SYSTEM AND METHOD FOR ESTABLISHING CHANNELIZED COMMUNICATIONS AND RESOURCE MANAGEMENT

RELATED APPLICATIONS

[0001] Under provisions of 35 U.S.C. §119(e), the Applicant claims benefit of U.S. Provisional Application No. 63/390,546 filed on July 19, 2022, and having inventors in common, which is incorporated herein by reference in its entirety.

[0002] This patent application is related to U.S. Application No. 18/154,426 filed on January 13, 2023, which claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Application No. 63/390,546 filed on July 19, 2022, entitled "SYSTEM AND METHOD FOR ESTABLISHING CHANNELIZED COMMUNICATIONS AND RESOURCE MANAGEMENT", and having inventors in common, which are incorporated herein by reference in its entirety.

[0003] This patent application is related to U.S. Application No. 18/154,534 filed on January 13, 2023, which claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Application No. 63/390,546 filed on July 19, 2022, entitled "AI-BASED SYSTEM AND METHOD FOR ESTABLISHING CHANNELIZED COMMUNICATIONS", and having inventors in common, which are incorporated herein by reference in its entirety.

[0004] This patent application is related to U.S. Application No. 18/154,737 filed on January 13, 2023, which claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Application No. 63/390,546 filed on July 19, 2022, entitled "SYSTEM AND METHODS FOR ESTABLISHING AND RENDERING CHANNELIZED COMMUNICATION MODEL", and having inventors in common, which are incorporated herein by reference in its entirety.

[0005] This patent application is related to U.S. Application No. 18/154,780 filed on January 13, 2023, which claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Application No. 63/390,546 filed on July 19, 2022, entitled "AI-BASED SYSTEM AND METHOD FOR PREDICTION OF MEDICAL DIAGNOSIS", and having inventors in common, which are incorporated herein by reference in its entirety.

[0006] It is intended that the referenced application maybe applicable to the concepts and embodiments disclosed herein, even if such concepts and embodiments are disclosed in the referenced application with different limitations and configurations and described using different examples and terminology. FIELD OF DISCLOSURE

[0007] The present disclosure generally relates to intercorporate communications, and more particularly, to an intelligent automated system for establishing channelized communications within an organization. The present disclosure is also related to an intelligent, automated system for establishing a channelized communication model of an organization and rendering channelized communication-related data to members of the organization. The present disclosure also relates to health organizations communications, and more particularly, to an intelligent automated system for predicting a medical diagnosis over channelized communications.

BACKGROUND

[0008] There have been two waves of modern Corporate Communications. The first wave was the adoption of email and its wildly successful proliferation. However, in larger organizations with more than a dozen members, email has failed due to an inherent flaw in its model. Every user’s inbox within the organization is public in a sense that any other user can send them an email, relevant or not. There have been numerous user interface tools built into various email programs, e.g., conversation modes, filters, and hardware devices designed to stop unwanted emails. These measures have all been unsuccessful, as nearly 85% of all email is now considered spam.

[0009] In dissecting human communication, there exists two components: the individual and the subject matter. An analysis of the communication model for email points to the individual as the pivot in this model. This means that email relies entirely on the individual sender to properly identify who to send emails to. In a team environment, the individual has to first assess the content of the message, and based on some real or virtual individual/function mapping table, route that message to the correct individuals in the team. While it challenging for small teams, efficient routing becomes virtually impossible as the size of the organization grows. This failure is due to the inherent shortcoming or flaw in the email model - i.e., dependency on the individuals to properly and promptly route the messages to the appropriate recipients. Using the email model, as we scale the size of the organization, the exponential rise of noise to information ratio will eventually paralyze the network and render it useless or ineffective. [0010] The second wave of Corporate Communications was an adoption based on social media tools such as Facebook. Similar group communication systems have emerged for the corporate market such as Slack, Yammer or MS Teams. In these new models, the new pivot has now shifted to the subject matter from the individual. A manager creates communication channels with specific subject lines in advance, and individuals can then decide whether to subscribe to these channels based on their relevance to their functions or needs within the organization. This allows the receivers to choose which types of messages or communications they wish to receive. This constitutes a tremendous improvement over the email model since the recipient is given some level of control over the types of messages received. This model is superior to the email model and is somewhat effective when not only the size of the organization is small, but the team’s workflow is extremely simple. The challenge here is the process of finding a proper model for communication channel creation. However, creation of the communication channel turns out to be an impossible task in modern organizations for the following reasons. Since modern teams are organized along a production line or workflow, the relevant discussions of the team members have to be focused and synchronized generally with the workflow and specifically with the objects that move around within the workflow. Representing multi-dimensional complex workflows using a series of somewhat arbitrarily named chat channels is an impossible demand on this model. The problem is exacerbated when individuals can create channels at will, and pretty soon the organization can end up with overlapping channels and/or an overwhelming number of channels. In other words, even if the subject is the new pivot, the individual still plays a significant role in creating the communication channels. Accordingly, the chat-based model fails to associate the workflow with human communications in an effective manner.

[0011] Making a correct medical diagnosis based on patient’s medical test data has been a goal of medical practitioners for a long time. However, more often than not, a doctor needs to consult with colleagues, order additional tests, even administer some medications before arriving to an accurate diagnosis. This process takes time and is often inaccurate - i.e., wrong assumptions and guesses are made by the doctor and wrong treatment is administered to a patient. While making a diagnosis, the doctor cannot efficiently consider massive amounts of data available from patients of the same age, gender, race, etc. having the same or similar symptoms. Additionally, the doctor has to rely on opinions of other practitioners such as radiologist, CT scan and MRI specialists, pathologists, lab technicians, etc. It is rather difficult for the doctor to efficiently process all this information and arrive to a correct diagnosis in a short time period.

[0012] Accordingly, intelligent and/or Al based automated systems and methods for establishing effective channelized communications within an organization are desired. Moreover, intelligent automated systems and methods for establishing channelized communication model of an organization and rendering channelized communication- related data to members of the organization are desired. An Al -based automated system and method for prediction of a medical diagnosis over channelized communications within healthcare organization is also desired.

BRIEF OVERVIEW

[0013] This brief overview is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This brief overview is not intended to identify key features or essential features of the claimed subject matter. Nor is this brief overview intended to be used to limit the claimed subject matter’s scope.

[0014] One embodiment of the present disclosure provides a system for establishing channelized communications and resource management for an organization. The system includes: a processor of a channel server node connected to an organization network; a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: monitor the at least one organization network to collect analytics data, process the analytics data to identify nodes and associated pivot objects of the at least one organization network, query workflow-related communication records of the at least one organization network to retrieve workflow communication data, and generate at least one communication channel configured to connect the nodes and the associated pivot objects based on the workflow communication data.

