[001] This application claims priority to U.S. Provisional Application No. 63/363,017, filed on April 14, 2022, the contents of which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

[002] The present disclosure relates generally to the field of computer- implemented systems and methods for health data analysis and management. More specifically, and without limitation, this disclosure relates to systems, methods, and computer-readable media with instructions executable by a processor for computational analysis of health data and the determination of a user’s biological age. This disclosure also relates to the systematic analysis and identification of high- priority data markers for a user, and systems and methods for generating and providing personalized health recommendations. The systems and methods disclosed herein may be used in various applications, including to monitor, manage, intervene, and improve health-related components of a user such as metabolic health, cardiovascular health, muscle health, immune health, and psychological health, among others.

BACKGROUND

[003] Modern health analysis systems often require specific and/or extensive user data sets for their operations. Such data sets may be used by extant systems to, for example, calculate health-related attributes for a user, identify areas of focus or improvement, and provide recommendations. However, the required data sets may not always be readily accessible and/or require specific testing, including blood or genetic tests. As a further drawback, when a new user enrolls or signs up to a health analysis system, the user may not know or wish to share his or her personal health information. Extant systems and methods often fail to account for such circumstances and may be unable to operate without missing user data or prescribed health parameters. Furthermore, extant systems and methods often cannot be personalized, which may lead to recommendations or advice that fail to address the particular user’s health needs or goals.

[004] Therefore, there is a need for improvements in computer-implemented health data analysis and management systems and applications. For example, there is a need for more flexible computational systems and methods. There is also a need for systems and methods that can address and provide their main operations even if there is missing or incomplete user data. Furthermore, solutions are needed that can provide more intelligent and personalized computational analysis of a user’s data to provide estimates of health-related attributes for an individual with a high-degree of accuracy. Improved systems and methods are also needed for providing personalized recommendations, based on a user’s available data and/or health- related goals. Embodiments of the present disclosure provide such improvements and can address one or more of the above-noted drawbacks or disadvantages of extant solutions.

SUMMARY

[005] A system of one or more computers or processors can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One general aspect includes a computer-implemented system for calculating a biological age for an individual. The computer-implemented system may comprise at least one processor configured to receive a plurality of attributes associated with a plurality of individuals; receive a first set of attributes associated with a first individual, apply at least one predictive model to the plurality of attributes and the first set of attributes to estimate a second set of attributes associated with the first individual, and calculate a biological age for the first individual using the first set of attributes and the second set of attributes. Other embodiments of this aspect include corresponding computer-implemented methods executed by computer systems, apparatus, and computer programs recorded on one or more computer storage devices.

[006] Implementations may include one or more of the following features. The at least one processor may be further configured to apply the at least one predictive model to the plurality of attributes associated with the plurality of individuals to generate a plurality of artificial attributes. The at least one processor may be further configured to generate a matching plurality of artificial attributes within a predetermined threshold of the first set of attributes associated with the first individual. The at least one processor may be further configured to generate the second set of attributes associated with the first individual using at least some of the matching plurality of artificial attributes. The at least one processor may be further configured to calculate a group biological age using at least some of the matching plurality of artificial attributes. The at least one processor may be further configured to calculate the biological age for the first individual using the group biological age. The at least one processor may be further configured to calculate a confidence interval associated with the biological age and the group biological age. The first set of attributes may include one or more of age, gender, height, weight, waist circumference, arm circumference, and ethnicity. The at least one processor may be further configured to: receive a guideline representing a value for an attribute in the plurality of attributes; calculate a score for the attribute using the guideline; and calculate the biological age using the score. The at least one processor may be further configured to calculate the biological age by calculating a score relative to a chronological age of the first individual. The at least one processor may be further configured to: calculate a confidence score for the estimated second set of attributes; and classify the calculated biological age based on the confidence score. The at least one processor is further configured to: receive at least one hypothetical attribute for the first individual; and recalculate the biological age using the at least one hypothetical attribute. The at least one predictive model may include one or more machine learning models. The at least one predictive model may include one or more score models and/or generative models. The at least one predictive model may include one or more principal component analysis models. The at least one predictive model may include one or more Bayesian networks. Other embodiments of these aspects include corresponding computer-implemented methods executed by computer systems, apparatus, and computer programs recorded on one or more computer storage devices.

[007] A further general aspect includes a computer-implemented system for providing health recommendations. The computer-implemented system may comprise at least one processor configured to receive a first set of attributes associated with a first individual; estimate, using at least one predictive model, a second set of attributes associated with the first individual; apply the first set of attributes and the second set of attributes to at least one decision tree; generate, with the at least one decision tree, a plurality of classifications for at least some of the attributes in the first set of attributes and the second set of attributes; and provide, based on the plurality of classifications, a health recommendation for the first individual to alter at least one of the attributes in the first set of attributes and the second set of attributes. Other embodiments of this aspect include corresponding computer-implemented methods executed by computer systems, apparatus, and computer programs recorded on one or more computer storage devices.

[008] Implementations may include one or more of the following features. The system where the at least one processor may be further configured to prioritize at least one of the attributes in the first set of attributes and the second set of attributes. The at least one processor may be further configured to provide the health recommendation to improve the prioritized at least one attribute in the first set of attributes and the second set of attributes. The at least one decision tree may be based on at least one of metabolic health, cardiovascular health, muscle health, immune health, psychological health and/or other health-related categories, as disclosed herein. The at least one decision tree may include one or more machine learning models. The one or more machine learning models may be trained based on whether following the health recommendation alters the at least one of the attributes in the first set of attributes and the second set of attributes. The at least one predictive model may include one or more machine learning models. The at least one predictive model may include one or more score models and/or generative models. The at least one predictive model may include one or more principal component analysis models. The at least one predictive model may include one or more Bayesian networks. The first set of attributes and the second set of attributes may include one or more of age, gender, weight, height, arm circumference, waist circumference, systolic blood pressure, diastolic blood pressure, total blood pressure, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, total cholesterol, oxidized LDL (oxLDL) levels, triglyceride levels, fasting plasma glucose (FPG) levels, two-hour glucose levels, non-esterified fatty acids (NEFA) levels, blood pressure, moderate muscle strength, vigorous muscle strength, walking distance per time unit, body fat percentage, squat strength, pushup strength, knee pushup strength, plank strength, balance tests, Short Physical Performance Battery (SPPB) score, sleep patterns, maximum rate of oxygen (VO2), physical activity patterns, resting heart rate, health diseases, family history of health diseases, pregnancy, breast feeding patterns, pregnancy complications, contraceptive practices, perceived stress, immune markers, vitamin levels, allergen markers, and microbiome markers. It will be appreciated that the above attributes are merely examples and that the attributes may relate to any combination of attributes from one or more categories, including metabolic health, cardiovascular health, weight management, muscle health, immune health and inflammation, weight management, mobility and joint health, liver health, mental and emotional health and performance, stress management, gastrointestinal health, vision, appearance, sleep, biorhythm, men’s health, women’s health, sexual health and function, nutrient intake, quality of life measures, and/or other health-related attributes. Further, embodiments of these and other aspects include corresponding computer-implemented methods executed by computer systems, apparatus, and computer programs recorded on one or more computer storage devices.

[009] A still further general aspect includes a computer-implemented system for providing personalized health recommendations. The computer-implemented system may comprise at least one processor configured to receive a plurality of attributes associated with a plurality of individuals, the plurality of attributes being associated with a plurality of sample weights; receive a set of attributes associated with a first individual; generate, using the plurality of sample weights, a plurality of personalized weights associated with the set of attributes associated with the first individual; generate, using the plurality of personalized weights, a priority order for the set of attributes associated with the first individual; and provide, based on the priority order, a personalized health recommendation for the first individual to improve a high priority attribute from the set of attributes associated with the first individual. Other embodiments of this aspect include corresponding computer- implemented methods executed by computer systems, apparatus, and computer programs recorded on one or more computer storage devices.

[010] Implementations may include one or more of the following features.

The system where the at least one processor may be further configured to calculate a biological age using the set of attributes associated with the first individual and the plurality of personalized weights. The at least one processor may be further configured to: estimate a set of attributes associated with the first individual; and calculate the biological age using the estimated a set of attributes. The at least one processor may be further configured to generate the estimated set of attributes using at least one predictive model. The at least one predictive model may include one or more machine learning models. The at least one predictive model may include one or more score models and/or generative models. The at least one predictive model may include one or more principal component analysis models. The at least one predictive model may include one or more Bayesian networks. The at least one processor may be further configured to calculate plurality of confidence scores for the estimated set of attributes. The at least one processor may be further configured to: calculate an average score using the plurality of confidence scores and the plurality of personalized weights; and calculate the biological age using the average score. The estimated set of attributes may include one or more attributes related to metabolic health, cardiovascular health, weight management, muscle health, immune health and inflammation, weight management, mobility and joint health, liver health, mental and emotional health and performance, stress management, gastrointestinal health, vision, appearance, sleep, biorhythm, men’s health, women’s health, sexual health and function, nutrient intake, quality of life measures, and/or other health-related attributes. By way of example, the estimated set of attributes may include one or more of age, gender, weight, height, arm circumference, waist circumference, systolic blood pressure, diastolic blood pressure, total blood pressure, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, total cholesterol, triglyceride levels, fasting plasma glucose (FPG) levels, blood pressure, moderate muscle strength, vigorous muscle strength, walking distance per time unit, body fat percentage, squat strength, pushup strength, plank strength, sleep patterns, maximum rate of oxygen (VO2), and physical activity patterns. Other embodiments of these aspects include corresponding computer- implemented methods executed by computer systems, apparatus, and computer programs recorded on one or more computer storage devices.

[011] Embodiments of the present disclosure also include computer- implemented methods and computer-readable media with instructions executed by at least one processor to provide steps and features corresponding to the above- mentioned systems and the elements, operations, and aspects thereof. The above summary and following detailed description are provided for illustration and does not limit the present disclosure, example embodiments, or claims presented herein. BRIEF DESCRIPTION OF THE DRAWINGS

[012] The following figures/drawings which comprise a part of this specification, illustrate several embodiments of the present disclosure and, together with the description, serve to explain the principles and features of the disclosed embodiments. In the drawings:

[013] FIG. 1 is a schematic representation of an example computer- implemented system for health data analysis and management, consistent with embodiments of the present disclosure.

[014] FIG. 2 illustrates an example computing device which may be employed in connection with the example system of FIG. 1 and other embodiments of the present disclosure.

[015] FIG. 3 illustrates an example computer-implemented system for health data analysis and management, consistent with embodiments of the present disclosure.

[016] FIG. 4 illustrates an example flowchart for calculating a biological age, consistent with embodiments of the present disclosure.

[017] FIG. 5 illustrates an example flowchart for calculating a biological age using scores, consistent with embodiments of the present disclosure.

[018] FIG. 6 illustrates an example flowchart for calculating a biological age using a plurality of artificial attributes, consistent with embodiments of the present disclosure.

[019] FIGS. 7A-7B illustrate examples group scores for calculating a biological age, consistent with embodiments of the present disclosure.

[020] FIG. 7C illustrates examples group biological ages, consistent with embodiments of the present disclosure. [021] FIG. 8 illustrates an example flowchart for providing a health recommendation using at least one decision tree, consistent with embodiments of the present disclosure.

[022] FIGS. 9A-9B provide a high-level illustration of an exemplary decision tree for providing health recommendations, consistent with embodiments of the present disclosure.

[023] FIG. 10 illustrates an example flowchart for using prioritized attributes with a decision tree to provide a health recommendation, consistent with embodiments of the present disclosure.

[024] FIG. 11 illustrates an example flowchart for providing a personalized recommendation with personalized attribute weights, consistent with embodiments of the present disclosure.

[025] FIG. 12 illustrates an example flowchart for providing a personalized recommendation to lower a biological age calculated using personalized attribute weights, consistent with embodiments of the present disclosure.

[026] FIG. 13A illustrates an example welcome screen for a new user, consistent with embodiments of the present disclosure.

[027] FIGS. 13B-13I illustrate example onboarding questions for obtaining attributes associated with the user, consistent with embodiments of the present disclosure.

