regardless of data also known as prior probability of A; and P(B) is the probability of the data regardless of the hypothesis. A naive Bayes algorithm may be generated by first transforming training data into a frequency table. Computing device 1404 may then calculate a likelihood table by calculating probabilities of different data entries and classification labels. Computing device 1404 may utilize a naive Bayes equation to calculate a posterior probability for each class. A class containing the highest posterior probability is the outcome of prediction. Naive Bayes classification algorithm may include a gaussian model that follows a normal distribution. Naive Bayes classification algorithm may include a multinomial model that is used for discrete counts. Naive Bayes classification algorithm may include a Bernoulli model that may be utilized when vectors are binary.

With continued reference to FIG. 14, computing device 1404 may be configured to generate bias classifier 1412 using a K-nearest neighbors (KNN) algorithm, as previously described in FIG. 1.

Bias classifier 1412 may content elements to clusters corresponding to labels of potential bias. Where bias classifier 1412 is updated, for instance by adding to a list of potential biases corresponding to clusters and rerunning bias classifier 1412 to classify to the updated list, content elements stored in memory may be subjected to bias classifier 1412 again to update association of content elements with potential bias; each of these actions, including without limitation rerunning bias classifier 1412 to classify to the updated list and/or updating potential biases, may be performed by computing device 1404. bias classifier 1412 may be run against one or more sets of bias training data 1416, where bias training data 1416 may include any form of training data as described above.

In an embodiment, and still referring to FIG. 14, computing device 1404 may detect a candidate data set, defined herein as a set or more elements of candidate data items, correlated with a potential bias in a potential bias training set 1424, which may include training data as described above containing a plurality of pairs of candidate data sets and actions performed by persons in reaction to such candidate data set; such a training set may be compiled using candidate data items gathered from populations of applications. A pair of a candidate data set and a potential bias may include, for instance, a candidate data item or set thereof extract ed from an application who had already previously included bias in its contents; latent patterns may be present in such data that, when considered in larger numbers, may make correlations between combinations of candidate data items and a given potential bias apparent when analyzed.

With continued reference to FIG. 14 computing device 1404 may be designed and configured to identify a set of potential biases that may correspond to candidate data items using a behavior model 1420 trained using a potential bias training set 1424. Computing device 1404 may generate behavior model 1420 using one or more machine-learning processes, which may include any machine-learning processes as described below. Set of potential biases may include a set of behaviors that react to particular elements of candidate data; in other words, behavior model 1420 may act to identify clusters of behavior and associated clusters of candidate data, which in turn may indicate biased behaviors and triggers therefor in candidate data. In some embodiments, clusters so identified may be labeled automatically, for instance using random or pseudorandom identifiers, and/or may presented to a user for labeling based on a social or linguistic characterization of the bias apparently revealed. Such potential biases may be added to bias database 1408.

Continuing to refer to FIG. 14, machine-learning algorithms may be implemented using techniques for development of linear regression models. Linear regression models may include ordinary least squares regression, which aims to minimize the square of the difference between predicted outcomes and actual outcomes according to an appropriate norm for measuring such a difference (e.g. a vector-space distance norm); coefficients of the resulting linear equation may be modified to improve minimization. Linear regression models may include ridge regression methods, where the function to be minimized includes the least-squares function plus term multiplying the square of each coefficient by a scalar amount to penalize large coefficients. Linear regression models may include least absolute shrinkage and selection operator (LASSO) models, in which ridge regression is combined with multiplying the least-squares term by a factor of 14 divided by double the number of samples. Linear regression models may include a multi-task lasso model wherein the norm applied in the least-squares term of the lasso model is the Frobenius norm amounting to the square root of the sum of squares of all terms. Linear regression models may include the elastic net model, a multi-task elastic net model, a least angle regression model, a LARS lasso model, an orthogonal matching pursuit model, a Bayesian regression model, a logistic regression model, a stochastic gradient descent model, a perceptron model, a passive aggressive algorithm, a robustness regression model, a Huber regression model, or any other suitable model that may occur to persons skilled in the art upon reviewing the entirety of this disclosure. Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher- order equation) providing a best predicted output/actual output fit is sought; similar methods to those described above may be applied to minimize error functions, as will be apparent to persons skilled in the art upon reviewing the entirety of this disclosure,