[0015] Another embodiment of the present disclosure provides a method that includes one or more of: monitoring at least one organization network to collect analytics data, processing the analytics data to identify nodes and associated pivot objects of the at least one organization network, querying workflow-related communication records of the at least one organization network to retrieve workflow communication data, and generating at least one communication channel configured to connect the nodes and the associated pivot objects based on the workflow communication data.

[0016] Another embodiment of the present disclosure provides a computer-readable medium including instructions for monitoring at least one organization network to collect analytics data, processing the analytics data to identify nodes and associated pivot objects of the at least one organization network, querying workflow-related communication records of the at least one organization network to retrieve workflow communication data, and generating at least one communication channel configured to connect the nodes and the associated pivot objects based on the workflow communication data.

[0017] In yet another embodiment of the present disclosure provides an Al-based system for establishing channelized communications and resource management for an organization. The system includes: a processor of a channel server node connected to an organization network; a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: monitor the at least one organization network to collect analytics data comprising workflow data, generate a plurality of classifiers based on the analytics data, the plurality of classifiers comprising data reflecting nodes and associated pivot objects of the at least one organization network, feed the plurality of classifiers to an AI/ML module configured to output configuration parameters for at least one communication channel; and generate the at least one communication channel for the at least one organization network based on the configuration parameters.

[0018] A fifth embodiment of the present disclosure provides a method that includes one or more of: monitoring the at least one organization network to collect analytics data comprising workflow data; generating a plurality of classifiers based on the analytics data; the plurality of classifiers comprising data reflecting nodes and associated pivot objects of the at least one organization network; feeding the plurality of classifiers to an AI/ML module configured to output configuration parameters for at least one communication channel; and generating the at least one communication channel for the at least one organization network based on the configuration parameters.

[0019] Still another embodiment of the present disclosure provides a computer- readable medium including instructions for monitoring the at least one organization network to collect analytics data comprising workflow data; generating a plurality of classifiers based on the analytics data; the plurality of classifiers comprising data reflecting nodes and associated pivot objects of the at least one organization network; feeding the plurality of classifiers to an Al /ML module configured to output configuration parameters for at least one communication channel; and generating the at least one communication channel for the at least one organization network based on the configuration parameters.

[0020] Another embodiment of the present disclosure provides a system for establishing channelized communication model of an organization and rendering channelized communication-related data to members of the organization. The system includes: a processor of a channel server node connected to an organization network including a plurality of organization member nodes having UIs; a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: receive user credentials from an organization member node, identify at least one object associated with the user, extract properties from the at least one object and render the properties to the user via a UI of the organization member node, retrieve channel data of at least one channel associated with the object and render the channel data via the UI, and automatically render channel communications to the user via the UI. [0021] Yet another embodiment of the present disclosure provides a method that includes one or more of: receiving user credentials from an organization member node, identifying at least one object associated with the user, extracting properties from the at least one object and render the properties to the user via a UI of the organization member node, retrieving channel data of at least one channel associated with the object and render the channel data via the UI, and automatically rendering channel communications to the user via the UI.

[0022] A ninth embodiment of the present disclosure provides a computer-readable medium including instructions for receiving user credentials from an organization member node, identifying at least one object associated with the user, extracting properties from the at least one object and render the properties to the user via a UI of the organization member node, retrieving channel data ofat least one channel associated with the object and render the channel data via the UI, and automatically rendering channel communications to the user via the UI.

[0023] Another embodiment of the present disclosure provides an Al-based system for a medical diagnosis. The system includes a processor of a diagnosis server node connected to at least one health organization network including provider nodes and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: identify at least one patient object associated with a patient, monitor the at least one patient object to collect patient’s data, access a communication channel associated with the at least one patient object to extract communications’ data of the provider nodes, generate a plurality of classifiers based on the collected patient’s data and the extracted communications’ data, feed the plurality of classifiers into an AI/ML module configured to output patient diagnosis-related data into the communication channel, and update the patient object based on the diagnosis-related data.

[0024] Still another embodiment of the present disclosure provides a method that includes one or more of: identifying at least one patient object associated with a patient, monitoring the at least one patient object to collect patient’s data, accessing a communication channel associated with the at least one patient object to extract communications’ data of provider nodes, generating a plurality of classifiers based on the collected patient’s data and the extracted communications’ data, feeding the plurality of classifiers into an Al /ML module configured to output patient diagnosis-related data into the communication channel, and updating the patient object based on the diagnosis- related data.

[0025] Yet another embodiment of the present disclosure provides a computer- readable medium including instructions for identifying at least one patient object associated with a patient, monitoring the at least one patient object to collect patient’s data, accessing a communication channel associated with the at least one patient object to extract communications’ data of provider nodes, generating a plurality of classifiers based on the collected patient’s data and the extracted communications’ data, feeding the plurality of classifiers into an AI/ML module configured to output patient diagnosis- related data into the communication channel, and updating the patient object based on the diagnosis-related data.

[0026] Both the foregoing brief overview and the following detailed description provide examples and are explanatory only. Accordingly, the foregoing brief overview and the following detailed description should not be considered to be restrictive. Further, features or variations may be provided in addition to those set forth herein. For example, embodiments may be directed to various feature combinations and sub-combinations described in the detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present disclosure. The drawings contain representations of various trademarks and copyrights owned by the Applicant. In addition, the drawings may contain other marks owned by third parties and are being used for illustrative purposes only. All rights to various trademarks and copyrights represented herein, except those belonging to their respective owners, are vested in and the property of the Applicant. The Applicant retains and reserves all rights in its trademarks and copyrights included herein, and grants permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose.