[028] FIG. 13J illustrates an example display for presenting a biological age calculated using attributes associated with the user, consistent with embodiments of the present disclosure.

[029] FIG. 13K illustrates an example scenario planning to decrease a biological age, consistent with embodiments of the present disclosure. [030] FIG. 13L illustrates an example personalized recommendation strength for a user, consistent with embodiments of the present disclosure.

[031] FIGS. 13M-13P illustrate example personalized recommendations, consistent with embodiments of the present disclosure.

[032] FIG. 13Q illustrates example user information used by a health management application, consistent with embodiments of the present disclosure.

DETAILED DESCRIPTION

[033] Example embodiments are described below with reference to the accompanying drawings. The figures/drawings are not necessarily drawn to scale. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items. It should also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.

[034] In the following description, various working examples are provided for illustrative purposes. However, it will be appreciated that the present disclosure may be practiced without one or more of these details.

[035] Throughout this disclosure there are references to “disclosed embodiments,” which refer to examples of inventive ideas, concepts, and/or manifestations described herein. Many related and unrelated embodiments are described throughout this disclosure. The fact that some “disclosed embodiments” are described as exhibiting a feature or characteristic does not mean that other disclosed embodiments necessarily share that feature or characteristic.

[036] Embodiments described herein include non-transitory computer readable medium containing instructions that when executed by at least one processor, cause the at least one processor to perform a method or set of operations. Non-transitory computer readable mediums may be any medium capable of storing data in any memory in a way that may be read by any computing device with a processor to carry out methods or any other instructions stored in the memory. The non-transitory computer readable medium may be implemented as software, firmware, hardware, or any combination thereof. Software may preferably be implemented as an application program tangibly embodied on a program storage unit or computer readable medium consisting of parts, or of certain devices and/or a combination of devices. The application program may be uploaded to, and executed by, a machine comprising any suitable architecture. Preferably, the machine may be implemented on a computer platform having hardware such as one or more central processing units (“CPUs”), a memory, and input/output interfaces. The computer platform may also include an operating system and microinstruction code. The various processes and functions described in this disclosure may be either part of the microinstruction code or part of the application program, or any combination thereof, which may be executed by a CPU, whether or not such a computer or processor is explicitly shown. In addition, various other peripheral units may be connected to the computer platform such as an additional data storage unit and a printing unit. Furthermore, a non-transitory computer readable medium may be any computer readable medium except for a transitory propagating signal. [037] The memory may include any mechanism for storing electronic data or instructions, including Random Access Memory (RAM), a Read-Only Memory (ROM), a hard disk, an optical disk, a magnetic medium, a flash memory, other permanent, fixed, volatile or non-volatile memory. The memory may include one or more separate storage devices collocated or disbursed, capable of storing data structures, instructions, or any other data. The memory may further include a memory portion containing instructions for the processor to execute. The memory may also be used as a working memory device for the processors or as a temporary storage.

[038] Some embodiments may involve at least one processor. A processor may be any physical device or group of devices having electric circuitry that performs a logic operation on input or inputs. For example, the at least one processor may include one or more integrated circuits (IC), including application-specific integrated circuit (ASIC), microchips, microcontrollers, microprocessors, all or part of a central processing unit (CPU), graphics processing unit (GPU), digital signal processor (DSP), field-programmable gate array (FPGA), server, virtual server, or other circuits suitable for executing instructions or performing logic operations. The instructions executed by at least one processor may, for example, be pre-loaded into a memory integrated with or embedded into the controller or may be stored in a separate memory.

[039] In some embodiments, the at least one processor may include more than one processor. Each processor may have a similar construction, or the processors may be of differing constructions that are electrically connected or disconnected from each other. For example, the processors may be separate circuits or integrated in a single circuit. When more than one processor is used, the processors may be configured to operate independently or collaboratively. The processors may be coupled electrically, magnetically, optically, acoustically, mechanically or by other means that permit them to interact.

[040] Embodiments consistent with the present disclosure may involve a network. A network may constitute any type of physical or wireless computer networking arrangement used to exchange data. For example, a network may be the Internet, a private data network, a virtual private network using a public network, a Wi-Fi network, a local area network (“LAN”), a wide area network (“WAN”), and/or other suitable connections that may enable information exchange among various components of the system. In some embodiments, a network may include one or more physical links used to exchange data, such as Ethernet, coaxial cables, twisted pair cables, fiber optics, or any other suitable physical medium for exchanging data. A network may also include one or more networks, such as a private network, a public switched telephone network (“PSTN”), the Internet, and/or a wireless cellular network. A network may be a secured network or unsecured network. In other embodiments, one or more components of the system may communicate directly through a dedicated communication network. Direct communications may use any suitable technologies, including, for example, BLUETOOTH™, BLUETOOTH LE™ (BLE), Wi-Fi, near field communications (NFC), or other suitable communication methods that provide a medium for exchanging data and/or information between separate entities.

[041] In some embodiments, machine learning networks or algorithms may be trained using training examples, for example in the cases described below. Some non-limiting examples of such machine learning algorithms may include predictive models, support vector machines, random forests, nearest neighbor algorithms, deep learning algorithms, artificial neural network algorithms, convolutional neural network algorithms, recursive neural network algorithms, linear machine learning models, non-linear machine learning models, ensemble algorithms, and so forth. For example, a trained machine learning network or algorithm may comprise an inference model, such as a predictive model, a classification model, a regression model, a clustering model, a segmentation model, an artificial neural network (such as a deep neural network, a convolutional neural network, a recursive neural network, etc.), a random forest, a support vector machine, and so forth. In some examples, the training examples may include example inputs together with the desired outputs corresponding to the example inputs. Further, in some examples, training machine learning algorithms using the training examples may generate a trained machine learning algorithm, and the trained machine learning algorithm may be used to estimate outputs for inputs not included in the training examples. The training may be supervised or non-supervised, or a combination thereof. In some examples, engineers, scientists, processes and machines that train machine learning algorithms may further use validation examples and/or test examples. For example, validation examples and/or test examples may include example inputs together with the desired outputs corresponding to the example inputs, a trained machine learning algorithm and/or an intermediately trained machine learning algorithm may be used to estimate outputs for the example inputs of the validation examples and/or test examples, the estimated outputs may be compared to the corresponding desired outputs, and the trained machine learning algorithm and/or the intermediately trained machine learning algorithm may be evaluated based on a result of the comparison. In some examples, a machine learning algorithm may have parameters and hyperparameters, where the hyper-parameters are set manually by a person or automatically by a process external to the machine learning algorithm (such as a hyper parameter search algorithm), and the parameters of the machine learning algorithm are set by the machine learning algorithm according to the training examples. In some implementations, the hyper-parameters are set according to the training examples and the validation examples, and the parameters are set according to the training examples and the selected hyper-parameters. The machine learning networks or algorithms may be further retrained based on any output.

[042] FIG. 1 illustrates an example computer-implemented system 100 for health data analysis and management, according to embodiments of the present disclosure. As shown in FIG. 1 , system 100 may comprise a health management application 130. “Application” as used herein may refer broadly to any set of electronic instruction, whether commonly known as software, firmware, middleware, microcode, hardware description language, or otherwise. Examples of application include serverless code instances, scripts, and programs. Applications may comprise one or more source code instructions written in a software application language that may be translated into executable, binary, or any other machine- readable code. Examples of software application languages include Python, JavaScript, Java, C, C++, C#, Ruby, although a software application may be written in any other language or format. Health management application 130 may be executed by and/or in electronic communication with one or more servers, databases, and computing devices (such as computing device 160 or computing device 200 described in more detail in connection with FIG. 2), which may be used to perform the functions and operations described in more detail herein. [043] As shown in FIG. 1 , health management application 130 may be in electronic communication with one or more networks 120. Network(a) 120 may be or include any electronic communication channel, such as the Internet, a local or wide area network, Wi-Fi, or BLUETOOTH™, as explained above. Through network(s) 120, health management application 130 may receive one or more inputs 130, and it may transmit one or more outputs 140.

[044] Input(s) 110 may comprise any data or information that may be used by health management application 130 to perform the functions and operations described herein. Non-limiting examples of inputs that may be received by health management application 130 include user data, non-user data, user health data, client device data, database data, metadata, API requests, API responses, or any other data. Input(s) 110 may be entered manually by a user or automatically under the control of software or programmed processes, for example. Input(s) 110 may include original data (e.g., data entered upon the registration of a user), corrected data (e.g., to revise earlier entered data), and/or updated data (e.g., after gathering more data or revised data based on intervention or following health recommendations). Further, input(s) 110 may be entered separately, collectively, and/or periodically.

[045] Output(s) 140 may comprise any data or information, whether intermediary or final, resulting from the functions and operations described herein. Non-limiting examples of outputs that may be generated by health management application 130 include a biological age, high-priority data markers, personalized diet patterns, personalized food recommendations (“hero foods”), meal timings, supplements, sleep pattern suggestions, articles, and stress management recommendations, among others. Output(s) 140 may be provided in response to processing data and/or performing functions or processes, consistent with the present disclosure. Output(s) 140 may include original output(s) (e.g., an original set of output(s) generated in response to input(s) of data first entered by a user), corrected output(s) (e.g., output(s) generated in response to revised input(s) for earlier entered data), and/or updated output(s) (e.g., output(s) generated in response to input(s) of more data or revised data following an intervention or health recommendations followed by a user). Further, output(s) 140 may be provided separately, collectively, and/or periodically.

[046] FIG. 2 illustrates an example computing device 200 for use with health management application 130 of FIG. 1 , consistent with embodiments of the present disclosure. Computing device 200 may be used in connection with the implementation of the example system of FIG. 1 (including, e.g., computing device 160). It is to be understood that in some embodiments the computing device may include multiple sub-systems, such as cloud computing systems, servers, and/or any other suitable components for receiving and processing real-time video.

[047] As shown in FIG. 2, computing device 200 may include one or more processor(s) 230, which may include, for example, one or more integrated circuits (IC), including application-specific integrated circuit (ASIC), microchips, microcontrollers, microprocessors, all or part of a central processing unit (CPU), graphics processing unit (GPU), digital signal processor (DSP), field-programmable gate array (FPGA), server, virtual server, or other circuits suitable for executing instructions or performing logic operations, as noted above. In some embodiments, processor(s) 230 may include, or may be a component of, a larger processing unit implemented with one or more processors. The one or more processors 230 may be implemented with any combination of general-purpose microprocessors, microcontrollers, digital signal processors (DSPs), field programmable gate array (FPGAs), programmable logic devices (PLDs), controllers, state machines, gated logic, discrete hardware components, dedicated hardware finite state machines, or any other suitable entities that can perform calculations or other manipulations of information. In some embodiments, health management application 130 of FIG. 1 is executed in whole or part by computing device 200 with one or more processors 230.

[048] As further shown in FIG. 2, processor(s) 230 may be communicatively connected via a bus or network 250 to a memory 240. Bus or network 250 may be adapted to communicate data and other forms of information. Memory 240 may include a memory portion 245 that contains instructions that when executed by the processor(s) 230, perform the operations and methods described in more detail herein. Memory 240 may also be used as a working memory for processor(s) 230, a temporary storage, and other memory or storage roles, as the case may be. By way example, memory 240 may be a volatile memory such as, but not limited to, random access memory (RAM), or non-volatile memory (NVM), such as, but not limited to, flash memory.

[049] Processor(s) 230 may also be communicatively connected via bus or network 250 to one or more I/O device 210. I/O device 210 may include any type of input and/or output device or periphery device. I/O device 210 may include one or more network interface cards, APIs, data ports, and/or other components for supporting connectivity with processor(s) 230 via network 250.

[050] As further shown in FIG. 2, processor(s) 230 and the other components

(210, 240) of computing device 200 may be communicatively connected to a database or storage device 220. Storage device 220 may electronically store data in an organized format, structure, or set of files. Storage device 220 may include a database management system to facilitate data storage and retrieval. While illustrated in FIG. 2 as a single device, it is to be understood that storage device 220 may include multiple devices either collocated or distributed. In some embodiments, storage device 220 may be implemented on a remote network, such as a cloud storage.