Still referring to FIG. 14, machine-learning algorithms may include, without limitation, linear discriminant analysis. Machine-learning algorithm may include quadratic discriminate analysis. Machine-learning algorithms may include kernel ridge regression. Machine-learning algorithms may include support vector machines, including without limitation support vector classification-based regression processes. Machine-learning algorithms may include stochastic gradient descent algorithms, including classification and regression algorithms based on stochastic gradient descent. Machine-learning algorithms may include nearest neighbors algorithms. Machine-learning algorithms may include Gaussian processes such as Gaussian Process Regression. Machine-learning algorithms may include cross-decomposition algorithms, including partial least squares and/or canonical correlation analysis. Machine-learning algorithms may include naive Bayes methods. Machine-learning algorithms may include algorithms based on decision trees, such as decision tree classification or regression algorithms. Machine-learning algorithms may include ensemble methods such as bagging meta-estimator, forest of randomized tress, AdaBoost, gradient tree boosting, and/or voting classifier methods. Machine-learning algorithms may include neural net algorithms, including convolutional neural net processes.

With continued reference to FIG. 14, models may be generated using alternative or additional artificial intelligence methods, including without limitation by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of "training" the network, in which elements from a training dataset are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning. This network may be trained using training data.

Still referring to FIG. 14, machine-learning algorithms may include supervised machinelearning algorithms. Supervised machine learning algorithms, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised machine-learning process may include a scoring function representing a desired form of relationship to be detected between inputs and outputs; scoring function may, for instance, seek to maximize the probability that a given input and/or combination of elements inputs is associated with a given output to minimize the probability that a given input is not associated with a given output. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various possible variations of supervised machine learning algorithms that may be used to determine relation between inputs and outputs. Machine learning model is further described herein with reference to FIG. 4.

With continued reference to FIG. 14, supervised machine-learning processes may include classification algorithms, defined as processes whereby a computing device 1404 derives, from training data, a model for sorting inputs into categories or bins of data. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers including without limitation k-nearest neighbors classifiers, support vector machines, decision trees, boosted trees, random forest classifiers, and/or neural network-based classifiers.

Still referring to FIG.1, machine learning processes may include unsupervised processes. An unsupervised machine-learning process, as used herein, is a process that derives inferences in datasets without regard to labels; as a result, an unsupervised machine-learning process may be free to discover any structure, relationship, and/or correlation provided in the data. Unsupervised processes may not require a response variable; unsupervised processes may be used to find interesting patterns and/or inferences between variables, to determine a degree of correlation between two or more variables, or the like. Unsupervised machine-learning algorithms may include, without limitation, clustering algorithms and/or cluster analysis processes, such as without limitation hierarchical clustering, centroid clustering, distribution clustering, clustering using density models, subspace models, group models, graph-based models, signed graph models, neural models, or the like. Unsupervised learning may be performed by neural networks and/or deep learning protocols as described above.

Continuing to refer to FIG. 14, machine-learning processes as described in this disclosure may be used to generate machine-learning models. A machine-learning model, as used herein, is a mathematical representation of a relationship between inputs and outputs, as generated using any machine-learning process including without limitation any process as described above, and stored in memory; an input is submitted to a machine-learning model once created, which generates an output based on the relationship that was derived. For instance, and without limitation, a linear regression model, generated using a linear regression algorithm, may compute a linear combination of input data using coefficients derived during machine-learning processes to calculate an output datum. As a further non-limiting example, a machine-learning model may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of "training" the network, in which elements from a training dataset are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning.

Still referring to FIG. 14, computing device 1404 may be configured to generate behavior model 1420 using a first feature learning algorithm 1428 and potential bias training data 1424. A “feature learning algorithm 1428,” as used herein, is a machine-learning algorithm that identifies associations between elements of data in a training data set, where particular outputs and/or inputs are not specified. For instance, and without limitation, a feature learning algorithm 1428 may detect co-occurrences of sets of candidate data items, as defined above, with each other and with potential biases. Computing device 1404 may perform a feature learning algorithm 1428 by dividing candidate data items from a given user into various sub-combinations of such data to create candidate data sets as described above and evaluate which candidate data sets tend to cooccur with which other candidate data sets, and potential bias. In an embodiment, first feature learning algorithm 1428 may perform clustering of data; for instance, a number of clusters into which data from training data sets may be sorted using feature learning may be set as a number of potential biases.