[0028] Furthermore, the drawings may contain text or captions that may explain certain embodiments of the present disclosure. This text is included for illustrative, nonlimiting, explanatory purposes of certain embodiments detailed in the present disclosure. In the drawings:

[0029] FIG. 1 illustrates a network diagram of a system for intelligent automated monitoring of communications withing an organization consistent with the present disclosure;

[0030] FIG. IB illustrates a network diagram of an intelligent automated system for establishing effective channelized communications withing an organization, consistent with the present disclosure;

[0031] FIG. 1C illustrates network diagram of an Al-based automated system for establishing effective channelized communications withing an organization, consistent with the present disclosure;

[0032] FIG. 2 A illustrates a network diagram of a system including detailed features of a channel server node consistent with the present disclosure;

[0033] FIG. 2B illustrates another network diagram of a system including detailed features of a channel server node consistent with the present disclosure;

[0034] FIG. 2C illustrates yet another network diagram of a system including detailed features of a channel server node consistent with the present disclosure; [0035] FIG. 3 A illustrates a flowchart of a method for establishing effective channelized communications within an organization consistent with the present disclosure;

[0036] FIG. 3B illustrates a further flow chart of a method establishing effective channelized communications withing an organization consistent with the present disclosure;

[0037] FIG. 4A illustrates a flowchart of a method for establishing effective channelized communications within an organization consistent based on Al predictive modeling consistent with the present disclosure;

[0038] FIG. 4B illustrates a further flow chart of a method establishing effective channelized communications withing an organization consistent with the present disclosure;

[0039] FIG. 5 illustrates deployment of a machine learning model for predictive configuration of channelized communications based on blockchain assets consistent with the present disclosure;

[0040] FIG. 6 A illustrates a flowchart of a method for establishing channelized communication model of an organization and rendering channelized communication- related data to members of the organization consistent with the present disclosure;

[0041] FIG. 6B illustrates a further flow chart of a method for establishing channelized communication model of an organization and rendering channelized communication- related data to members of the organization consistent with the present disclosure;

[0042] FIG. 7A illustrates a network diagram of an Al-based automated medical diagnosis prediction system, consistent with the present disclosure;

[0043] FIG. 7B illustrates a network diagram of an Al-based automated medical diagnosis prediction system implemented over a blockchain network, consistent with the present disclosure;

[0044] FIG. 7C illustrates a network diagram of an Al-based automated medical diagnosis prediction system within channelized communications of a healthcare organization, consistent with the present disclosure;

[0045] FIG. 8 illustrates a network diagram of a system including detailed features of a diagnosis server node consistent with the present disclosure;

[0046] FIG. A illustrates a flowchart of a method for Al-based prediction of medical diagnosis consistent with the present disclosure; [0047] FIG. 9B illustrates a further flow chart of a method for Al -based prediction of medical diagnosis consistent with the present disclosure;

[0048] FIG. 10 illustrates deployment of a machine learning model for predictive medical diagnosis within channelized communications based on blockchain assets consistent with the present disclosure;

[0049] FIG. 11 illustrates a user interface displaying channel data for different patients including doctor name and diagnosis consistent with the present disclosure;

[0050] FIG. 12 illustrates a user interface displaying selected patient information consistent with the present disclosure;

[0051] FIG. 13 illustrates a user interface displaying medical images of the selected patient consistent with the present disclosure;

[0052] FIG. 14 illustrates a user interface displaying larger patient images with annotations consistent with the present disclosure;

[0053] FIG. 15 illustrates a user interface displaying patient channel communications among the doctors and the Al Bot., consistent with the present disclosure;

[0054] FIG. 16 illustrates a user interface displaying treatment recommendation provided by the Al Bot connected to the AI/ML module consistent with the present disclosure, and

[0055] FIG. 17 illustrates a block diagram of a system including a computing device for performing the methods of FIGs. 3A, 3B, 4A, 4B, 6A, 6B, 9A, and/or 9B.

DETAILED DESCRIPTION

[0056] As a preliminary matter, it will readily be understood by one having ordinary skill in the relevant art that the present disclosure has broad utility and application. As should be understood, any embodiment may incorporate only one or a plurality of the above-disclosed aspects of the disclosure and may further incorporate only one or a plurality of the above-disclosed features. Furthermore, any embodiment discussed and identified as being "preferred” is considered to be part of a best mode contemplated for carrying out the embodiments of the present disclosure. Other embodiments also may be discussed for additional illustrative purposes in providing a full and enabling disclosure. Moreover, many embodiments, such as adaptations, variations, modifications, and equivalent arrangements, will be implicitly disclosed by the embodiments described herein and fall within the scope of the present disclosure. [0057] Accordingly, while embodiments are described herein in detail in relation to one or more embodiments, it is to be understood that this disclosure is illustrative and exemplary of the present disclosure and are made merely for the purposes of providing a full and enabling disclosure. The detailed disclosure herein of one or more embodiments is not intended, nor is to be construed, to limit the scope of patent protection afforded in any claim of a patent issuing here from, which scope is to be defined by the claims and the equivalents thereof. It is not intended that the scope of patent protection be defined by reading into any claim a limitation found herein that does not explicitly appear in the claim itself.

[0058] Thus, for example, any sequencers ) and/or temporal order of steps of various processes or methods that are described herein are illustrative and not restrictive. Accordingly, it should be understood that, although steps of various processes or methods may be shown and described as being in a sequence or temporal order, the steps of any such processes or methods are not limited to being carried out in any particular sequence or order, absent an indication otherwise. Indeed, the steps in such processes or methods generally may be carried out in various different sequences and orders while still falling within the scope of the present invention. Accordingly, it is intended that the scope of patent protection is to be defined by the issued claim(s) rather than the description set forth herein.

[0059] Additionally, it is important to note that each term used herein refers to that which an ordinary artisan would understand such term to mean based on the contextual use of such term herein. To the extent that the meaning of a term used herein — as understood by the ordinary artisan based on the contextual use of such term — differs in any way from any particular dictionary definition of such term, it is intended that the meaning of the term as understood by the ordinary artisan should prevail.

[0060] Regarding applicability of 35 U.S.C. §112, ]f6, no claim element is intended to be read in accordance with this statutory provision unless the explicit phrase "means for” or "step for” is actually used in such claim element, whereupon this statutory provision is intended to apply in the interpretation of such claim element.

[0061] Furthermore, it is important to note that, as used herein, "a” and "an” each generally denotes "at least one,” but does not exclude a plurality unless the contextual use dictates otherwise. When used herein to join a list of items, "or” denotes "at least one of the items,” but does not exclude a plurality of items of the list. Finally, when used herein to join a list of items, "and” denotes "all of the items of the list.”

[0062] The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar elements. While many embodiments of the disclosure may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting, reordering, or adding stages to the disclosed methods. Accordingly, the following detailed description does not limit the disclosure. Instead, the proper scope of the disclosure is defined by the appended claims. The present disclosure contains headers. It should be understood that these headers are used as references and are not to be construed as limiting upon the subjected matter disclosed under the header.

[0063] The present disclosure includes many aspects and features. Moreover, while many aspects and features relate to, and are described in, the context of processing job applicants, embodiments of the present disclosure are not limited to use only in this context.

[0064] The present disclosure provides a system, method and computer-readable medium for establishing effective channelized communications withing an organization. The present disclosure further provides a system, method and computer-readable medium for establishing channelized communication model of an organization and rendering channelized communication-related data to members of the organization via respective user interfaces (UIs).