[051] Processor(s) 230 and/or memory 240 may also include machine- readable media for storing software or sets of instructions. “Software” as used herein refers broadly to any type of instructions, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. Instructions may include code (e.g., in source code format, binary code format, executable code format, or any other suitable format of code). The instructions, when executed by one or more processors 230, may cause the processor(s) to perform the various operations and functions described in further detail herein.

[052] Implementations of computing device 200 are not limited to the example embodiment shown in FIG. 2. The number and arrangement of components (210, 220, 230, 240) may be modified and rearranged. Further, while not shown in FIG. 2, computing device 200 may be in electronic communication with other network(s), including the Internet, a local area network, a wide area network, a metro area network, and other networks capable of enabling communication between the elements of the computing architecture. Also, computing device 200 may retrieve data or other information described herein from any source, including storage device 220 as well as from network(s) or other database(s). Further, computing device 200 may include one or more machine-learning models used to implement the neural networks described herein and may retrieve or receive weights or parameters of machine-learning models, training information or training feedback, and/or any other data and information described herein.

[053] FIG. 3 illustrates an example computer-implemented system 300 for computational analysis of health data and providing health-related information and management, consistent with embodiments of the present disclosure. Similar to the example system 100 of FIG. 1 , the example system 300 of FIG. 3 may be implemented with the aid of one or more processors (e.g., processor(s) 230 of the computing device of FIG. 2) or non-transitory computer readable medium, such as a CPU, FPGA, ASIC, or any other processing structure(s) or storage medium. As shown in FIG. 3, system 300 may include a main server 310, an application programming interface (API) gateway 320, one or more user devices 330, an e- commerce platform 340, one or more fitness trackers 350, a health engine 360, and a database manager 370. It is to be understood that the elements shown in FIG. 3 are exemplary only and are not intended to be exhaustive of the example system 300. Additional or fewer components than those shown in FIG. 3 may be used, depending on the specific application or purpose.

[054] Main server 310 may maintain states, handle events, and initiate actions of the example system 300. As shown in FIG. 3, for example, main server 310 may comprise an identity service layer for authenticating users and managing access to resources; an application service layer for managing the performance of health engine 360; an integration data service layer for incorporating data from various sources; an ETL layer for extracting, transforming, and loading data from various data sources; a utility service layer for managing non-business-related logic; and a communication service layer for managing data transfers between health engine 360 and outside components (e.g., user devices 330). Additional or fewer components than those shown in FIG. 3 may be part of the main server 310.

[055] Main server 310 may interface with API gateway 320 for receiving requests and transmitting responses of API calls. API gateway 320 may perform functions to ensure proper handling of requests and responses, such as authentication of user devices 330 issuing API requests or to validate actions with e- commerce platform 340 (e.g., purchase of items). Main server 310 may also interact with user devices 330 directly, such as through the use of a smartphone application. For example, as shown in FIG. 3, main server 310 may transmit “Push Notifications” to alert a user of certain information (e.g., an update, alert, or message). Additionally, main server 310 may receive from a user device a “Request with Token” that identifies the user device 330 as authentic. Further, main server 310 may issue “Recommendations” to a user via a user device 330 to aid the user in health improvement.

[056] Main server 310 may obtain information from user devices 330, such as to obtain inputs for performing the operations described herein. Similarly, main server 310 or user devices 330 may interface with one or more fitness trackers 350 for obtaining input(s) of user data and health-related information, such as cardiac information, number of steps walked during a session (e.g., the past day, past week, or past month), and any other information captured or computed by fitness trackers 350. A user’s credentials may be needed to enter and/or access fitness tracker information, as shown in FIG. 3.

[057] Main server 310 may also be in electronic communication with health engine 360, which may calculate and generate output(s) of data including health- related information, such as biological age and recommendations as described in more detail herein. Similar to health management application 130 of FIG. 1 , health engine 360 may comprise machine-learning algorithms (e.g., one or more predictive models), non-machine-learning algorithms, or a combination of both to perform the functions and operations described herein.

[058] Main server 310 may also communicate with database manager 370. Database manager 370 may store data associated with system 300, such as inputs of data (e.g., user-provided information, etc.), outputs of data (e.g., estimated user attributes, biological age, recommendations, etc.), machine learning models or other algorithms, and any other data. In some embodiments, as shown in FIG. 3, database manager 370 (or databases in general) may be in communication with health engine 360. For example, database manager 370 may provide trained machine learning models to be applied by health engine 360 to calculate a biological age, generate health recommendations, and/or perform other functions and operations of the health engine. Main server 310 may relay information between different components of system 300 (e.g., relaying recommendations from health engine 360 to user devices 330), although in some embodiments the different components may communicate directly.

[059] FIG. 4 illustrates an example method 400 for calculating a biological age, consistent with embodiments of the present disclosure. The example method 400 may be implemented with the aid of one or more processors (e.g., processor(s) 230 of the computing device of FIG. 2) or non-transitory computer readable medium, such as a CPU, FPGA, ASIC, or any other processing structure(s) or storage medium. In some embodiments, example method 400 may be performed by health engine 360 of FIG. 3. It is to be understood that the steps shown in FIG. 4 are exemplary only and are not intended to be exhaustive. Additional or fewer steps than those shown in FIG. 4 may be performed, depending on the specific application or purpose.

[060] At step 410, the at least one processor may receive a plurality of attributes associated with a plurality of individuals. An “attribute,” as used herein, may refer broadly to any characteristic associated with one or more individuals. By way of example, attributes may comprise one or more of metabolic, cardiovascular, muscle, and/or immunity attributes. Non-limiting examples of attributes include anthropometric measurements (e.g., height, weight, waist circumference, arm circumference, etc.), age, biological gender, race, ethnicity, functional tests, medical and laboratory test results, blood levels, heart rate, food intake, sleep schedule, workout schedule, fitness tracker data, genetic profiles, and microbiome profiles, behaviors, and user-defined goals or areas of interest, among others. For example, an attribute may comprise one or more of the following: age, gender, weight, height, arm circumference, waist circumference, systolic blood pressure, diastolic blood pressure, total blood pressure, high-density lipoprotein (HDL) cholesterol, low- density lipoprotein (LDL) cholesterol, total cholesterol, oxidized LDL (oxLDL) levels, triglyceride levels, fasting plasma glucose (FPG) levels, two-hour glucose levels, non-esterified fatty acids (NEFA) levels, blood pressure, moderate muscle strength, vigorous muscle strength, walking distance per time unit, body fat percentage, squat strength, pushup strength, knee pushup strength, plank strength, balance tests, Short Physical Performance Battery (SPPB) score, sleep patterns, maximum rate of oxygen (VO2), physical activity patterns, resting heart rate, health diseases, family history of health diseases, pregnancy, breast feeding patterns, pregnancy complications, contraceptive practices, perceived stress, immune markers, vitamin levels, allergen markers, and microbiome markers. It will be appreciated that the above attributes are merely examples and that the attributes may relate to any combination of attributes from one or more categories, including metabolic, cardiovascular, muscle, and/or immunity attributes. Further, in some embodiments, an attribute may be determined from one or more affirmative or negative responses to one or more health-related questions (e.g., “what is your age” or “what is your weight” or “are you a smoker?” or “are you pregnant?”). In this manner, attributes may be received from each individual through a questionnaire or other list of questions displayed via a health management software application (“app”) or program running on, e.g., a user’s smartphone, laptop, or computer. Attribute data may also be received through online questionnaires managed through one or more websites or through studies or surveys organized by a clinic, health care provider, or organization. Attribute data may also be electronically stored and received from a database or memory device. In some embodiments, attributes are stored as part of enrollment data, survey data, population data, and/or personal health records. Attribute data may also be corrected, revised, or estimated (e.g., in case of incomplete or incorrect entry of such data) and may also be updated (e.g., in case of changes to one or more user attributes due to monitoring (such as by a fitness tracker or other device) or intervention and/or the following of health recommendations by a user and the improvement to their attributes). The above examples are provided for illustration purposes only and are not intended to be exhaustive.

[061] By way of further example, the plurality of attributes associated with the plurality of individuals may be received through various suitable means, including through any electrical medium such as one or more signals, instructions, API calls, databases, memories, hard drives, private data networks, virtual private networks, Wi-Fi networks, LAN or WAN networks, Ethernet cables, coaxial cables, twisted pair cables, fiber optics, public switched telephone networks, wireless cellular networks, BLUETOOTH™, BLUETOOTH LE™ (BLE), Wi-Fi, near field communications (NFC), or any other suitable communication method that provides a medium for exchanging data. For example, the at least one processor may receive the plurality of attributes from a database such as the database shown in FIG. 3 in communication with database manager 370.

[062] At step 420, the at least one processor may receive a first set of attributes associated with a particular individual (e.g., a first individual). The first set of attributes of the first or particular individual may include the same or similar attributes as those listed above for the plurality of individuals. For example, the first set of attributes may include one or more attributes related to metabolic health, cardiovascular health, weight management, muscle health, immune health and inflammation, weight management, mobility and joint health, liver health, mental and emotional health and performance, stress management, gastrointestinal health, vision, appearance, sleep, biorhythm, men’s health, women’s health, sexual health and function, nutrient intake, quality of life measures, and/or other health-related attributes. Further, the first set of attributes of the first individual may also be received in the same or similar manner as the plurality of attributes associated with a plurality of individuals. In some embodiments, the first set of attributes may not include all needed or preferred attributes for generating health components or recommendations. That is, the first set of attributes of the first individual may include missing or unknown attributes. Further, following the example process of FIG. 4, step 420 and the other steps of FIG. 4 (described below) may be repeated when attributes of an individual are corrected or revised (e.g., in case of incomplete or incorrect entry of such data) or updated (e.g., where there are changes to one or more user attributes) due to monitoring (such as by a fitness tracker or other device) or due to intervention and/or the following of health recommendations by a user resulting in the improvement of their attributes).

[063] At step 430, the at least one processor may apply at least one predictive model to the plurality of attributes and the first set of attributes to estimate a second set of attributes associated with the first individual. The second set of attributes may provide estimates of missing or unknown attributes of the first individual (i.e., attributes that are needed or prescribed, but were not included as part of the first set of attributes for the first individual). Various types of predictive models may be used to generate the second set of attributes, such as a Bayesian network, a Principal Component Analysis (PCA) model, a decision tree, a random forest classifier, a binary classifier, a multiclass classifier, a linear classifier, a neural network, a deep neural network, a support vector machine, a Hidden Markov model, or any other model. The one or more predictive models may also be implemented using non-machine learning algorithms, such as a nearest neighbor model, a regression model, a clustering model, an outliers model, a classification model, a least square fitting model, a time series model, or any other model. In some embodiments, two or more types of models (e.g., two machine-learning models, two non-machine learning models, or a combination of machine-learning and nonmachine learning models) may be used in combination with one another, as will be appreciated by those having ordinary skill in the art upon reviewing the present disclosure.

[064] For example, the one or more predictive models may include one or more score models and/or generative models. As a further example, the one or more predictive models may include one or more principal component analysis models. As a still further example, the one or more predictive models may include one or more Bayesian networks. In some embodiments, generative model(s) may be used to generate a synthetic or artificial group of individuals (e.g., a peer group based on population data) that match or correspond to a particular individual. The generative model(s) may also estimate the uncertainty for the individual participant’s missing health data. One or more Bayesian networks or statistical models can generate a significantly large set of samples for any kind of individual and closely replicate the distribution of true data. In some embodiments, score model(s) may also be used to generate scores for a particular individual’s data. Principal component model(s) or similar models may apply weighted ranks for health components, e.g., from healthy to poor metabolism on a continuous scale. Peer group scores may be generated that show an individual’s relation to their peer group in relation to metabolism and/or other health components. Such scoring may be expressed in terms of a scale (e.g., above average, average, below average) or as unit of time (e.g., years relative to the individual’s chronological age). As further disclosed herein, guideline scores may also be generated to show an individual’s health components relative to guideline or predetermined thresholds for their chronological age. These scores may also be expressed and displayed to a user relative to a scale or unit of time. The thresholds may be derived from one or more sources of expert guidelines or recommendations, such as leading sources of health guidelines or recommendations like the National Institutes of Health (NIH) or World Health Organization (WHO).