Continuing refer to FIG. 14, a feature learning and/or clustering algorithm may be implemented, as a non-limiting example, using a k-means clustering algorithm. A “k-means clustering algorithm” as used in this disclosure, includes cluster analysis that partitions n observations or unclassified cluster data entries into k clusters in which each observation or unclassified cluster data entry belongs to the cluster with the nearest mean, using, for instance potential bias training set 1424 as described above. “Cluster analysis” as used in this disclosure, includes grouping a set of observations or data entries in way that observations or data entries in the same group or cluster are more similar to each other than to those in other groups or clusters. Cluster analysis may be performed by various cluster models that include connectivity models such as hierarchical clustering, centroid models such as k-means, distribution models such as multivariate normal distribution, density models such as density -based spatial clustering of applications with nose (DBSCAN) and ordering points to identify the clustering structure (OPTICS), subspace models such as biclustering, group models, graph-based models such as a clique, signed graph models, neural models, and the like. Cluster analysis may include hard clustering whereby each observation or unclassified cluster data entry belongs to a cluster or not. Cluster analysis may include soft clustering or fuzzy clustering whereby each observation or unclassified cluster data entry belongs to each cluster to a certain degree such as for example a likelihood of belonging to a cluster; for instance, and without limitation, a fuzzy clustering algorithm may be used to identify clustering of gene combinations with multiple disease states, and bias versa. Cluster analysis may include strict partitioning clustering whereby each observation or unclassified cluster data entry belongs to exactly one cluster. Cluster analysis may include strict partitioning clustering with outliers whereby observations or unclassified cluster data entries may belong to no cluster and may be considered outliers. Cluster analysis may include overlapping clustering whereby observations or unclassified cluster data entries may belong to more than one cluster. Cluster analysis may include hierarchical clustering whereby observations or unclassified cluster data entries that belong to a child cluster also belong to a parent cluster.

With continued reference to FIG. 14, computing device 1404 may generate a k-means clustering algorithm receiving unclassified candidate data items and/or combinations thereof with potential biases as inputs and outputs a definite number of classified data entry cluster wherein the data entry clusters each contain cluster data entries. K-means algorithm may select a specific number of groups or clusters to output, identified by a variable Generating a k- means clustering algorithm includes assigning inputs containing unclassified data to a “k-group ” or “k-cluster” based on feature similarity. Centroids of k-groups or k-clusters may be utilized to generate classified data entry cluster. K-means clustering algorithm may select and/or be provided “ ” variable by calculating k-means clustering algorithm for a range of k values and comparing results. K-means clustering algorithm may compare results across different values of k as the mean distance between cluster data entries and cluster centroid. K-means clustering algorithm may calculate mean distance to a centroid as a function of k value, and the location of where the rate of decrease starts to sharply shift, this may be utilized to select a k value. Centroids of k-groups or k-cluster include a collection of feature values which are utilized to classify data entry clusters containing cluster data entries. K-means clustering algorithm may act to classify a given potential bias to one or more candidate data sets, enabling computing device 1404 to identify candidate data sets correlated with potential biases.

With continued reference to FIG. 14, generating a k-means clustering algorithm as described previously in this disclosure. With continued reference to FIG. 14, potential bias training set 1424 may be stored in and/or retrieved from one or more databases; for instance, a potential bias training set may be stored in and/or retrieved from a potential bias training database. Potential bias training database may include any data structure suitable for use as bias database 1408 as described above. Data entries in a potential bias training database may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in a relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a user database may reflect categories of data consistently with this disclosure. Potential bias training database may include one or more tables from which data records may be retrieved with linking data. One or more tables may include a candidate data item table and a potential bias table, which is described further in this disclosure.

Alternatively or additionally, and still referring to FIG. 14, potential bias training set 1424 and/or data used therefor, including without limitation candidate data items and/or data describing potential bias, may be stored in a potential bias training database 1432, which may be implemented in any way suitable for implementation of a bias database 1416 as described above.

Still referring to FIG. 14, potential bias training database 1432 may be implemented as any data structure suitable for use as clustering database, as described above in this disclosure. Potential bias training database 1432 may store one or more potential biases. One or more tables contained within potential bias training database 1432 may include candidate data items table; candidate data items table may store one or more candidate data items relating to the candidate. One or more tables contained within potential bias training database 1432 may include potential bias table; potential bias table may store one or more candidate data items obtained from an application that were flagged for bias.

Still referring to FIG. 14, computing device 1404 is configured to identify a potential bias of the candidate data items. The identification includes generate, using bias training data, a potential bias classifier, wherein the potential bias classifier inputs candidate data items and outputs a potential bias of the candidate item, extract, from the candidate data item, a plurality of candidate data item content elements, classify each content element of the plurality of candidate item data content elements to a candidate data item object of a plurality of candidate data item objects using an item classifier, and input the plurality of candidate data item objects to the potential bias theme classifier.