[0065] In any modern organization, workflow-based communication is at the heart of all relevant communications within the organization. The disclosed embodiments represent a novel wave of Corporate Communications based on a communication model where communication channels are not created by individuals, but rather generated (i.e., established) based on the entities (i.e., objects) that move through the organization’s workflow. This communication model automatically integrates human communication directly with the organization's workflow in an optimal manner. Note that the individual and the subject matter are not used as the pivots in the proposed model. Instead, the new pivots can be automatically identified based on analysis of the workflow itself. These pivots may be the critical entities [i.e., objects) that are, for example, moving around the production line or within the workflow. In other words, any entity or component of an entity within the workflow which may be considered critical and/or worthy of a discussion amongst the team members may be used as the new pivot of the communication model. The pivot entity has assigned attributes associated with a particular communication channel. Once the channelized communication is established [i.e., auto-generated on-the-fly), individuals within the organization may simply search a database for objects or entities of interest and may subscribe or follow that object/entity [e.g., via a UI). The individuals or organization members may also use the chat channel associated with that object/entity to communicate with other individuals or interested parties [i.e., stakeholders) by accessing the channel via a member Ul.

[0066] For example, if the goal of a team within the organization is to make chairs, each part and/or component of the chair may be considered a pivot. Just as these components have length, color and other attributes, they may also have an associated communication channel the users [i.e., stakeholders) can communicate over with regards to the particular part/or component of the chair being moved through the production workflow. As discussed above, the participating organization members [e.g., stakeholders) may find this communication channel by searching for this object [i.e., a part or component) in the database and connect to this object and to other users [e.g., stakeholders) subscribed or otherwise connected to this object. The whole chair can also have a unique chat channel attribute. As a matter of fact, every single chair or its components may have their own unique communication channels or subchannels which can be the pivot for a variety of users [stakeholders) in the assembly line team.

[0067] The disclosed embodiments represent a completely different paradigm of inter-organizational communications that automatically guarantees optimal communication within organizations. This means that there are no longer any irrelevant messages. The messages are not routed by individual or arbitrary channels, but by the entities/objects of interest which are moving through the production process or workflow. Another important aspect of the disclosed communication model is that the entity/object itself can use its channel to communicate to users [stakeholders) about changes in the state of the object caused by other stakeholder entities or automatic systems. This provides the added benefit of timely communication about the state of the object to all stakeholders and allows them to react to those changes in real-time. This approach also, advantageously, centralizes and normalizes all communication about the object or entity of interest that can be accessed by a user via one or more UIs. In some embodiments, an Artificial Intelligence [Al] machine learning approach may be used for accurate prediction of communication channel configuration of the disclosed communication model.

[0068] The disclosed communication model may not only be applicable to pure communications systems, but may be applicable to all current business software applications. Every business application such as an accounting system or an Electronic Health Records is essentially a custom communication system. These systems are based on complex state machines that manage and communicate data in a structured fashion. The structured data that is input into these systems by humans are extracted from ad- hoc human communications flowing in from a variety of sources such as phone calls, faxes, mail, discussion forums, etc. After the extraction of the structured data and inputting it into the business applications, the source is either filed away or lost completely. The new communication paradigm provides a road map to integrate all such ad-hoc communications with the structured data in one place.

[0069] As an example, within an Electronic Health Record system, a physician is examining an X-ray of her patient. She decides to ask for a consultation from a radiology specialist. She may simply open up the X-ray’s chat channel and may mention or flag the radiology specialists while posing the question. The specialist receives a message from this X-ray object’s chat channel, proceeds to examine the X-ray and enters his comments into the same channel. All of these back-and-forth communications are now recorded in the channel that is an attribute of the X-ray object. Since the X-ray is associated with a specific patient in the database, all other attributes of that patient are known in the system ad may be used as pivots for private communication channels. In some embodiments, an Al bot, with access to all this information, can participate in the discussions within this chat channel with physicians and radiologists and may offer suggestions or insight in a significant and meaningful way since the entire domain of this object [e.g., mammogram X-ray along with patient demographics) are available to the Al bot connected to the patient predictive model.

[0070] While projects need to be completed on time, within budget, and according to specification, software development teams routinely fail to meet these requirements. Many teams assign a project manager to ensure that the project is successfully completed per the requirements. However, in existing systems it is difficult to determine whether the project manager is effectively performing their role - as so many factors can lead to the successful completion of a project and existing systems lack the transparency necessary to make an accurate determination of whether individual resources were competent with their specific tasks. Some organization use project management applications that lack proper communication channels. In one embodiment, automated generation of the chat channels is provided based on monitoring of the organization’s communications, current workflow and infrastructure.

[0071] FIG. 1A illustrates a network diagram of a system for intelligent automated monitoring of communications withing an organization consistent with the present disclosure.

[0072] Referring to FIG. 1A, the example network 100 includes a channel server node 102 connected to an organization network 101 including a variety of organization user (i.e., stakeholder) nodes 103 and objects 106. As discussed above, the objects 106 are the entities of interest to the stakeholders of the nodes 103. As a non-limiting example, the object may reflect an element of a production product associated with the production workflow, a medical file (e.g., X-ray, MRI, etc.) that is of interest to the organization’s (e.g., a hospital) doctors and technicians participating in a medical evaluation/diagnosis workflow or a contract or another legal document that is being accessed and modified or commented on by attorneys and paralegals of the law firm as part of legal process workflow. The organization network 101 may also include communication workflow records databases 105 for recording the communications within the organization. As can be seen from FIG. 1A, the nodes 103 may communicate with each other in nearly random fashion, where some of the nodes 103 may get communications related to the object(s) 106 that is not of any interest to the stakeholder associated with the particular node 103. [0073] The channel server node 102 may monitor the organization network 101 to determine the workflow data and the pivot objects 106. The channel server 102 may access the communication workflow records databases 105 to acquire additional workflow communication data reflecting communications between the nodes 103. Then, the channel server 102 may analyze the collected analytics data to generate the organization communication channels (and sub-channels) based on the pivot objects 106 and the workflow data ofthe organization network 101 as shown in FIG. IB. The channel server 102 may use channel configuration data accumulated through previous analysis of the organization network 101. In one embodiment, the channel server 102 may provide the collected analytics data along with the pre-stored channel configuration data to an artificial intelligence (Al) machine learning (ML) module 108 (see FIG. 1C) configured to predict most optimal communication channel(s) configuration. The AI/ML module 108 may generate one or more predictive models based on different classifiers generated from analytics data reflecting nodes, pivot objects, workflow data and the prestored channel configuration data.