[065] By way of further example, one or more Bayesian networks (or other statistical models) may be applied to the plurality of attributes associated with the plurality of individuals to generate a plurality of artificial attributes associated with a plurality of artificial individuals. In some embodiments, the artificial individuals may be generated or identified so as to match the first individual based on the first set of attributes, such as through the use of threshold(s) or other suitable means related to one or more attributes (e.g., age, gender, ethnicity, etc). A collection of such one or more artificial attributes generated according to the processes described herein may be associated with the synthetic or artificial individuals. With the matching group of artificial individuals and attributes, the predictive model(s) may estimate missing or unknown attributes of the first individual, so as to generate a second set of attributes associated with the first individual. In some embodiments, the generated second set of attributes may be larger than the first set of attributes. Further, in some embodiments, the predictive model(s) may output a confidence score associated with the generated artificial attributes and/or second set of attributes.

[066] Consistent with FIG 4 and the other examples embodiments disclosed herein, the plurality of attributes of the plurality of individuals, the first set of attributes of the particular individual, and the estimated second set of attributes may include any combination of attributes for one or more categories, including metabolic health, cardiovascular health, weight management, muscle health, immune health and inflammation, weight management, mobility and joint health, liver health, mental and emotional health and performance, stress management, gastrointestinal health, vision, appearance, sleep, biorhythm, men’s health, women’s health, sexual health and function, nutrient intake, quality of life measures, and/or other health-related attributes. As previously noted, examples of attributes include one or more of the following: age, gender, weight, height, arm circumference, waist circumference, systolic blood pressure, diastolic blood pressure, total blood pressure, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, total cholesterol, oxidized LDL (oxLDL) levels, triglyceride levels, fasting plasma glucose (FPG) levels, two-hour glucose levels, non-esterified fatty acids (NEFA) levels, blood pressure, moderate muscle strength, vigorous muscle strength, walking distance per time unit, body fat percentage, squat strength, pushup strength, knee pushup strength, plank strength, balance tests, Short Physical Performance Battery (SPPB) score, sleep patterns, maximum rate of oxygen (VO2), physical activity patterns, resting heart rate, health diseases, family history of health diseases, pregnancy, breast feeding patterns, pregnancy complications, contraceptive practices, perceived stress, immune markers, vitamin levels, allergen markers, and microbiome markers.

[067] At step 440, the at least one processor may calculate a biological age for the first individual using the first set of attributes and the second set of attributes. Various suitable processes or formulas for calculating a biological age may be used. For example, the following formula may be applied to an individual’s attributes to calculate a biological age:

[068] BA = CA + a * S + b * S ₂ + ••• n * S _n

[069] In the formula above, BA may correspond to the particular individual’s biological age, CA may correspond to the particular individual’s chronological age, S? may correspond to a score associated with a first attribute, a may correspond to a weighting coefficient associated with the first attribute, S2 may correspond to a score associated with a second attribute, b may correspond to a weighting coefficient associated with the second attribute, S _n may correspond to a score associated with an n ^th attribute, n may correspond to a weighting coefficient associated with the n ^th attribute, and so forth. Each score (S?, S2, S _n , etc.) may in turn be calculated using a formula associated with one or more attributes. In some embodiments, for example, a score may be calculated using one or more medical guidelines. As a non-limiting example, in embodiments where body mass index (BMI) is an attribute, a score may correspond to a difference between a particular individual’s body mass index and an ideal body mass index according to a medical guideline. Accordingly, the particular individual’s score may lead to a lower biological age when it is closer to the ideal body mass index, and it may lead to a higher biological age when it is further from the ideal body mass index. Other methods of calculating a score may be used, however, which may include using mean values, standard deviations, best fit models, least square fitting, regressions, statistical models, machine-learning models, or any other process of assessing an individual’s attribute.

[070] As a further example, in embodiments where a plurality of artificial attributes associated with a plurality of artificial individuals are generated, a range of biological ages may be calculated for at least some of the artificial individuals. The plurality of artificial individuals may be generated so as to match the particular individual, such as through the use of a threshold applied to at least some of the particular individual’s attributes, as discussed above. The same or similar formula as described above may be used to calculate the range of biological ages, although other processes may be used. The calculated range of biological ages may be used to calculate the biological age of the particular individual, such as by estimating or refining the biological age. For example, the particular individual’s biological age may be estimated as the mean biological age in the range of biological ages, or a calculated biological age may be increased or decreased by a predetermined amount based on deviations between the calculated biological age and the range of biological ages. Other process for calculating a biological age using artificial attributes may be used, as will be appreciated by those having ordinary skill in the art upon reviewing the present disclosure. [071] The example method 400 of FIG. 4 may be repeated or otherwise executed to provide an updated biological age for an individual. For example, the method of FIG. 4 may be performed when one or more first attributes of an individual are corrected or revised (e.g., in case of incomplete or incorrect entry of such data) or updated (e.g., where there are changes to one or more user attributes) due to monitoring (such as by a fitness tracker or other device) or due to intervention and/or the following of health recommendations by a user resulting in the improvement to at least one of their attributes). The entry of new or updated attribute(s) for an individual may automatically trigger the operation of the method of FIG. 4 and calculation of an individual’s biological age, or an individual may manually instruct (e.g., through a smartphone app and/or graphical user interface) the operation of the method of FIG. 4 to see how their biological age changes as a result of an improvement in one or more of their health attributes.

[072] Consistent with the present disclosure, the biological age calculated by method 400 may be based on a plurality of attributes, including attributes related to metabolic health, cardiovascular health, weight management, muscle health, immune health and inflammation, weight management, mobility and joint health, liver health, mental and emotional health and performance, stress management, gastrointestinal health, vision, appearance, sleep, biorhythm, men’s health, women’s health, sexual health and function, nutrient intake, quality of life measures, and/or other health-related attributes. Further, weighting factors may be applied to one or more attributes so that some attribute data weighs more heavily than other attribute data when calculating an individual’s biological age. Such weighting factors may be selected for an individual based on their personal attributes and/or needed areas of improvement. In some embodiments, weighting factors may vary across one or more attributes or attribute categories (e.g., metabolic, cardiovascular, muscle, and/or immunity, etc.). A user may also select which attribute(s) to focus on (i.e., weigh more heavily) and/or they may select to have their biological age calculated based on one or more specific categories of attributes (e.g., a metabolic biological age, a cardiovascular biological age, a muscle biological age, or an immunity biological age alone or a biological age based on a combination of different categories of attributes, such as metabolic and cardiovascular attributes). As will be appreciated, these are just examples and other weighting arrangements and features may be implemented, consistent with the present disclosure.

[073] In some embodiments, a biological age for a user may be calculated using one or more specified or target attributes. For example, the at least one processor may be configured to receive at least one hypothetical or target attribute for a particular individual. The hypothetical attribute(s) may correspond to a target goal or scenario planning aimed at improving one or more attributes for the particular individual. Using the hypothetical attribute(s) alone or together with existing attributes of the user, the at least one processor may be configured to recalculate the biological age. Accordingly, the particular individual may be able to see the effect of improving specific attribute(s) on his or her biological age and may consequently be motivated to alter his or her behaviors, including related to diet and/or exercise.

[074] In some embodiments, the at least one processor may be configured to provide trend modeling and allow a user to see their biological age in the future, assuming certain attribute(s) and the maintenance of a current lifestyle of diet, exercise, etc. Trend modeling may be implemented using aging data or similar surveys tracking the attributes of a population set (i.e., plurality of users) over time in view of their lifestyle. Examples of such aging data or surveys include The Irish LongituDinal Study on Aging (TILDA) conducted by Trinity College Dublin, which is a large-scale, longitudinal study on aging in Ireland. Such datasets may be used for modeling and group matching to predict a user’s biological age in the future. In such embodiments, the at least one processor may receive a set of input(s) including one or more current attributes of a user and lifestyle data for the user (e.g., defining the user’s lifestyle habits of diet, exercise, drinking, smoking, etc). Hypothetical attributes and/or a further target date may also be provided as input data. With these inputs, the at least one processor may calculate a future biological age for the user. In this manner, the user may be able to see the impact of attributes and lifestyle behavior on their future biological age and be motivated to seek health recommendations and intervention to improve their aging profile and overall health.

[075] Furthermore, in some embodiments, the at least one processor may be configured to provide health-based recommendations. The recommendations may be provided to positively impact one or more aspects of an individual’s health, including a biological age, one or more attributes, mental or emotional health, or any other aspect, as explained in further detail herein. In some embodiments, machine learning models may be used to learn the impact of following a recommendation for a particular individual. For example, a machine learning model may learn the impact of a meal plan, exercise schedule, sleep patterns, supplement regime, or any other recommendation, and may consequently tailor recommendations for the particular individual, as disclosed herein. It will be appreciated that other suitable approaches for providing and refining recommendations may be used.

[076] FIG. 5 illustrates an example flowchart 500 for calculating a biological age using scores, consistent with embodiments of the present disclosure. The example flowchart 500 may be implemented with the aid of one or more processors (e.g., processor(s) 230 of the computing device of FIG. 2) or non-transitory computer readable medium, such as a CPU, FPGA, ASIC, or any other processing structure(s) or storage medium. In some embodiments, example flowchart 500 may be performed by health engine 360 of FIG. 3. It is to be understood that the steps shown in FIG. 5 are exemplary only and are not intended to be exhaustive.

Additional or fewer components than those shown in FIG. 5 may be performed, depending on the specific application or purpose.

[077] At block 510, the at least one processor may receive data from a dataset corresponding to all user data preferred or prescribed for processing by the health management application (although in some embodiments, it may receive only a subset of all possible user data). The dataset may include, for example, population data based on a study of the health and/or nutritional status of a population of certain demographic(s) (e.g., adults, children, males, females, or individuals within a certain age range, etc.). An exemplary dataset may be the National Health & Nutrition Examination Survey (NHANES) dataset, which is a representative study of adults and children in the United States. It is to be understood that other population datasets or representative datasets appropriate for a particular application may be used. For example, the dataset may be generated manually by a skilled artisan and/or a partially or wholly machine-assisted process in view of the present disclosure. In some embodiments, the dataset may be generated artificially through a generative network trained to generate artificial user data, a combination of both, or any other process.

[078] At block 520, the at least one processor may receive one or more attributes of a particular individual (e.g., anthropometric measurements, age, biological gender, ethnicity, user-defined goals or areas of interest, behaviors, functional tests, medical and laboratory test results, blood levels, food intake, fitness trackers, genetic profiles, and microbiome profiles, etc.). In some embodiments, the particular individual may be a user of the health management application 130 of FIG. 1. For example, the health management application 130 may be implemented as an application running on a user’s smartphone, laptop, computer, or other computing device. Further, the at least one processor may receive the one or more attributes through user device(s) 330, fitness tracker(s) 350, database(s) connected to database manager 370, or any other source. The received attributes may be possibly incomplete, meaning only a subset of all attributes that could be received or processed by the health management application may be available for the particular individual. This may be a result, for example, if the particular individual is a new user that has recently signed up to the health management application and/or has provided only some of his or her information due to certain attributes being unknown or unavailable.

[079] At block 530, the at least one processor may input the dataset and the attributes associated with the particular individual to at least one predictive model. Any combination of predictive model(s) may be used, as discussed above, including one or more Bayesian networks, Principal Component Analysis (PCA) models, decision trees, random forest classifiers, binary classifiers, multiclass classifiers, linear classifiers, neural networks, deep neural networks, support vector machines, Hidden Markov models, and/or other suitable models.

[080] At block 540, the at least one processor may generate attributes associated with a plurality or group of individuals using the predictive model(s). In some embodiments, the artificial attributes may be generated so as to match or substantially match the attributes of a particular individual. For example, a conditional probability distribution technique may be used to generate random samples that are conditional on the received attributes of the particular individual. In such embodiments, a trained machine learning model (e.g., a Bayesian network) may be used to generate the conditional probability distribution. As another example, a predetermined threshold may be applied so as to ensure the artificial attributes are within a range of the attributes of the particular individual. Other techniques to ensure that generated artificial attributes match the received attributes associated with the particular individual may be used, as will be appreciated by those having ordinary skill in the art upon reviewing the present disclosure.