Still referring to FIG. 14, computing device 1404 is configured to generate a potential bias classifier 1444 using potential bias training data. Potential bias classifier 1444 inputs candidate data items 1464 and outputs a potential bias of the candidate item. Potential bias training data 1448 may be populated by receiving a plurality of user inputs, for instance via graphical user interface forms; as a non-limiting example, each such form may present to a user at least a candidate data item and a user may select a label for each such candidate data item from a list of labels provided to the user and/or may enter one or more words in a text entry element, which may be mapped to labels using language processing as described below; label selected by user may correspond to a user-entered identification of a potential bias of the candidate data item. Potential bias classifier 1444 may input candidate data items and output potential biases. Biases to which potential bias classifier 1444 may classify candidate data may include biases previously identified using bias database, user entries, and/or behavior model 1420.

Continuing to refer to FIG. 14, computing device 1404 is configured to generate potential bias classifier 1444 using a classification algorithm, which may be implemented, without limitation, using any classification algorithm suitable for generating a bias classifier 1412 as described above. As a non-limiting example, potential bias classifier 1444 may use a K-nearest neighbors algorithm that may be configured to classify an input vector including a plurality of attributes of a candidate item, such as spoken or written text, objects depicted in images, metadata, etc., to clusters representing themes. Potential bias classifier 1444 may alternatively or additionally be created using a naive-Bayes classification algorithm as described above. Potential bias classifier 1444 may enable computing device 1404 to identify a single theme represented by the best-matching cluster and/or some number of best-matching clusters, such as the K best matching clusters; in the latter case, matching a theme as described below may include matching any of the K best themes, or the most probable theme may be treated as the main theme and the remaining matching clusters may be treated as identifying themes of secondary importance.

Still referring to FIG. 14, identifying a potential bias also includes extracting, from the candidate data item 1464, a plurality of candidate data item content elements. A “content element” is an image, word, number, or anything shown in a candidate data item that may convey prejudice. Once these content elements are extracted, computing device 1404 classifies each content element of the plurality of candidate item data content elements to a candidate data item object of a plurality of candidate data item objects using an item classifier 1452.

In an embodiment, and continuing to refer to FIG. 14, computing device 1404 may modify potential bias training data 1448, for instance to replace a candidate data item with plurality of candidate data item objects; plurality of candidate data item objects may be used as attributes of a vector associated with a candidate data item in potential bias training data 1448, for instance for use in KNN or other classification algorithms as described above. Objects of plurality of candidate data item objects may include, without limitation, objects depicted in images or frames of application, objects described in textual data extracted from images or text, and/or converted from spoken words in application, or the like. In an embodiment, computing device 1404 may be configured to extract, from each candidate data item, a plurality of content elements, such as without limitation geometric forms extracted from images and/or video frames, words or phrases of textual data, or the like. Computing device 1404 may be configured to classify each content element of the plurality of content elements to an object of a plurality of candidate data item objects using an item classifier 1452, where the item classifier 1452 may be generated using any classification algorithm as described above. Item classifier 1452 may classify words, phrases, and/or geometrical forms to clusters corresponding to labels of objects, enabling a vector representing presence or relative frequency of objects to be created, for instance by populating a vector index corresponding to each of a list of objects with a number indicating presence or absence of an object corresponding to an index and/or a number indicating a number of occurrences of an object corresponding to an index. Tn the latter case, as a nonlimiting example, a higher number may indicate a greater prevalence of a given object in the candidate data item, which may, as a non-limiting example, cause potential bias classifier 1444 to classify candidate data item to a theme consistent with a higher prevalence of a given object; prevalence and/or relative frequency of an object in candidate data item may also be used, as described below, to determine a degree to which the object is presented in the candidate data item for additional processing. In an embodiment, computing device 1404 may replace candidate data item with a plurality of objects as described above in potential bias training data 1448; for instance, a separate instance of potential bias training data 1448 in which candidate data items are replaced with plurality of objects may be generated, permitting use thereof in place of the original potential bias training data 1448. Where item classifier 1452 is updated, for instance by adding to a list of objects corresponding to clusters and rerunning item classifier 1452 to classify to the updated list, candidate data items stored in memory may be subjected to item classifier 1452 again to update each plurality of candidate data item objects; each of these actions, including without limitation rerunning item classifier 1452 to classify to the updated list and/or updating plurality of objects, may be performed by computing device 1404. Potential bias classifier 1444 may likewise be updated by rerunning classification algorithms on updated media training data 1448. Identifying a potential bias further includes inputting the plurality of candidate data item objects to the potential bias theme classifier 1444.