[0074] FIG. IB illustrates a network diagram of an intelligent automated system for establishing effective channelized communications withing an organization, consistent with the present disclosure. Similarly, FIG. 1C illustrates a network diagram of an AI- based automated system for establishing effective channelized communications withing an organization, consistent with the present disclosure.

[0075] Referring to FIG. IB, the example network 100’ includes a channel server node 102 connected to an organization network 101’ including a variety of organization user (i.e., stakeholder) nodes 103 and pivot objects 106. As discussed above, the pivot objects 106 are the entities of interest to the stakeholders of the nodes 103.

[0076] With reference to FIG. 1C, the channel server node 102 may host the AI/ML module 108 configured to generate one or more predictive models 109 for accurate prediction of channel(s) configurations for the channelized model of the organization network 101 (see FIG. 1A).

[0077] The channel server node 102 may provide the collected analytics data along with the pre-stored channel configuration data 107 to the AI/ML module 108 configured to predict most optimal configurations for the channels 110 and subchannels 112. The AI/ML module 108 may generate one or more predictive models 109 based on different classifiers generated from analytics data reflecting nodes 103, pivot objects 106, workflow data and the pre-stored channel configuration data 107.

[0078] The organization network 101’ represents the channelized model of the organization network 101 (see FIG. 1A) generated by the channel server node 102 based on the analytics data acquired through monitoring of the organization network 101 and/or predictive outputs of the AI/ML module 108 (FIG. 1A). As can be seen from FIGs. IB and 1C, the organization network 101’ is now configured to have communication channels 110 and subchannels 112 generated based on the pivots - i.e., pivot objects 106 that have properties defining what nodes 103 should be communicating with each other over the communication channels 110 and subchannels 112. Using the pervious X-ray example, a doctor at one of the nodes 103 may be associated with the X-ray object 106 over a patient’s X-ray communication channel. The doctor may be interested in an opinion of another doctor or a radiologist. In this case, the doctor may add a request (i.e., a new property) to the X-ray object 106 indicating another doctor or the radiologist. Now, this doctor or radiologist at another node 103 is automatically added to this patient’s X- ray communication channel. This way, the channelized model provides for the most efficient communications where only the interested parties (i.e., stakeholders) communicate with each other with regards to the object of interest while all other communications that are not relevant to them are implemented over other communication channels 110 and/or communication subchannels 112. In one embodiment, as shown in FIG. 1C, an Al hot 116, with access to all channel information, can participate in the discussions within the chat channel with other users of nodes 103 and may offer suggestions or insight in a significant and meaningful way since the entire domain of a particular object 106 (e.g., an X-ray along with patient demographics) is available to the Al bot 116 connected to the AI/ML module 108 and the predictive model(s) 109 (e.g., patient model).

[0079] As discussed above, the channel server node 102 uses the analytics data including communication workflow records data 105 acquired through monitoring of the organization network 101 (see FIG. 1A). For example, the analytics data maybe used for accurate prediction of configuration parameters of the communication channels 110 and/or communication subchannels 112. The predicted channel configuration data may be recorded in the database 107 residing on or connected to the channel server node 102 that may be a cloud server or an edge server. The channel configuration data may be used for generation or modification of the communication channels 110 based on new pivot objects 106 being introduced into the organization or modification of the objects 106. In one embodiment, the channels 110 may be reconfigured or additional subchannels 112 may be added based on new nodes 103 being added to the organization network 101’. For example, a new doctor joins the group, a new diagnostic equipment is added, a new product workflow is introduced, etc.

[0080] As discussed above, the disclosed embodiments depicted in FIGs. IB and 1C provide a communications layer that forms the fabric of an informative expression (i.e., a communication of information/data) in the channel configuration database 107. The communications layer consists of nodes corresponding to the pivot objects in the channel configuration database 107. The pivot objects 106 correspond to any element of the database for which a dialog/communication is determined to exist or have the potential to exist. The nodes 103, in turn, are associated with the communication channels 110/112 in this new communications layer which is integrated into the existing channel configuration database 107 (or formed along with the database]. The channel server 102 may be configured to process the channel data and render the channelized communication model data to user devices (member nodes) 103’ via respective UI.

[0081] Each node 103 is tied to the pivot object(s) 106. The nodes 103 can be grouped together, or related to each other, based on the underlying relationship of the pivot objects 106 they are associated with (i.e., based on parent, siblings, children relationship). Such grouping may be reflected in the UI as "tiers of nodes.” The nodes 103, in turn, may serve as relays of information between the nodes themselves and to the end-users (i.e., interested stakeholders) of the underlying pivot objects 106 associated therewith. As the underlying pivot object 106 undergoes the workflow process, the stakeholders at the nodes 103 are kept apprised and are able to follow the pivot object 106 through its workflow process, as it goes from Tier to Tier, and as it becomes associated/unassociated with other nodes 103 or groups of entities.

[0082] According to the disclosed embodiments, the integration of the communications layer into the channel configuration database 107 includes forming nodes of expression (both informative expression and actionable expression) that are based on identified pivot points associated with the pivot objects 106 within the channel configuration database 107. Note that the pivot objects 106 are the critical objects representing any objects in the database that require discussion by organization members (i.e., stakeholders) or present data for communication to the organization members.

[0083] As discussed above, the nodes 103 are associated with pivot objects 106 in the channel configuration database 107. A node of an informative expression is a forum for the input or output of:

[0084] - Output of Textual and Graphical Representations related to the database;

[0085] - Input of Parameters or Operational commands.

[0086] The node may allow multiple types of expression: [0087] i. Expression from a user about the node and/or the critical object associated with the node; and

[0088] ii. Expression from the object regarding any changes made thereto.

[0089] Expression may be implemented in human-readable terms, such as natural language. Expression may be attributed to a user who caused the change of the pivot object. In one embodiment, expression may be received from another node or pivot object that affects this pivot object:

[0090] - i.e., informative expressions generated by the node and presented to the user, relating to a status /activity of the underlying pivot object or pivot objects related thereto (or derivative expression associated therewith); and

[0091] - pivot points serving as points of data relay between the targeted objects in the database.

[0092] The disclosed embodiments enable the conversation to follow the entity/object, as the entity/object travels through the workflow, and the user(s) to engage in a dialog related to the entity/object in one place with all relevant information. In one embodiment, any entity (pivot object) that organization members can discuss with verbal communication, has an additional attribute that contains dialog about the pivot object associated with the dialog. The pivot points provide access to:

[0093] 1. Data and parameters associated with the entities/pivot objects;

[0094] 2. Surrounding data/relevant data to the entities/pivot objects.