[081] At block 550, the at least one processor may estimate attributes associated with the particular individual using one or more predictive model(s). The estimated attributes may be used to supplement the attributes provided for the individual and/or address missing attributes (e.g., one or more metabolic, cardiovascular, muscle, and/or immunity attributes). In some embodiments, artificial and/or non-artificial attributes may be used to make the estimation. For example, a mean value may be computed from the plurality of artificial and/or non-artificial attributes, which may be used as an estimated attribute for the particular individual. Additionally, or alternatively, an upper and lower value may be computed from the plurality of artificial and/or non-artificial attributes, so as to provide a range of values for the estimated attribute. Other processes for estimating an attribute may be used, however, as will be appreciated by those having ordinary skill in the art upon reviewing the present disclosure.

[082] At block 560, the at least one processor may calculate one or more group scores from the plurality of artificial and/or non-artificial attributes. A “score,” as used herein, may represent a deviation of an attribute from a reference value, which may be an average value (e.g., from among a peer group), a recommended value (e.g., from a medical guideline), a suggested value (e.g., based on personalized health recommendations), or other value. In some embodiments, for example, the at least one processor may receive a selection of a group of individuals having one or more group attributes, such as by allowing a user to select a group for comparison which may be based on age, ethnicity, or any other attribute. The at least one processor may calculate a score using the one or more group attributes, and it may calculate a biological age using the score, as explained herein. In some other embodiments, for example, the at least one processor may be configured to receive a recommended value associated with one or more medical guidelines for each attribute. The at least one processor may subsequently calculate a difference between each attribute and its corresponding recommended value. The difference may be further processed to arrive at a more meaningful score for a particular application, such as by multiplying each score by a scaling or weighting factor suitable for a particular attribute or for the particular individual. Further, in some embodiments, the scores for each attribute may be combined into a single score. For example, a mean or average may be calculated from among the scores for the attributes, although any other mathematical formula may be used. Other processes or methods for arriving at a group score may be used, as will be appreciated by those having ordinary skill in the art upon reviewing the present disclosure.

[083] At block 570, the at least one processor may calculate one or more personalized scores from the received and/or estimated attributes for the particular individual. As disclosed herein, the particular individual may be a user of health management application 130 of FIG. 1. In some embodiments, the personalized score may be calculated in the same or similar manner as the group score. In some other embodiments, the personalized score(s) may be calculated at least in part based on the group score(s). For example, a mean or average may be calculated from one or more scores associated with one or more artificial and/or non-artificial attributes, which may then be used as an estimated score for any received and/or estimated attribute for the particular individual. The estimated score may be multiplied by a scaling or weighting factor suitable for a particular attribute or for the particular individual to arrive at a more personalized estimated score. Other processes or methods for arriving at a personalized score may be used, as will be appreciated by those having ordinary skill in the art upon reviewing the present disclosure.

[084] At block 580, the at least one processor may calculate a personalized biological age for the particular individual. Various methods or processes for calculating a biological age may be used, as discussed above. For example, any personalized scores calculated using the received and/or estimated attributes, along with weighting coefficients, may be used to calculate a personalized biological age. Other methods or processes may be used, however. In some embodiments, a group biological age may be calculated for a plurality of artificial or non-artificial individuals. For example, the group scores(s) may be used to calculate one or more group biological ages, in the same or similar manner as discussed before. The one or more group biological ages may then be used to calculate the personalized biological age for the particular individual. For example, a mean or average biological age may be calculated from multiple group biological ages, although any other mathematical approach may be used. Subsequently, the group biological age may be used to calculate the personalized biological age, such as by using the group biological age as a starting value that may be further refined using attributes or other data associated with the particular individual. As another example, the group biological age may be used to validate a previously calculated personalized biological age, such as by modifying the personalized biological age if it deviates by a predetermined amount from the group biological age. It will be appreciated that the steps shown in FIG. 5 are exemplary only and are not intended to be limiting or exhaustive.

[085] FIG. 6 illustrates an example method 600 for calculating a biological age using a plurality of artificial attributes associated with a plurality of artificial individuals, consistent with embodiments of the present disclosure. The example method 600 may be implemented with the aid of at least one processor (e.g., processor(s) 230 of the computing device of FIG. 2) or non-transitory computer readable medium, such as a CPU, FPGA, ASIC, or any other processing structure(s) or storage medium. In some embodiments, example method 600 may be performed by health engine 360 of FIG. 3. It is to be understood that the steps shown in FIG. 6 are exemplary only and are not intended to be exhaustive. Additional or fewer steps than those shown in FIG. 6 may be performed, depending on the specific application or purpose.

[086] At step 610, the at least one processor may receive a plurality of attributes associated with a plurality of individuals. The plurality of attributes associated with a plurality of individuals may be the same or similar as those listed above (e.g., anthropometric measurements, age, race, biological gender, blood levels, etc.). Further, the plurality of attributes associated with a plurality of individuals may be received in the same or similar manner as discussed above (e.g., through one or more API calls, databases, memories, networks, etc.). [087] At step 620, the at least one processor may receive a first set of attributes associated with a particular individual (e.g., a first individual). The first set of attributes of the particular individual may be the same or similar as those listed above. Further, the first set of attributes of the particular individual may be received in the same or similar manner as the plurality of attributes associated with a plurality of individuals.

[088] At step 630, the at least one processor may apply one or more predictive model(s) to the received plurality of attributes and the received first set of attributes to generate a matching plurality of artificial attributes within a predetermined threshold of the first set of attributes. For this purpose, any suitable combination of analytics and threshold(s) may be used. As a non-limiting example, in embodiments where a statistical model is used (e.g., a Bayesian model), a threshold may correspond to a best fit given the particular model, such as by maximizing a likelihood function given the first set of attributes, calculating a posterior predictive distribution based on the first set of attributes, determining a regression function based on the first set of attributes, and/or other process(es) as will be appreciated by those having ordinary skill in the art upon reviewing the present disclosure. Artificial attributes may subsequently be generated based on the model’s best fit. As a further example, a threshold rejection function may be applied, such that a generated artificial attributes above or below predetermined threshold(s) may be rejected. Such thresholds may be derived from one or more sources of expert guidelines or recommendations, such as leading sources of health guidelines or recommendations like the National Institutes of Health (NIH) or World Health Organization (WHO). Other suitable thresholds may be used, however, based on the particular context or application. [089] At step 640, the at least one processor may, using the matching plurality of artificial attributes, estimate a second set of attributes associated with the particular individual. In some embodiments, the second set of attributes may be selected from among the matching plurality of artificial attributes, such as by selecting them using a predetermined rule, randomly, or through another suitable process. For example, in embodiments where a distribution of artificial attributes is generated, a value from that distribution may be selected and added to the second set of attributes. In other embodiments, the second set of attributes may be generated based on the matching plurality of artificial attributes. For example, an average or mean value may be calculated from such a distribution, which may subsequently be added to the second set of values. Other ways of estimating attributes using the plurality of artificial attributes may be used, however, as will be appreciated from this disclosure.

[090] At step 650, the at least one processor may calculate a group biological age from at least some of the matching plurality of artificial attributes. A group biological age may be calculated from the artificial attributes directly, such as by averaging or otherwise combining the same artificial attributes into a single value, and computing the group biological age from such combined values. As another example, the group biological age may be calculated from a plurality of group biological ages, such as by calculating a biological age from the artificial attributes associated with one or more artificial individuals, and subsequently averaging or otherwise combining them into a single group biological age. Other ways of calculating a group biological age may used, however.

[091] At step 660, the at least one processor may, using the group biological age, calculate a biological age for the particular individual. In some embodiments, for example, the group biological age may be used directly to calculate a biological age for the particular individual, such as by using the group biological age as a starting value that may be further refined using attributes or other data associated with the particular individual. As another example, the group biological age may be used to validate a previously calculated biological age based on at least the second set of attributes, such as by modifying the particular individual’s biological age if it deviates by a predetermined amount from the group biological age. Other ways of using the group biological age may be employed, however. It will be appreciated that the steps shown in FIG. 6 are exemplary only and are not intended to be limiting or exhaustive.

[092] FIGS. 7A-7B illustrate example group scores for calculating a biological age, consistent with embodiments of the present disclosure. FIG. 7A depicts three group scores, 710a, 720a, and 730a, and FIG. 7B depicts three group scores, 710b, 720b, and 730b. Each score in FIGS. 7A and 7B includes a different mean value and confidence interval, and which may be generated based on a plurality of attributes (e.g., artificial, estimated, and/or known attributes), as discussed above. As further shown in FIGS. 7A and 7B, the age group scores may be represented as a deviation from a guidance value (e.g., a medical guideline or value based on NHANES data or similar), although any other reference value may be used (e.g., a personalized guidance value). The group scores may be represented as a mean value, represented as a circle, and an upper and lower value for the confidence interval, represented as two vertical lines to the right and left of the mean value. A group score may be represented using other mathematical representations, however, as will be appreciated by those having ordinary skill in the art upon reviewing the present disclosure. [093] As disclosed above, a group score may be calculated from a matching plurality of attributes associated with a particular individual. For example, group score 710a may be calculated from an individual who is a white male of age 44 with a BMI of 28 kg/m2, an arm circumference of 32 cm, a waist circumference of 96 cm, and a fasting glucose level of 5.5 mmol/L. Next, group score 720a may be calculated from the same individual with additional known attributes, such as a diastolic blood pressure of 70 mmHg and a systolic blood pressure of 125 mmHg. As can be appreciated from comparing group scores 710a and 720a, as a result of the additional known attributes, the confidence interval for the group score may be increased as shown by the smaller gap between the confidence interval’s upper and lower values. Similarly, group score 730a may be calculated from the same individual with yet additional known attributes, such an HDL cholesterol level of 0.8 mmol/L, a triglyceride level of 1.5 mmol/L, a diastolic blood pressure of 6 mmol/L, and a 2-hour glucose level of 8 mmol/L. As can also be appreciated from comparing group scores 720a and 730a, because of these additional known attributes, the confidence interval for the group score may be increased further as shown by the yet smaller gap between the confidence interval’s upper and lower values.

[094] Further, as can be seen from comparing FIG. 7A and FIG. 7B, the health management application may be configured to adjust a score (whether a group score or otherwise) in view of an intervention or change in one or more attributes (e.g., improvements in an individual’s BMI, arm circumference, and/or waist circumference etc). As will be appreciated, the intervention or adjustment may be based on any combination of information, such as previous data (e.g., a previously known or estimated attribute, score, or biological age), a weighting factor (e.g., a weighting factor for a particular attribute), a personalized adjustment (e.g., based on a particular individual or group of individuals), or any other data.

[095] For instance, FIG. 7A may represent scores with no or small intervention, while FIG. 7B may represent scores with some or large intervention. For example, adjusted scores 710b, 720b, and 730b may be seen to be shifted to the left due to an intervention that results in a greater deviation from a reference value with respect to originally calculated scores 710a, 720a, and 730a. By way example, for the same particular individual, the difference 710a and 710b may be the result of including additional attributes for the score (e.g., one or more estimated, second attributes). The intervention or adjustment may further be the result of the individual’s actual attributes improving. For example, the difference between 720a and 720b may be the result of the individual improving their BMI, arm circumference, and waist circumference. Further, the difference between 730a and 730c may be the result of the individual improving their HDL cholesterol level. It is to be appreciated that the amount of intervention and the scores shown in FIGS. 7A and 7B are intended to be illustrative only and do not limit the present disclosure.