Still referring to FIG. 14, item classifier 1452 and/or classifiers may be run against one or more sets of item training data 1456, where item training data 1456 may include any form of item training data 1456 as described above. Item training data 1456 may include, without limitation, a plurality of data entries, each data entry including one or more content elements and one or more objects represented thereby. Item training data 1456 and/or elements thereof may be entered by users, for instance via graphical user interface forms; as a non-limiting example, each such form may present to a user a geometric form, word, image, or the like, and a user may select a label for each such geometric form, word, image, or the like from a list of labels provided to the user and/or may enter one or more words in a text entry element, which may be mapped to labels using language processing as described below. Still referring to FIG. 14, extracting a plurality of candidate data item content elements comprises of extracting a plurality of textual elements from a candidate application and classifying the plurality of textual elements to a potential bias using a linguistic item classifier. Computing device 1404 may be configured to classify textual elements to objects using a linguistic item classifier 1460; that is, item classifier 1452 may include a linguistic item classifier 1460. Textual elements may include words or phrases, as described in further detail below, extracted from textual data such as documents or the like. Textual elements may include other forms of data converted into textual data, such as without limitation textual data converted from audio data using speech-to-text algorithms and/or protocols, textual data extracted from images using optical character recognition (OCR), or the like. Linguistic item classifier 1460 may include any classifier described above; linguistic item classifier 1460 may generate an output classifying an element of textual data to an object according to any classification algorithm as described above. In an embodiment, computing device 1404 may train linguistic item classifier 1460 using a linguistic classification training set, which may, as a non-limiting example, include elements of textual data and identifications of one or more objects associated therewith. Linguistic classification training set may, for instance, be populated by user entries of textual data along with corresponding user entries identifying and/labeling objects as described above. Computing device 1404 may be configured to extract the plurality of content elements by extracting a plurality of textual elements from a verbal component of the candidate data item and classify the plurality of textual elements using linguistic item classifier 1460.

Generation of linguistic classification training set, mapping of user entries to object labels, and/or classification of textual objects to labels may alternatively or additionally be performed using a language processing algorithm. As used herein, language processing algorithm may operate to produce a language processing model. Language processing model may include a program automatically generated by language processing algorithm to produce associations between one or more words and/or phrases, including without limitation mathematical associations, between such words. Associations between language elements, where language elements include for purposes herein extracted words and/or object labels, without limitation, mathematical associations, including without limitation statistical correlations between any language element and any other language element and/or language elements. Statistical correlations and/or mathematical associations may include probabilistic formulas or relationships indicating, for instance, a likelihood that a given word and/or phrase indicates a given object label and/or a given additional word and/or phrase. As a further example, statistical correlations and/or mathematical associations may include probabilistic formulas or relationships indicating a positive and/or negative association between at least a word and/or phrase and an object label and/or an additional word.

Still referring to FIG. 14, language processing algorithm may generate the language processing model by any suitable method, including without limitation a natural language processing classification algorithm; language processing model may include a natural language process classification model that enumerates and/or derives statistical relationships between input term and output terms. Algorithm to generate language processing model may include a stochastic gradient descent algorithm, which may include a method that iteratively optimizes an objective function, such as an objective function representing a statistical estimation of relationships between terms, including relationships between input terms and output terms, in the form of a sum of relationships to be estimated. In an alternative or additional approach, sequential tokens may be modeled as chains, serving as the observations in a Hidden Markov Model (HMM). HMMs as used herein are statistical models with inference algorithms that that may be applied to the models. In such models, a hidden state to be estimated may include an association between at least a word and/or phrase and an object label and/or an additional word. There may be a finite number of labels, words and/or phrases, and/or relationships therebetween; an HMM inference algorithm, such as the forward-backward algorithm or the Viterbi algorithm, may be used to estimate the most likely discrete state given a word or sequence of words. Language processing algorithm may combine two or more approaches. For instance, and without limitation, machine-learning program may use a combination of Naive-Bayes, Stochastic Gradient Descent (SGD), and parameter grid-searching classification techniques; the result may include a classification algorithm that returns ranked associations.