[0095] The pivot point may ensure that all contextually relevant information is presented at the node, so that the user would not need to navigate through the underlying database system to obtain relevant information. The nodes may enable publishing of informative expression associated with the related pivot object(s). The channelized communication system may generate the informative expression to be presented within the node based on an activity within the node (e.g., changes to a property of the pivot object associated with the node); and an activity related to the node (e.g., changes to a property of a different pivot object that affects this pivot object).

[0096] The channelized communication system may communicate the state of the pivot object by the expression that may be attributed to a user who caused a change in the critical object, but still be presented by the node itself. In one embodiment, the nodes may be structured into tiers based on one or more of:

[0097] i. The underlying structure of the pivot objects; [0098] ii. One or more workflows associated with the pivot objects.

[0099] In one embodiment, the nodes may form a network of multi-directional channeling of the informative expression (i.e., data). In this way, the nodes form relays of informative expression. In turn, the network of nodes forms the fabric of the channelized communication layer that provides a new-age of communication of informative expressions relating to pivot objects in the channel configuration database. Note that the stakeholders of each node 103 may self-identify and/or may be selected based on user activity monitored by the channel server 102. The channel server 102 may identify pivot objects 106 and the associated stakeholders, and automatically add the stakeholders to the corresponding nodes 103. The channelized communication system provided by the channel server 102 may provide access to the informative expressions of each node 103 to its stakeholders, channelized communication system may enable the stakeholders to add expressions into the Input Port of the node 103. The expression may be propagated through the relays in the node network as needed. Rules engine may be used to determine when to propagate the expression and/or to which connected nodes 103 the expression should be propagated. Alerts or notifications may be provided to the users (e.g., stakeholders) when the node 103 or an associated pivot object 106 are updated (e.g., when one or more informative expressions are published, including image, textual, audio and/or video data).

[00100] According to the disclosed embodiments, conducting communication of informative expression within the channelized communication system is implemented as follows. In one embodiment, each node 103 may track its activity (e.g., activity of a pivot object 106 associated with the node 103). Each node 103 may "talk” to related nodes to receive updates on their activity. Relations may be transitory - i.e., relations may have set beginning and/or ending times, and relations may form/break when the corresponding stakeholders follow and ‘unfollow’ the organization network. Furthermore, relations may be identified by a user and may be identified by the channelized communication system. Each node 103 may be configured to alert (e.g., provide an indication to) stakeholders in a derivative data point (e.g., a related node) of associated activity of the node and related activity of the related node.

[00101] In one embodiment, a channel configuration database may be used for project management. The channelized communication system may be integrated with the channel configuration database based on an expression layer that may include multiple forums for publishing the expression. Each forum may be associated with a corresponding pivot object via an attribute of the object. The channel server 102 may analyze communication workflow records databases 105 to identify and extract pivot objects - e.g., objects that move through a workflow. As discussed above, pivot object may serve as pivot points into the expression layer that is being constructed for the organization on top of the existing organization’s network. Workflows within the Project Management System may be replicated by the channelized communication system because the communication pivot points are tied to the pivot objects. Organization of the communication pivot points may mirror the organization of the pivot objects within the workflow stored in the communication workflow records databases 105.

[00102] In one embodiment, the channel server 102 may connect to an organization network (e.g., Project Management System, Electronic Healthcare Records system, Accounting System. Etc.]. The channel server 102 may identify critical entities referred to as pivot objects in the organization’s workflow. The channel server 102 may continuously track how the pivot objects move within the organization’s workflow while spawning communication data associated with the workflow pivot points defined by the pivot objects. The channel server 102 may also update the communication channels of the organization based on changes of the pivot objects that occur as the pivot objects move through the workflow. The updates are rendered on GUIs of the organization members subscribed to a particular object associated with the communication channel.

[00103] According to the disclosed embodiments, s user in the communication system is no longer observing a Dashboard UI of channels to choose from and communicate over. Instead, this is automatically configured by the channel server 102. The user is provided a UI of the notifications he needs based on his association with the critical object entities of the workflow. Accordingly, a user may no longer need to look for things because the user is provided all the information he needs via the UI. For example, a user would not need to go to a Project Management Tool to get updates, search for the objects he is following, and look at the conversations or comments there.

[00104] In one embodiment, a user is enabled to join/follow forums based on user’s stake/interest in an object or entity. Instead of users joining communication channels, users may follow objects/entities and may be notified of the updates related to these objects/entities. The channels of communication are provided to users via UI automatically based on the movement of the object/entity throughout the workflow. The UI is universal because it may reflect the channelized data based on the workflow regardless of the organization’s infrastructure.

[00105] FIG. 2 A illustrates a network diagram of a system including detailed features of a channel server node 102 consistent with the present disclosure.

[00106] Referring to FIG. 2 A, the example network 200 includes the channel server node 102 connected to a channel configuration database 107. As discussed above with respect to FIG. 1A, the channel server node 102 may monitor the organization network and infrastructure to acquire real-time analytics data 201 that may include pivot objects, organization user nodes, communications data, workflow data, etc. The channel server node 102 may also acquire communication workflow records data 210 from the communication workflow records databases 105 (see FIG. 1A).

[00107] While this example describes in detail only one channel server node 102, multiple such nodes may be connected to the network. It should be understood that the channel server node 102 may include additional components and that some of the components described herein may be removed and/or modified without departing from a scope of the channel server node 102 disclosed herein. The channel server node 102 may be a computing device or a server computer, or the like, and may include a processor 204, which may be a semiconductor-based microprocessor, a central processing unit (CPU), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), and/or another hardware device. Although a single processor 204 is depicted, it should be understood that the channel server node 102 may include multiple processors, multiple cores, or the like, without departing from the scope of the channel server node 102 system.

[00108] The channel server node 102 may also include a non-transitory computer readable medium 212 that may have stored thereon machine-readable instructions executable by the processor 204. Examples of the machine-readable instructions are shown as 214-220 and are further discussed below. Examples of the non-transitory computer readable medium 212 may include an electronic, magnetic, optical, or other physical storage device that contains or stores executable instructions. For example, the non-transitory computer readable medium 212 may be a Random-Access memory (RAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a hard disk, an optical disc, or other type of storage device. [00109] The processor 204 may fetch, decode, and execute the machine-readable instructions 214 to monitor at least one organization network to collect analytics data 201. The processor 204 may fetch, decode, and execute the machine-readable instructions 216 to process the analytics data to identify nodes and associated pivot objects of the at least one organization network. The processor 204 may fetch, decode, and execute the machine-readable instructions 218 to query workflow-related communication records of the at least one organization network to retrieve workflow communication data. The processor 204 may fetch, decode, and execute the machine- readable instructions 220 to generate at least one communication channel configured to connect the nodes and the associated pivot objects based on the workflow communication data.