[096] FIG. 7C illustrates example group biological ages, consistent with embodiments of the present disclosure. FIG 7C depicts three group biological ages 710c, 720c, and 730c, each with a different mean value and a confidence interval, and which may be calculated from group scores (e.g., group scores 710a, 720a, 730a of FIG. 7A and 710b, 720b, and 730b of FIG. 7B), as discussed above. As further shown in FIG. 7C, the group biological ages may be represented as a divergence from a reference value, which may be a particular individual’s chronological age, although any other reference value may be used (e.g., a target biological age). The group biological ages may be represented as a mean value, represented as a circle, and an upper and lower value for the confidence interval, represented as two vertical lines to the right and left of the mean value. A group biological age may be represented using other mathematical representations, however, as will be appreciated by those having ordinary skill in the art upon reviewing the present disclosure. As discussed above, a group biological age may be calculated from group scores (although in some embodiments it may be calculated from a plurality of attributes directly). For example, group biological age 710c may be calculated from group score 710a of FIG. 7A or group score 710b of FIG. 7B, which as discussed above may be calculated for an individual with a known biological gender, ethnicity, chronological age, BMI, arm circumference, waist circumference, and fasting glucose levels. Next, group biological age 720c may be calculated from group score 720a of FIG. 7A or group score 720b of FIG. 7B, which as discussed above may be calculated from the same individual with additional known attributes, such as diastolic blood pressure and systolic blood pressure. As can be appreciated from comparing group biological ages 710c and 720c, as a result of the additional known attributes, the confidence interval for the group biological age may be increased as shown by the smaller gap between the confidence interval’s upper and lower values. Similarly, group biological age 730c may be calculated from group score 730a of FIG. 7A or group score 730b of FIG. 7B, which as discussed above may be calculated from the same individual with yet additional known attributes, such as HDL cholesterol levels, triglyceride levels, diastolic blood pressure, and 2-hour glucose levels. As can also be appreciated from comparing group biological ages 720c and 730c, because of these additional known attributes, the confidence interval for the group biological age may be increased further as shown by the yet smaller gap between the confidence’s upper and lower values. It is to be understood that FIGS. 7A-7C are exemplary only and are not intended to be limiting with respect to the present disclosure.

[097] In some embodiments, an individual may be given the option to compare his/her biological age to different groups and peers. Such a comparison may be defined through any combination of inputs, including groups or peers defined by age, sex, geolocation, fitness or activity level, profession (e.g., athletes), and so on. A user can also be given the option to compare their individual attributes (e.g., weight, BMI, etc) to the mean values of those in corresponding or matching groups or peers. Attributes of an individual with the largest deviations from those of group or peer members may be highlighted and displayed to a user (e.g., via a smartphone app and/or graphical user interface) and links may be provided so that the individual can receive recommendations to improve those attributes and run scenarios to see how improving certain attributes can improve their computed biological age. These and other features are further described herein.

[098] FIG. 8 illustrates an example method 800 for calculating a biological age using at least one decision tree, consistent with embodiments of the present disclosure. The example method 800 may be implemented with the aid of at least one processor (e.g., processor(s) 230 of the computing device of FIG. 2) or non- transitory computer readable medium, such as a CPU, FPGA, ASIC, or any other processing structure(s) or storage medium. In some embodiments, example method 800 may be performed by health engine 360 of FIG. 3. It is to be understood that the steps shown in FIG. 8 are exemplary only and are not intended to be exhaustive. Additional or fewer steps than those shown in FIG. 8 may be performed, depending on the specific application or purpose. [099] At step 810, the at least one processor may receive a first set of attributes associated with a particular individual (e.g., a first individual). The first set of attributes may be the same or similar as those listed above (e.g., anthropometric measurements, age, race, biological gender, blood levels, etc.). Further, the first set of attributes may be received in the same or similar manner as discussed above (e.g., through one or more API calls, databases, memories, networks, etc.).

[100] At step 820, the at least one processor may estimate, using at least one predictive model, a second set of attributes associated with the particular individual. The same or similar predictive model(s) as those described above may be used (e.g., a Bayesian network, a Principal Component Analysis (PCA) model, a decision tree, a random forest classifier, a binary classifier, a multiclass classifier, a linear classifier, a neural network, a deep neural network, a support vector machine, a Hidden Markov model, nearest neighbor model, a regression model, a clustering model, an outliers model, a classification model, a least square fitting model, a time series model, or any other machine learning or non-machine learning model).

[101] At step 830, the at least one processor may input or apply the first set of attributes and the second set of attributes to one or more decision trees. A “decision tree,” as used herein, may refer to any algorithms or models for classifying one or more inputs based on one or more predetermined rules or logic. Non-limiting examples of decision trees include classification decision trees, regression decision trees, linear discriminant classification trees, quadratic discriminant classification trees, logistic regression classification trees, classification and regression trees (CART), multiple additive regression trees (MART), prediction analysis for microarrays (PAM), random forest decision trees, nearest neighbor decision trees, or any other suitable machine learning or non-machine learning decision tree algorithms. The health management application may use one or more health-based decision trees, such as diet-based decision trees, metabolism-based decision trees, genotype-based decision trees, phenotype-based decision trees, mental healthbased decision trees, vitals-based decision trees (e.g., based on heart rate, oxygen levels, body temperature, or any other vitals), a combination thereof, or any other decision tree based on an individual’s state, whether physiological, psychological, or otherwise. In some embodiments, decision trees may be implemented to provide recommendations in specific areas or categories of health (e.g., metabolic health; cardiovascular health; immune system health; muscular or activity health; and so on) and such decision tree(s) may be selected and applied based on an individual’s health interests or requests for specific types of recommendations. Further, each decision tree’s rules or logic may be static, or they may change through time (e.g., through machine learning or manual intervention). In some embodiments, the one or more decision trees may include one or more machine learning models. As disclosed herein, the decision tree(s) may be configured to classify input attributes. Further, the one or more machine learning models may be trained based on whether following a health recommendation alters attributes in the first set of attributes and/or the second set of attributes. In some embodiments, the decision tree(s) using machine learning models may be configured to provide health recommendation(s) to improve a prioritized attribute in the first set of attributes and the second set of attributes.

[102] In some embodiments, decision tree(s) may be selected and applied as part of step 830 based on an individual’s health interests or the type or range of recommendations selected or needed by an individual (e.g., through smartphone app and/or graphical user interface). Additionally, or alternatively, specific types or categories of decision tree(s) (e.g., decision trees for metabolic health; cardiovascular health; immune system health; muscular or activity health; and so on) may be selected by the at least one processor based on an individual’s attributes and identified areas for improvement. By way of example, a determined ranking or prioritized order of attributes for a particular individual (see, e.g., FIG. 10) may be used to determine their health state and which decision tree(s) to apply for generating health recommendations to the individual.

[103] At step 840, the at least one processor may receive from the decision tree(s) a plurality of classifications for at least some of the attributes in the first set of attributes and the second set of attributes. For example, a decision tree may classify an individual or an individual’s attributes into two or more categories. In such embodiments, as a non-limiting example, a decision tree may classify an individual’s glucose level as “low,” “normal,” or “high” based on predetermined rules or logic. Similarly, the decision tree may classify the individual’s waist circumference as “low,” “normal,” or “high” based on predetermined rules or logic. The decision tree may repeat this process for other attributes. The decision tree may classify an input using other representations, however, such as numerical representations (e.g., a spectrum between 0 and 1 ), vectors, arrays, labels, or any other format, as will be appreciated by those having ordinary skill in the art upon reviewing the present disclosure.

[104] At step 850, the at least one processor may provide, based on the plurality of classifications, a recommendation for the particular individual to alter at least one of the attributes in the first set of attributes and the second set of attributes. Following the example above, for instance, the at least one processor may arrive at dietary-based recommendations based on a diet-based decision tree’s outputs. For example, a decision tree may classify an individual or an individual’s attributes by diet type, which the at least one processor may use to recommend or rank meal plans, macronutrients, hero foods, vitamins, supplements, and/or any other dietarybased information. In such embodiments, as a non-limiting example, the at least one processor may recommend a diet type based on a glucose level’s classification (e.g., a Mediterranean diet, a low carbohydrate diet, a high protein diet, etc.). Similarly, the at least one processor may recommend a diet type based on a waist circumference’s classification. The at least one processor repeat this process for other attributes, and it may apply other rules or logic to arrive at a final diet recommendation. In some embodiments, for example, the at least one processor may tally votes for each type of recommended diet, and it may output as a final recommendation the diet type having the most votes. In other embodiments, the at least one processor may weigh the results of the decision tree(s) differently, such as by applying different weighting factors to different branches (e.g., attribute-related branches may have a different weighting coefficients), by applying a prioritization order to its branches (e.g., attribute- related branches may be ranked based on an attribute prioritization order for a particular individual), by following further predetermined rules or logic (e.g., the at least one processor may provide its results to another decision tree or receive as input the results of other decision trees to arrive at a final recommendation), or by performing any other suitable processes based on the particular context or application. It will be appreciated that the steps shown in FIG. 8 are exemplary only and are not intended to be limiting or exhaustive.

[105] FIGS. 9A-9B provide a high-level illustration of an exemplary decision tree 900 for providing personalized recommendations, consistent with embodiments of the present disclosure. It will be appreciated that the high-level illustration of

FIGS. 9A-9B is a non-limiting example showing the different attributes that may be received as input or applied to the decision tree. The number and type of these attributes may be modified, as appropriate. It will also be appreciated that the decision tree may include logic, algorithms, or machine learning models (not shown in FIGS. 9A-9B) for performing operations, such as classifying input attributes and/or providing health recommendations. As will be appreciated from this disclosure, the example of FIGS 9A-9B and other decision tree(s) may be implemented by any combination of hardware, software, and/or firmware. In some embodiments, decision tree(s) are implemented as part of health engine 360 of the example system 300 of FIG. 3.

[106] As shown in FIGS. 9A-9B, the decision tree may receive as input one or more attributes associated with a particular individual. Examples of attributes for an individual include attributes such as age (901 ), gender (903), weight (905), height (907), arm circumference (909), waist circumference (911), systolic blood pressure (913), diastolic blood pressure (915), total blood pressure (917), high-density lipoprotein (HDL) cholesterol (919), low-density lipoprotein (LDL) cholesterol (921 ), total cholesterol (923), triglyceride levels (925), fast plasma glucose (FPG) levels (927), blood pressure (929), moderate muscle strength (931 ), vigorous muscle strength (933), walk distance per time unit (935), body fat percentage (937), squat strength (939), pushup strength (941), plank strength (943), sleep patterns (945), maximum rate of oxygen (VO2) (947), and physical activity patterns (949). Some of the input attributes may be missing or not provided and therefore estimated for the particular individual, as discussed above.

[107] The decision tree may be configured to perform operations such as to classify (not shown in FIGS. 9A-9B) one or more of the input attributes. For example, the decision tree may classify weight, arm circumference, and waist circumference as “low,” “normal,” or “high” based on predetermined guidelines, rules or logic. Similarly, the decision tree may classify systolic blood pressure, diastolic blood pressure, total blood pressure, high-density lipoprotein (HDL) cholesterol, low- density lipoprotein (LDL) cholesterol, total cholesterol, triglyceride levels, fast plasma glucose (FPG) levels, and other levels as “low,” “normal,” “elevated,” or “strongly elevated” based on predetermined rules or logic. Other classifications may be used, however, as will be appreciated by those having ordinary skill in the art upon reviewing the present disclosure.

[108] As disclosed herein, the health management application may be configured to receive the decision tree’s classifications and output a health-based recommendation based on the same. In some embodiments, the decision tree may be configured to classify attributes and provide the health recommendations based on the classifications and/or perform other operations. Still further, as disclosed herein, different decision trees may be implemented or organized for different types of health recommendations and associated input attributes. For example, one or more of the following decision trees may be provided: a metabolic health decision tree; a cardiovascular health decision tree; a muscle health decision tree; and/or an immune health decision tree. By way of further example, decision trees may be implemented to provide recommendations related to one or more of: metabolic health, cardiovascular health, weight management, muscle health, immune health and inflammation, weight management, mobility and joint health, liver health, mental and emotional health and performance, stress management, gastrointestinal health, vision, appearance, sleep, biorhythm, men’s health, women’s health, sexual health and function, nutrient intake, quality of life measures or other attributes. It will therefore be appreciated that the attributes shown in FIGS. 9A-9B are exemplary only, and decision tree(s) may analyze some, more, all, or none of the attributes shown in FIGS. 9A-9B.