Continuing to refer to FIG. 14, generating language processing model may include generating a vector space, which may be a collection of vectors, defined as a set of mathematical objects that can be added together under an operation of addition following properties of associativity, commutativity, existence of an identity element, and existence of an inverse element for each vector, and can be multiplied by scalar values under an operation of scalar multiplication compatible with field multiplication, and that has an identity element is distributive with respect to vector addition, and is distributive with respect to field addition. Each vector in an n-dimensional vector space may be represented by an n-tuple of numerical values. Each unique extracted word and/or language element as described above may be represented by a vector of the vector space. In an embodiment, each unique extracted and/or other language element may be represented by a dimension of vector space; as a non-limiting example, each element of a vector may include a number representing an enumeration of co-occurrences of the word and/or language element represented by the vector with another word and/or language element. Vectors may be normalized, scaled according to relative frequencies of appearance and/or file sizes. In an embodiment associating language elements to one another as described above may include computing a degree of vector similarity between a vector representing each language element and a vector representing another language element; vector similarity may be measured according to any norm for proximity and/or similarity of two vectors, including without limitation cosine similarity, which measures the similarity of two vectors by evaluating the cosine of the angle between the vectors, which can be computed using a dot product of the two vectors divided by the lengths of the two vectors. Degree of similarity may include any other geometric measure of distance between vectors.

Still referring to FIG. 14, language processing algorithm may use a corpus of documents to generate associations between language elements in a language processing algorithm, and computing device 1404 may then use such associations to analyze words extracted from one or more documents and determine that the one or more documents indicate a given relationship between at least a word and/or phrase and an object label and/or an additional word. In an embodiment, computing device 1404 may perform this analysis using a selected set of significant documents, such as documents identified by one or more users and/or expert users, and/or a generalized body of documents and/or co-occurrence data, which may be compiled by one or more third parties. Documents and/or co-occurrence data may be received by computing device 1404 by being uploaded by an expert or other persons using, without limitation, file transfer protocol (FTP) or other suitable methods for transmission and/or upload of documents; alternatively or additionally, where a document is identified by a citation, a uniform resource identifier (URT), uniform resource locator (URL) or other datum permitting unambiguous identification of the document, computing device 1404 may automatically obtain the documents, co-occurrence data, or the like by downloading and/or navigating to one or more centralized and/or distributed collections thereof. Computing device 1404 may alternatively or additionally receive any language processing model from one or more remote devices or third-party devices and utilize such language processing model as described above.

With continued reference to FIG. 14, computing device 1404 is configured to identify a potential bias of a candidate data item 1464 using potential bias classifier 1444. Computing device 1404 may input candidate data item 1464 to potential bias classifier 1444, which may output a potential bias, for instance by identifying a cluster, corresponding to a bias, which is most closely associated with candidate data item 1464, as described above. In an embodiment, computing device 1404 may input a plurality of objects identified in candidate data item 1464 to potential bias classifier 1444. For instance, and without limitation, computing device 1404 may extract a plurality of content elements from candidate data item 1464, where extraction may be performed in any manner described above. Computing device 1404 may classifying each content element of plurality of content elements to an object of a plurality of objects using an item classifier 1452, which may be any item classifier 1452 or collection of item classifiers 1452 as described above. Computing device 1404 may input plurality of objects to the potential bias classifier 1444.

Still referring to FIG. 14, computing device 1404 may combine the above assessments in any suitable way that may occur to persons skilled in the art upon reviewing the entirety of this disclosure. By way of illustration, and as a non-limiting example, computing device 1404 may establish a rule whereby a tally and/or proportion of one or more objects associated with potential bias, and/or grade thereof as described above, combined with a degree of relatedness and/or grade of secondary theme with potential bias, may cause computing device 1404 to block transmission and/or display of candidate data item 1464 with detected potential bias; threshold applied to object and threshold applied to secondary theme may each be insufficient to trigger blocking candidate data item 1464 alone, but the combination thereof may be sufficient to cause computing device 1404 to block candidate data item 1464. Blocking the potential bias includes matching a candidate data item content element to a potential bias. Blocking the potential bias may include classifying a candidate data item content element of the plurality of candidate data item content elements to a non-biased element. A “non-biased element” is an element that is not associated with a bias while having a similar semantic meaning to a biased element.

Still referring to FIG. 14, once potential bias is detected in a candidate data item 1464, computing device 1404 blocks the potential bias by removing the candidate data item 1464 associated with the potential bias from the candidate application 1436. Computing device 1404 may be programmed to block out the candidate data item 1464 by removing it from the candidate application 1436, crossing it out, placing a solid block over the information to make it unreadable, generating outputs that exclude such data, or any other way that allows the information to be imperceptible. This could include anonymizing data.

Referring now to FIG. 15, an exemplary embodiment of a method 1500 of neutralizing and classifying bias in an application is illustrated. At step 1505, method 1500 includes receiving candidate data items as a function of a candidate application. Candidate application is registered on an immutable sequential listing. Immutable sequential listing includes digitally signed assertions.