[00110] FIG. 2B illustrates another network diagram of an Al based system 230. Referring to FIG. 2B, the example system 230 includes the channel server node 102 configured to host the AI/ML module 108 configured to generate predictive models 109. As discussed above with respect to FIG. 2 A, the channel server node 102 may monitor the organization network and infrastructure to acquire real-time analytics data and communication workflow records data 210. The channel server node 102 may be configured to feed the real-time analytics data 201 and communication workflow records data 210 into the AI/ML module 108 for producing configuration parameters for communication channels 110 and subchannels 112 (see FIG. 1C).

[00111] As in FIG. 2A, while this example describes in detail only one channel server node 102, multiple such nodes may be connected to the network. It should be understood that the channel server node 102 may include additional components and that some of the components described herein may be removed and/or modified without departing from a scope of the channel server node 102 disclosed herein.

[00112] As in FIG. 2A, the channel server node 102 may also include the non-transitory computer readable medium 212 that may have stored thereon machine-readable instructions executable by the processor 204. Examples of the machine-readable instructions for the embodiment shown in FIG. 2B are shown as 234-240 and are further discussed below.

[00113] The processor 204 may fetch, decode, and execute the machine-readable instructions 234 to monitor the at least one organization network to collect analytics data comprising workflow data. The processor 204 may fetch, decode, and execute the machine-readable instructions 236 to generate a plurality of classifiers based on the analytics data, the plurality of classifiers comprising data reflecting nodes and associated pivot objects of the at least one organization network. The processor 204 may fetch, decode, and execute the machine-readable instructions 238 to feed the plurality of classifiers to an AI/ML module configured to output configuration parameters for at least one communication channel. The processor 204 may fetch, decode, and execute the machine-readable instructions 240 to generate the at least one communication channel for the at least one organization network based on the configuration parameters.

[00114] FIG. 2C illustrates yet another network diagram of a system including detailed features of a channel server node 102 consistent with the present disclosure.

[00115] Referring to FIG. 2C, the example network 250 includes the channel server node 102 connected to a channel configuration database 107. As discussed above with respect to FIG. 1A, the channel server node 102 may monitor the organization network and infrastructure to acquire real-time analytics data that may include pivot objects, organization user nodes, communications data, etc. The channel server node 102 may also acquire communication workflow records data 210 from the communication workflow records databases 105 (see FIG. 1A). The channel server 102 may receive user credentials from a user (i.e., organization member] node 103’ that has a UI for rendering channelized data received from the channel server node 102 to the user.

[00116] While this example describes in detail only one channel server node 102, multiple such nodes may be connected to the network. It should be understood that the channel server node 102 may include additional components and that some of the components described herein may be removed and/or modified without departing from a scope of the channel server node 102 disclosed herein. The channel server node 102 may be a computing device or a server computer, or the like, and may include a processor 204, which may be a semiconductor-based microprocessor, a central processing unit (CPU), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), and/or another hardware device. Although a single processor 204 is depicted, it should be understood that the channel server node 102 may include multiple processors, multiple cores, or the like, without departing from the scope of the channel server node 102 system.

[00117] As in Figs. 2A and 2B, the channel server node 102 of FIG. 2C may also include a non-transitory computer readable medium 212 that may have stored thereon machine- readable instructions executable by the processor 204. Examples of the machine- readable instructions are shown as 254-262 and are further discussed below. Examples of the non-transitory computer readable medium 212 may include an electronic, magnetic, optical, or other physical storage device that contains or stores executable instructions. For example, the non-transitory computer readable medium 212 may be a Random-Access memory (RAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a hard disk, an optical disc, or other type of storage device.

[00118] The processor 204 may fetch, decode, and execute the machine-readable instructions 254 to receive user credentials from an organization member node. The processor 204 may fetch, decode, and execute the machine-readable instructions 256 identify at least one object associated with the user. The processor 204 may fetch, decode, and execute the machine-readable instructions 258 to extract properties from the at least one object and render the properties to the user via a UI of the organization member node. The processor 204 may fetch, decode, and execute the machine-readable instructions 260 to retrieve channel data of at least one channel associated with the object and render the channel data via the UI. The processor 204 may fetch, decode, and execute the machine-readable instructions 262 to automatically render channel communications to the user via the UI.

[00119] FIG. 3A illustrates a flowchart of a method 300 for establishing effective channelized communications within an organization consistent with the present disclosure.

[00120] Referring to FIG. 3A, the method 300 may include one or more of the steps described below. FIG. 3 A illustrates a flow chart of an example method executed by the channel server 102 (see FIG. 2A). It should be understood that method 300 depicted in FIG. 3A may include additional operations and that some of the operations described therein may be removed and/or modified without departing from the scope of the method 300. The description of the method 300 is also made with reference to the features depicted in FIG. 2 A for purposes of illustration. Particularly, the processor 204 of the channel server 102 may execute some or all of the operations included in the method 300.

[00121] With reference to FIG. 3A, at block 302, the processor 204 may monitor the at least one organization network to collect analytics data. At block 304, the processor 204 may process the analytics data to identify nodes and associated pivot objects of the at least one organization network. At block 306, the processor 204 may query com workflow-related communication records of the at least one organization network to retrieve workflow communication data. At block 308, the processor 204 may generate at least one communication channel configured to connect the nodes and the associated pivot objects based on the workflow communication data.

[00122] FIG. 3B illustrates a further flowchart of a method 301 for establishing effective channelized communications within an organization consistent with the present disclosure. Referring to FIG. 3B, the method 301 may include one or more of the steps described below. FIG. 3B illustrates a flow chart of an example method executed by the channel server 102 (see FIG. 2A). It should be understood that method 301 depicted in FIG. 3B may include additional operations and that some of the operations described therein may be removed and/or modified without departing from the scope of the method 301. The description of the method 301 is also made with reference to the features depicted in FIG. 2 A for purposes of illustration. Particularly, the processor 204 of the channel server 102 may execute some or all of the operations included in the method 301.

[00123] With reference to FIG. 3B, at block 314, the processor 204 may monitor the pivot objects to detect changes in pivot object properties. At block 316, the processor 204 may, responsive to a detection of the changes in the pivot object properties, reconfigure the at least one communication channel associated with the pivot object. At block 318, the processor 204 may configure the at least one communication channel based on previously collected channel configuration data retrieved from a channel configuration database. At block 320, the processor 204 may record channel configuration data of the at least one communication channel into a channel configuration database. At block 322, the processor 204 may identify pivot points corresponding to the pivot objects and store the pivot points in a channel configuration database, wherein the pivot objects are associated with data for communication among users of the nodes. At block 324, the processor 204 may monitor a current workflow of an organization in realtime to generate a plurality of communication subchannels associated with pivot objects based on the current workflow. At block 326, the processor 204 may provide indications to users of the nodes of the at least one organization network when at least one property of at least one pivot object associated with the nodes changes. Note that the reconfiguring of the communication channel may result in addition of nodes associated with the pivot object and/or elimination of nodes no longer associated with the pivot object.