[109] FIG. 10 illustrates an example method 1000 for using prioritized attributes with a decision tree to provide a personalized recommendation, consistent with embodiments of the present disclosure. The example method 1000 may be implemented with the aid of at least one processor (e.g., processor(s) 230 of the computing device of FIG. 2) or non-transitory computer readable medium, such as a CPU, FPGA, ASIC, or any other processing structure(s) or storage medium. In some embodiments, example method 400 may be performed by health engine 360 of FIG. 3. It is to be understood that the steps shown in FIG. 10 are exemplary only and are not intended to be exhaustive. Additional or fewer steps than those shown in FIG. 10 may be performed, depending on the specific application or purpose.

[110] At step 1010, the at least one processor may receive a first set of attributes associated with a particular individual (e.g., a first individual). The first set of attributes may be the same or similar as those listed above (e.g, anthropometric measurements, age, race, biological gender, blood levels, etc.). Further, the first set of attributes may be received in the same or similar manner as discussed above (e.g., through one or more API calls, databases, memories, networks, etc.).

[111] At step 1020, the at least one processor may estimate, using at least one predictive model, a second set of attributes for the particular individual. The estimated second set of attributes may be used to supplement the first set of attributes and/or address missing attribute data (e.g., one or more attributes related to metabolic health, cardiovascular health, weight management, muscle health, immune health and inflammation, weight management, mobility and joint health, liver health, mental and emotional health and performance, stress management, gastrointestinal health, vision, appearance, sleep, biorhythm, men’s health, women’s health, sexual health and function, nutrient intake, quality of life measures or other health-related attributes). The same or similar predictive model(s) as those described above may be used to estimate the second set of attributes (e.g., one or more Bayesian networks, Principal Component Analysis (PCA) models, decision trees, random forest classifiers, binary classifiers, multiclass classifiers, linear classifiers, neural networks, deep neural networks, support vector machines, Hidden Markov models, nearest neighbor models, regression models, clustering models, outliers models, classification models, least square fitting models, time series models, and/or any other machine learning or non-machine learning models).

[112] At step 1030, the at least one processor may prioritize one or more of the attributes in the first set of attributes and the second set of attributes. Various suitable prioritization logic or methods may be used. In some embodiments, for example, attributes may be prioritized based on their deviation from a reference value, which may include a medical guideline, a personalized recommended value, a target value, a peer group value, or any other value. Accordingly, an attribute with a higher deviation from the reference value may be deemed higher priority than an attribute with a lower deviation from the reference value. As a further example, attributes may be prioritized manually, such as through an individual’s preference, an individual’s history (e.g., previous attributes that impacted the individual’s health), a user-defined goal, an application manager-defined goal (e.g., a health coach), or any other input. In such embodiments, the attributes may be ranked manually in a preferred order to best suit a particular individual. Although not show, in some embodiments the at least one processor may display on a user device prioritized attributes to inform a user. [113] As a further example, a machine learning model may be used to learn an attribute priority or ranking. In such embodiments, attributes and/or other data related to the particular individual and/or other individuals may be used. For instance, an individual’s health history over a period of time (e.g., one week, one month, one year, or several years) may be fed to one or more neural networks (e.g., a deep neural network, a convolutional neural network, a recursive neural network, etc.), a random forest, a support vector machine, or any other suitable machine learning model, trained to analyze the effect of attributes on an individual’s health. In one embodiment, the neural networks may learn to identify patterns in the particular individual’s attributes that lead to the highest impact on the particular individual’s health. For instance, the over a period of a year, the neural networks may learn that food or macronutrients impact the particular individual’s health with disparate effects, and may prioritize the foods or macronutrients accordingly. As another example, the neural networks may learn that a particular combination of attributes impact the particular’s health significantly, such as a combination of meal, exercise, and sleep timing. In another embodiment, the neural networks may be trained to classify attribute patterns as either “positive” or “negative” for an individual’s health, where the neural networks may be trained using attribute patterns of various individuals reflecting either a positive or a negative change. For example, a first set of training patterns where the effect of one or more attributes may be beneficial may be labeled as “positive,” while a second set of training patterns where the effect of one or more attributes may be detrimental may be labeled as “negative.” Other labeling conventions could be used, however, as will be appreciated by those having ordinary skill in the art. Weights or other parameters of the neural network may be adjusted based on its output with respect to a third, non-labeled set of training patterns based on whether the neural networks predict the outcome of the pattern to be “positive” or “negative,” and the process may be repeated with additional training patterns or with live data. The trained neural networks may be applied to monitor the attribute patterns of a particular individual, so as to arrive at an attribute priority. The examples provided herein for prioritizing attributes are not intended to be exhaustive and are intended to be illustrative only.

[114] At step 1040, the at least one processor may input or apply the first set of attributes and the second set of attributes to at least one decision tree. The decision tree(s) may be the same or similar to those discussed above (e.g., classification decision trees, regression decision trees, linear discriminant classification trees, quadratic discriminant classification trees, logistic regression classification trees, classification and regression trees (CART), multiple additive regression trees (MART), prediction analysis for microarrays (PAM), random forest decision trees, nearest neighbor decision trees, or any other suitable machine learning or non-machine learning decision tree algorithms). In some embodiments, as part of step 1040, one or more decision tree(s) may be selected and applied based on an individual’s health interests or the type or range of recommendations selected or needed by an individual (e.g., through smartphone app and/or graphical user interface). Additionally, or alternatively, specific types or categories of decision tree(s) (e.g., decision trees for metabolic health; cardiovascular health; immune system health; muscular or activity health; and so on) may be selected by the at least one processor based on an individual’s attributes and identified areas for improvement. By way of example, the prioritized order of attributes for a particular individual determined as part of step 1030 may be used to select and apply one or more specific decision tree(s) (e.g., a metabolic health decision tree, a cardiovascular health decision tree; and so on) to the first and second sets of attributes for generating health recommendations to the individual.

[115] In some embodiments, the at least one processor is configured to provide a health recommendation to improve a prioritized attribute among the first set of attributes and the second set of attributes. Further, the at least one processor may be configured to select at least one of a plurality of decision trees based on the prioritized attribute(s) to provide the health recommendation for the individual, wherein each of the plurality of decision trees relates to a different area of health. Examples of decision trees have been provided herein. To further illustrate, decision trees may be provided for metabolic health, cardiovascular health, weight management, muscle health, immune health, nutrient intake, quality of life measures, male or female-based health, psychological health, and/or gastro-intestinal health. These are non-limiting examples and other types of decision trees may be provided.

[116] At step 1050, the at least one processor may receive from the decision tree(s) a plurality of classifications for the first set of attributes and the second set of attributes. The plurality of classifications may be the same or similar as those described above (e.g., classifications of “low,” “normal,” “high,” “elevated,” and “strongly elevated,” among others).

[117] At step 1060, the at least one processor may provide, based on the plurality of classifications, at least one recommendation to alter the prioritized at least one attribute in the first set of attributes and the second set of attributes. In some embodiments, for example, outputs of the decision tree(s) (or other logic or algorithm) may be weighed differently based on the attribute priority. Following the diet-based example described above, for instance, the at least one processor may arrive at one or more dietary-based recommendations based on a diet-based decision tree’s outputs so as to alter a high priority attribute, such as by applying different weighting factors to different branches (e.g., attribute-related branches may have a different weighting coefficients), by applying a prioritization order to its branches (e.g., attribute-related branches may be ranked based on an attribute prioritization order for a particular individual), by following further predetermined rules or logic (e.g., using specific decision trees in view of prioritized attributes), or by performing another suitable process based on the particular context or application. Instead of tallying votes for each type of recommended diet, for example, the at least one processor may weigh particular classifications more strongly based on the attribute priority, such as by weighing “strongly elevated” classifications higher than “elevated” or “low” classifications, although any other priority order may be used as discussed above. It will be appreciated that the steps shown in FIG. 10 are exemplary only and are not intended to be limiting or exhaustive.

[118] FIG. 11 illustrates an example method 1 100 for providing a personalized recommendation with personalized attribute weights, consistent with embodiments of the present disclosure. The example method 1100 may be implemented with the aid of at least one processor (e.g., processor(s) 230 of the computing device of FIG. 2) or non-transitory computer readable medium, such as a CPU, FPGA, ASIC, or any other processing structure(s) or storage medium. In some embodiments, example method 1100 may be performed by health engine 360 of FIG. 3. It is to be understood that the steps shown in FIG. 11 are exemplary only and are not intended to be exhaustive. Additional or fewer steps than those shown in FIG. 11 may be performed, depending on the specific application or purpose.

[119] At step 11 10, the at least one processor may receive a plurality of attributes associated with a plurality of individuals, the plurality of attributes being associated with a plurality of sample weights. The plurality of attributes associated with a plurality of individuals may be the same or similar as those listed above (e.g., anthropometric measurements, age, race, biological gender, blood levels, etc.). Further, the plurality of attributes associated with a plurality of individuals may be received in the same or similar manner as discussed above (e.g., through one or more API calls, databases, memories, networks, etc.).

[120] The plurality of attributes may be associated with a plurality of sample weights. A “sample weight,’’ as used herein, may refer to a value representing a significance of an attribute with respect to one or more attributes, and which may serve as reference from which a personalized weight for a particular individual may be calculated. A sample weight may be calculated or received by the at least one processor. In some embodiments, for example, the at least one processor may determine a weight based on a degree of confidence that an attribute is correct, whether known, artificial, or estimated. For example, the at least one processor may assign a higher weight to an artificial or estimated attribute with a higher degree of confidence, while it may assign a lower weight to an artificial or estimated attribute with a lower degree of confidence. Similarly, the at least one processor may determine that a known attribute for an individual is inaccurate, which could be as a result of time, changes in health conditions, a typographical error, or any other reason, and it may accordingly assign a lower weight to the attribute. In other embodiments, the at least one processor may receive a plurality of attributes from a database or another source, along with values that may correspond to the significance of attributes in the plurality of attributes. For instance, it may have been previously determined that certain attributes impact individuals belonging to a certain group more significantly than other attributes, such as individuals having a certain age, gender, ethnicity, BM I , health status, or any other characteristic. For example, it may be determined that cholesterol levels may be a more significant attribute for individuals above a certain age, while it may be a less significant attribute for individuals below that certain age. It will be appreciated that other methods of determining a sample weight may be employed and the examples provided herein are illustrative only.

[121] At step 1120, the at least one processor may receive a set of attributes associated with a particular individual (e.g., a first individual). The set of attributes associated with the particular individual may the same or similar as those listed above. In some embodiments, at least some of the attributes in the set of attributes may be estimated using at least one predictive model, as discussed above. The first set of attributes of the particular individual may be received in the same or similar manner as the plurality of attributes associated with a plurality of individuals.

[122] At step 1130, the at least one processor may generate, using the plurality of sample weights, a plurality of personalized weights associated with the set of attributes associated with the particular individual. The plurality of personalized weights for a particular individual may be calculated in the same or similar manner as the plurality of sample weights, such as based on confidence, information associated with the particular individual, peers, or groups, or any other data. For example, in embodiments where attributes for a particular individual are estimated from a plurality of artificial attributes, the confidence of the estimated attribute may be used to determine the personalized weight, wherein an estimated attribute having a higher confidence may be assigned a higher personalized weight while an estimated attribute having a lower confidence may be assigned a lower personalized weight. As a further example, it may be determined that an attribute has a greater effect on a group to which the particular individual belongs, and it may accordingly be assigned a greater personalized weight. Conversely, it may be determined that an attribute has a lower effect on the group to which the particular individual belongs, and it may accordingly be assigned a lower personalized weight. As a further example, as disclosed above, a machine learning model may be employed to learn the effect of attributes for a particular individual, and the at least one processor may accordingly assign a higher personalized weight to such attributes. It will be appreciated that other ways of calculating a personalized weights may be employed, and the examples provided herein are illustrative only.

[123] At step 1140, the at least one processor may generate, using the plurality of personalized weights, a priority order for the set of attributes associated with the particular individual. In some embodiments, for example, the priority order may rank the attributes based on their weight, such as by prioritizing attributes with a higher personalized weight over attributes with a lower personalized weight. In other embodiments, however, additional information may be used, such as by taking into account attributes that previously impacted the particular user’s health, prioritization orders for peers, or any other data.