Still referring to FIG. 15, at step 1510, method 1500 includes identifying a potential bias of the candidate data items. Identifying a potential bias further comprises identifying a potential bias field.

Still referring to FIG. 15, at step 1515, method 1500 includes extracting, from the candidate data item, a plurality of candidate data item content elements. To extract a plurality of candidate data item content elements, the computing device is further configured to extract a plurality of textual elements from a candidate application and classify the plurality of textual elements to a potential bias using a linguistic item classifier.

Still referring to FIG. 15, at step 1520, method 1500 includes classifying each content element of the plurality of candidate item data content elements to a candidate data item object of a plurality of candidate data item objects using an item classifier.

Still referring to FIG. 15, at step 1525, method 1500 includes generating, using bias training data, a potential bias classifier, wherein the potential bias classifier inputs candidate data item objects and outputs a potential bias of the candidate item. Still referring to FIG. 15, at step 1530, method 1 00 includes classifying the plurality of candidate data item objects to potential biases using the potential bias theme classifier. Classifying the candidate data item objects to potential biases further comprises identifying a plurality of potential biases and classifying each candidate data item object to a potential bias of the plurality of potential biases. Identifying the plurality of potential biases further comprises querying a bias database.

Still referring to FIG. 15, at step 1535, method 1500 includes blocking the potential bias by removing the candidate data item associated with the potential bias from the candidate application. Blocking the potential bias includes classifying a candidate data item content element of the plurality of candidate data item content elements to a non-biased element and replacing the candidate data item content element with the non-biased element. Classifying a candidate data item content element to the non-biased element further comprises generating, using a language processing model, a plurality of synonyms of the candidate data item content element and identifying, a synonym of the plurality of synonyms as the non-biased element. Computing device is programmed to block the candidate data item associated with the potential bias by making it unreadable in the candidate application.

It is to be noted that any one or more of the aspects and embodiments described herein may be conveniently implemented using one or more machines (e.g, one or more computing devices that are utilized as a user computing device for an electronic document, one or more server devices, such as a document server, etc.) programmed according to the teachings of the present specification, as will be apparent to those of ordinary skill in the computer art. Appropriate software coding can readily be prepared by skilled programmers based on the teachings of the present disclosure, as will be apparent to those of ordinary skill in the software art. Aspects and implementations discussed above employing software and/or software modules may also include appropriate hardware for assisting in the implementation of the machine executable instructions of the software and/or software module.

Such software may be a computer program product that employs a machine-readable storage medium. A machine-readable storage medium may be any medium that is capable of storing and/or encoding a sequence of instructions for execution by a machine (e.g., a computing device) and that causes the machine to perform any one of the methodologies and/or embodiments described herein. Examples of a machine-readable storage medium include, but are not limited to, a magnetic disk, an optical disc (e.g., CD, CD-R, DVD, DVD-R, etc.), a magnetooptical disk, a read-only memory “ROM” device, a random access memory “RAM” device, a magnetic card, an optical card, a solid-state memory device, an EPROM, an EEPROM, and any combinations thereof. A machine-readable medium, as used herein, is intended to include a single medium as well as a collection of physically separate media, such as, for example, a collection of compact discs or one or more hard disk drives in combination with a computer memory. As used herein, a machine-readable storage medium does not include transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as a data signal on a data carrier, such as a carrier wave. For example, machine-executable information may be included as a data-carrying signal embodied in a data carrier in which the signal encodes a sequence of instruction, or portion thereof, for execution by a machine (e.g., a computing device) and any related information (e.g., data structures and data) that causes the machine to perform any one of the methodologies and/or embodiments described herein.

Examples of a computing device include, but are not limited to, an electronic book reading device, a computer workstation, a terminal computer, a server computer, a handheld device (e.g., a tablet computer, a smartphone, etc.), a web appliance, a network router, a network switch, a network bridge, any machine capable of executing a sequence of instructions that specify an action to be taken by that machine, and any combinations thereof. In one example, a computing device may include and/or be included in a kiosk.

FIG. 16 shows a diagrammatic representation of one embodiment of a computing device in the exemplary form of a computer system 1600 within which a set of instructions for causing a control system to perform any one or more of the aspects and/or methodologies of the present disclosure may be executed. It is also contemplated that multiple computing devices may be utilized to implement a specially configured set of instructions for causing one or more of the devices to perform any one or more of the aspects and/or methodologies of the present disclosure. Computer system 1600 includes a processor 1604 and a memory 1608 that communicate with each other, and with other components, via a bus 1612. Bus 1612 may include any of several types of bus structures including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures.