[124] At step 1150, the at least one processor may provide, based on the priority order, a recommendation to alter a highest priority attribute from the set of attributes associated with the particular individual. The recommendation may be determined in the same or similar manner as described above (e.g., by using decision trees, machine learning models, non-machine learning models, or through another suitable approach). It will be appreciated that the steps shown in FIG. 11 are exemplary only and are not intended to be limiting or exhaustive. [125] FIG. 12 illustrates an example method 1200 for providing a personalized recommendation to lower a biological age calculated using personalized attribute weights, consistent with embodiments of the present disclosure. The example method 1200 may be implemented with the aid of at least one processor (e.g., processor(s) 230 of the computing device of FIG. 2) or non- transitory computer readable medium, such as a CPU, FPGA, ASIC, or any other processing structure(s) or storage medium. In some embodiments, example method 1200 may be performed by health engine 360 of FIG. 3. It is to be understood that the steps shown in FIG. 12 are exemplary only and are not intended to be exhaustive. Additional or fewer steps than those shown in FIG. 12 may be performed, depending on the specific application or purpose.

[126] At step 1210, the at least one processor may receive a plurality of attributes associated with a plurality of individuals, the plurality of attributes being associated with a plurality of sample weights. The plurality of attributes associated with a plurality of individuals may be the same or similar as those listed above (e.g., anthropometric measurements, age, race, biological gender, blood levels, etc.). The sample weights may be determined and received in the same or similar manner as discussed above (e.g., based on confidence, information associated with the particular individual, peers, or groups, or any other data). Further, the plurality of attributes associated with a plurality of individuals may be received in the same or similar manner as discussed above (e.g., through one or more API calls, databases, memories, networks, etc.).

[127] At step 1220, the at least one processor may receive a first set of attributes associated with a particular individual (e.g., a first individual). The first set of attributes associated with the particular individual may the same or similar as those listed above. The first set of attributes of the particular individual may be received in the same or similar manner as the plurality of attributes associated with a plurality of individuals.

[128] At step 1230, the at least one processor may estimate a second set of attributes associated with the particular individual. The same or similar approach as discussed may be using, such as using predictive model(s) (e.g., one or more Bayesian networks, Principal Component Analysis (PCA) models, decision trees, random forest classifiers, binary classifiers, multiclass classifiers, linear classifiers, neural networks, deep neural networks, support vector machines, Hidden Markov models, nearest neighbor models, regression models, clustering models, outliers models, classification models, least square fitting models, time series models, and/or any other machine learning or non-machine learning models).

[129] At step 1240, the at least one processor may generate, using the plurality of sample weights, a plurality of personalized weights associated with the first set of attributes and the second set of attributes. The plurality of personalized weights may be determined and received in the same or similar manner as described above (e.g., based on confidence, information associated with the particular individual, peers, or groups, or any other data).

[130] At step 1250, the at least one processor may calculate plurality of confidence scores for the second set of attributes. A “confidence score,” as used herein, may be a value representing a degree of certainty that an attribute is accurate. A confidence score may be calculated, for example, based on confidence intervals for estimated or artificial attributes, as discussed above. In some embodiments, a confidence score may be normalized or otherwise manipulated to arrive at a more meaningful value based on the particular context or application. [131] At step 1260, the at least one processor may calculate an average score using the plurality of confidence scores and the plurality of personalized weights. The average score may be calculated in the same or similar manner as described above in connection with scores, such as by calculating scores for attributes as a deviation from a reference value (e.g., a medical guidance, a target value, a personalized value, or any other value). Various mathematical approaches may be used to arrive at the average score. For example, the average score may be an average of calculated scores multiplied by a corresponding confidence score and a personalized weight. The scores may be normalized or otherwise manipulated prior to averaging in order to arrive at a more meaningful representation. Other mathematical representations other than averaging may be used however (e.g., calculating a mean, standard deviation, etc.), as will be appreciated by those having ordinary skill in the art upon reviewing the present disclosure.

[132] At step 1270, the at least one processor may calculate a biological age using the average score. In some embodiments, the biological age may be calculated directly from the average score. For example, in embodiments where the average score represents an average deviation from reference values, the biological age may be equal to a chronological age plus or minus the average score (e.g., -5, - 4, -3, -2, 0, +1 , +2, +3, +4, +5 years). In other embodiments, the biological age may be calculated using a separate method, such as the ones described above (e.g., using weighting coefficients and scores for individual attributes, or any other suitable approach). In such embodiments, the calculated biological age may be adjusted by the average score, such as by adding or subtracting an amount based on the average score. [133] At step 1280, the at least one processor may generate, using the plurality of personalized weights, a priority order for the first set of attributes and the second set of attributes. The priority order may be generated in the same or similar manner as described above (e.g., the priority order may rank the attributes based on their weight, by taking into account attributes that previously impacted the particular user’s health, prioritization orders for peers, or any other data).

[134] At step 1290, the at least one processor may provide, based on the priority order, a recommendation to reduce the user’s biological age. The recommendation may be determined in the same or similar manner as described above (e.g., by using decision trees, machine learning models, non-machine learning models, or through any other suitable approach). It will be appreciated that the steps shown in FIG. 12 are exemplary only and are not intended to be limiting or exhaustive.

[135] FIGS. 13A-13Q illustrate example graphical user interfaces for a health management application, consistent with embodiments of the present disclosure. The example user interfaces may be generated and presented with the aid of at least one processor (e.g., processor(s) 230 of the computing device of FIG. 2) or non- transitory computer readable medium, such as a CPU, FPGA, ASIC, or any other processing structure(s) or storage medium. Further, the example user interfaces may be displayed to a user via a display screen of a user device (e.g., a smartphone, a laptop, or computer) or other suitable I/O component thereof. It is to be understood that the user interfaces shown in FIGS. 13A-13Q are exemplary only and are not intended to be exhaustive. Additional or fewer graphical user interfaces than those shown in FIGS. 13A-13Q may be generated and presented, depending on the specific application or purpose. [136] FIG. 13A illustrates an example welcome screen for a new user, consistent with embodiments of the present disclosure. As shown in FIG. 13A, a welcome screen may prompt a user to “Create Account” or “Continue as Guest.” Upon creating an account or continuing as a guest, a user may continue to access the functionality of the health management application, as described herein.

[137] FIGS. 13B-13I illustrate example onboarding questions for obtaining attributes associated with the user, consistent with embodiments of the present disclosure. As shown in FIGS. 13B-13I, a user may be prompted to input data that may be used as attributes to determine a biological age, recommendations, and/or other information as disclosed above. Non-limiting examples of onboarding questions may include health interests (FIG. 13B), biological gender (FIG. 13C), height (FIG. 13D), weight (FIG. 13E), waist circumference (FIG. 13F), arm circumference (FIG. 13G), ethnicity (FIG. 13H), and chronological age (FIG. 131). It will be appreciated that the exemplary onboarding questions presented herein are illustrative only and are not intended to be exhaustive, and all, some, more, or none of the onboarding questions in FIGS. 13B-13I may be used depending on the particular application or context.

[138] FIG. 13J illustrates an example display for presenting a biological age calculated using attributes associated with the user, consistent with embodiments of the present disclosure. As shown in FIG. 13J, a biological age may be presented as an age (e.g., “40” years), as a deviation from a chronological age or other reference value (e.g., “38.5” years and “-5” or “+5” years), or any other suitable representation. As also shown in FIG. 13J, a certainty or confidence for the biological age may be provided (e.g., “Accuracy is low”), which upon clicking may provide additional information for the user explaining the certainty or confidence value and/or how to improve it (e.g., by providing additional attributes). Further, as shown in FIG. 13J, a “Simulate Changes” button may allow a user to enter one or more hypothetical attributes (e.g., losing 10 pounds) or other scenarios, which may be used to recalculate the biological age in view of the hypothetical attributes or scenarios (e.g., showing how a user’s computed biological age may be reduced by a few years).

[139] FIG. 13K illustrates an example scenario planning to decrease a biological age, consistent with embodiments of the present disclosure. As shown in FIG. 13K, a scenario planning prompt may be presented to the user to enter a hypothetical attribute (e.g., “Lose 15 pounds”) or any other scenario, which may be used to recalculate a biological age to be presented to the user. In this manner, the user may be motivated to reach a user-defined goal by viewing quantifiable changes to his or her biological age.

[140] FIG. 13L illustrates an example personalized recommendation strength for a user, consistent with embodiments of the present disclosure. As shown in FIG. 13L, a recommendation strength may be presented as a category (e.g., “Poor,” “Good,” “Excellent”), although any other representation may be used (e.g., a score out of 10, a circle or other shape that becomes fuller with higher recommendation strength, etc.). As also shown in FIG. 13L, the user may be prompted to information to increase the recommendation strength. Although shown in FIG. 13L as a health interest questionnaire, it will be appreciated that any other information may be prompted and used to increase a recommendation strength, such as attributes, medical history, genetic data, lifestyle data, social connections, or any other data.

[141] FIGS. 13M-13P illustrate example personalized recommendations, consistent with embodiments of the present disclosure. As shown in FIGS. 13M-

13P, various health-related recommendations may be provided, such as dietary patterns and/or physical activities (FIG. 13M), articles (FIG. 13N), daily actions (FIG. 130), and supplements (FIG. 13P). It will be appreciated that other types of healthbased recommendations may be determined and provided, and the examples presented herein are illustrative only.

[142] FIG. 13Q illustrates example user information used by a health management application, consistent with embodiments of the present disclosure. As shown in FIG. 13Q, a user may view and perform an action with respect to user information used by the health management application (e.g., learn more, add, revoke, etc.). Data used by the health management application and shared by a user may include physical activity, heart rate, nutrition, sleep, weight, profile information, and any other information as the situation may be. It will be appreciated that the user information presented herein is provided for illustration purposes only and is not intended to be exhaustive.

[143] The diagrams and components in the figures/drawings described above illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer hardware or software products according to various example embodiments of the present disclosure. For example, each block in a flowchart or diagram may represent a module, segment, or portion of code, which includes one or more executable instructions for implementing the specified logical functions. It should also be understood that in some alternative implementations, functions indicated in a block may occur out of order noted in the figures/drawings. By way of example, two blocks or steps shown in succession may be executed or implemented substantially concurrently, or two blocks or steps may sometimes be executed in reverse order, depending upon the functionality involved.

Furthermore, some blocks or steps may be omitted. It should also be understood that each block or step of the diagrams, and combination of the blocks or steps, may be implemented by special purpose hardware-based systems that perform the specified functions or acts, or by combinations of special purpose hardware and computer instructions. Computer program products (e.g., software or program instructions) may also be implemented based on the described embodiments and illustrated examples.

[144] It should be appreciated that the above-described systems and methods may be varied in many ways and that different features may be combined in different ways. In particular, not all the features shown above in a particular embodiment or implementation are necessary in every embodiment or implementation. Further combinations of the above features and implementations are also considered to be within the scope of the herein disclosed embodiments or implementations.

[145] While certain embodiments and features of implementations have been described and illustrated herein, modifications, substitutions, changes and equivalents will be apparent to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes that fall within the scope of the disclosed embodiments and features of the illustrated implementations. It should also be understood that the herein described embodiments have been presented by way of example only, not limitation, and various changes in form and details may be made. Any portion of the systems and/or methods described herein may be implemented in any combination, except mutually exclusive combinations. By way of example, the implementations described herein can include various combinations and/or sub-combinations of the functions, components and/or features of the different embodiments described. [146] Moreover, while illustrative embodiments have been described herein, the scope of the present disclosure includes any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of aspects across various embodiments), adaptations or alterations based on the embodiments disclosed herein. Further, elements in the claims are to be interpreted broadly based on the language employed in the claims and not limited to examples described herein or during the prosecution of the present application. Instead, these examples are to be construed as non-exclusive. It is intended, therefore, that the specification and examples herein be considered as exemplary only, with a true scope and spirit being indicated by the following claims and their full scope of equivalents.