Processor 1604 may include any suitable processor, such as without limitation a processor incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit (ALU), which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; processor 1604 may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Processor 1604 may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable Logic Device (CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor Processing Unit (TPU), analog or mixed signal processor, Trusted Platform Module (TPM), a floating point unit (FPU), and/or system on a chip (SoC).

Memory 1608 may include various components (e.g., machine-readable media) including, but not limited to, a random-access memory component, a read only component, and any combinations thereof. In one example, a basic input/output system 1616 (BIOS), including basic routines that help to transfer information between elements within computer system 1600, such as during start-up, may be stored in memory 1608. Memory 1608 may also include (e.g, stored on one or more machine-readable media) instructions (e.g., software) 1620 embodying any one or more of the aspects and/or methodologies of the present disclosure. In another example, memory 1608 may further include any number of program modules including, but not limited to, an operating system, one or more application programs, other program modules, program data, and any combinations thereof.

Computer system 1600 may also include a storage device 1624. Examples of a storage device (e.g., storage device 1624) include, but are not limited to, a hard disk drive, a magnetic disk drive, an optical disc drive in combination with an optical medium, a solid-state memory device, and any combinations thereof. Storage device 1624 may be connected to bus 1612 by an appropriate interface (not shown). Example interfaces include, but are not limited to, SCSI, advanced technology attachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and any combinations thereof. In one example, storage device 1624 (or one or more components thereof) may be removably interfaced with computer system 1600 (e.g., via an external port connector (not shown)) Particularly, storage device 1624 and an associated machine-readable medium 1628 may provide nonvolatile and/or volatile storage of machine- readable instructions, data structures, program modules, and/or other data for computer system 1600. In one example, software 1620 may reside, completely or partially, within machine- readable medium 1628. In another example, software 1620 may reside, completely or partially, within processor 1604.

Computer system 1600 may also include an input device 1632. In one example, a user of computer system 1600 may enter commands and/or other information into computer system 1600 via input device 1632. Examples of an input device 1632 include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device, a joystick, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), a cursor control device (e.g., a mouse), a touchpad, an optical scanner, a video capture device (e.g., a still camera, a video camera), a touchscreen, and any combinations thereof. Input device 1632 may be interfaced to bus 1612 via any of a variety of interfaces (not shown) including, but not limited to, a serial interface, a parallel interface, a game port, a USB interface, a FIREWIRE interface, a direct interface to bus 1612, and any combinations thereof. Input device 1632 may include a touch screen interface that may be a part of or separate from display 1636, discussed further below. Input device 1632 may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface as described above.

A user may also input commands and/or other information to computer system 1600 via storage device 1624 (e.g., a removable disk drive, a flash drive, etc.) and/or network interface device 1640. A network interface device, such as network interface device 1640, may be utilized for connecting computer system 1600 to one or more of a variety of networks, such as network 1644, and one or more remote devices 1648 connected thereto. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network, such as network 1644, may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software 1620, etc.) may be communicated to and/or from computer system 1600 via network interface device 1640.

Computer system 1600 may further include a video display adapter 1652 for communicating a displayable image to a display device, such as display device 1636. Examples of a display device include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display adapter 1652 and display device 1636 may be utilized in combination with processor 1604 to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system 1600 may include one or more other peripheral output devices including, but not limited to, an audio speaker, a printer, and any combinations thereof. Such peripheral output devices may be connected to bus 1612 via a peripheral interface 1656. Examples of a peripheral interface include, but are not limited to, a serial port, a USB connection, a FIREWIRE connection, a parallel connection, and any combinations thereof.

The foregoing has been a detailed description of illustrative embodiments of the invention. Various modifications and additions can be made without departing from the spirit and scope of this invention. Features of each of the various embodiments described above may be combined with features of other described embodiments as appropriate in order to provide a multiplicity of feature combinations in associated new embodiments. Furthermore, while the foregoing describes a number of separate embodiments, what has been described herein is merely illustrative of the application of the principles of the present invention. Additionally, although particular methods herein may be illustrated and/or described as being performed in a specific order, the ordering is highly variable within ordinary skill to achieve methods, systems, and software according to the present disclosure. Accordingly, this description is meant to be taken only by way of example, and not to otherwise limit the scope of this invention. Exemplary embodiments have been disclosed above and illustrated in the accompanying drawings. It will be understood by those skilled in the art that various changes, omissions and additions may be made to that which is specifically disclosed herein without departing from the spirit and scope of the present invention.