[00124] FIG. 4A illustrates a flowchart of a method 400 for establishing effective channelized communications within an organization consistent with the present disclosure.

[00125] Referring to FIG. 4A, the method 400 may include one or more of the steps described below. FIG. 4A illustrates a flow chart of an example method executed by the channel server 102 (see FIG. 2B). It should be understood that method 400 depicted in FIG. 4A may include additional operations and that some of the operations described therein may be removed and/or modified without departing from the scope of the method 400. The description of the method 400 is also made with reference to the features depicted in FIG. 2B for purposes of illustration. Particularly, the processor 204 of the channel server 102 may execute some or all of the operations included in the method 400.

[00126] With reference to FIG. 4A, at block 402, the processor 204 may monitor the at least one organization network to collect analytics data comprising workflow data. At block 404, the processor 204 may generate a plurality of classifiers based on the analytics data, the plurality of classifiers comprising data reflecting nodes and associated pivot objects of the at least one organization network. At block 406, the processor 204 may feed the plurality of classifiers to an AI/ML module configured to output configuration parameters for at least one communication channel. At block 408, the processor 204 may generate the at least one communication channel for the at least one organization network based on the configuration parameters.

[00127] FI G. 4B illustrates a further flowchart of a method 401 for establishing effective channelized communications within an organization consistent with the present disclosure. Referring to FIG. 4B, the method 401 may include one or more of the steps described below.

[00128] FIG. 4B illustrates a flow chart of an example method executed by the channel server 102 (see FIG. 2B). It should be understood that method 401 depicted in FIG. 4B may include additional operations and that some of the operations described therein may be removed and/or modified without departing from the scope of the method 401. The description ofthe method 401 is also made with reference to the features depicted in FIG. 2B for purposes of illustration. Particularly, the processor 204 of the channel server 102 may execute some or all of the operations included in the method 401.

[00129] With reference to FIG. 4B, at block 414, the processor 204 may configure the at least one communication channel to connect the nodes and the associated pivot objects based on the workflow data. At block 416, the processor 204 may query communication workflow records of the at least one organization network to retrieve communication data of the at least one organization network. At block 418, the processor 204 may feed the communication data into the AI/ML module for generation of at least one communication predictive model. At block 420, the processor 204 may monitor the pivot objects to detect changes in pivot object properties. At block 422, the processor 204 may, responsive to a detection of the changes in the pivot object properties, provide the changes to the AI/ML module configured to generate reconfiguration parameters for at least one communication channel associated with the pivot object.

[00130] At block 424, the processor 204 may configure the at least one communication channel based on previously collected channel configuration data retrieved from a channel configuration database. At block 426, the processor 204 may record channel configuration data of the at least one communication channel onto a blockchain. Note that the application of reconfiguration parameters to the communication channel may result in addition of nodes associated with the pivot object and/or elimination of nodes no longer associated with the pivot object. At block 428, the processor 204 may identify pivot points corresponding to the pivot objects and provide the pivot points to the AI/ML module for generation of at least one predictive model of communication among the nodes based on association of the nodes with the pivot points. At block 430, the processor 204 may monitor a current workflow of an organization in real-time and may feed current workflow data to the AI/ML module configure to output configurations for a plurality of communication subchannels based on the current workflow.

[00131] FIG. 5 illustrates deployment of a machine learning model for predictive configuration of channelized communications based on blockchain assets consistent with the present disclosure.

[00132] In one disclosed embodiment, the channel model may be generated by the AI/ML module 108 that may use training data sets to improve accuracy of the prediction of configuration of channelized communications. The parameters used in training data sets may be stored in a channel configuration database 107 (see FIG. 1A). In one embodiment, a neural network may be used for channel modeling and prediction. The neural network may use organization nodes 103 and objects 106 (see FIG. 1A) as neurons.

[00133] In another embodiment, the AI/ML module 108 may use a decentralized storage such as a blockchain 510 that is a distributed storage system, which includes multiple nodes that communicate with each other. The decentralized storage includes an append-only immutable data structure resembling a distributed ledger capable of maintaining records between mutually untrusted parties. The untrusted parties are referred to herein as peers or peer nodes. Each peer maintains a copy of the parameter(s) records and no single peer can modify the records without a consensus being reached among the distributed peers. For example, the peers may execute a consensus protocol to validate blockchain storage transactions, group the storage transactions into blocks, and build a hash chain over the blocks. This process forms the ledger by ordering the storage transactions, as is necessary, for consistency. In various embodiments, a permissioned and/or a permissionless blockchain can be used. In a public or permissionless blockchain, anyone can participate without a specific identity. Public blockchains can involve assets and use consensus based on various protocols such as Proof of Work (PoW). On the other hand, a permissioned blockchain provides secure interactions among a group of entities which share a common goal such as communicating with respect to an object based on an organization’s workflow.

[00134] The disclosed embodiments may utilize a permissioned (private) blockchain that operates arbitrary, programmable logic, tailored to a decentralized storage scheme and referred to as "smart contracts” or "chaincodes.” In some cases, specialized chaincodes may exist for management functions and parameters which are referred to as system chaincodes. The system can further utilize smart contracts that are trusted distributed applications which leverage tamper-proof properties of the blockchain database and an underlying agreement between nodes, which is referred to as an endorsement or endorsement policy. Blockchain transactions associated with this application can be "endorsed” before being committed to the blockchain while transactions, which are not endorsed, are disregarded. An endorsement policy allows chaincodes to specify endorsers for a transaction in the form of a set of peer nodes that are necessary for endorsement. When a client sends the transaction to the peers specified in the endorsement policy, the transaction is executed to validate the transaction. After a validation, the transactions enter an ordering phase in which a consensus protocol is used to produce an ordered sequence of endorsed transactions grouped into blocks.

[00135] In the example depicted in FIG. 5, a host platform 520 builds and deploys a machine learning model for predictive monitoring of assets 530 (e.g., channel configurations). Here, the host platform 520 may be a cloud platform, an industrial server, a web server, a personal computer, a user device, and the like. Assets 530 can represent channel configuration parameters. The blockchain 510 can be used to significantly improve both a training process 502 of the machine learning model and a channelized communication predictive process 505 based on a trained machine learning model. For example, in 502, rather than requiring a data scientist/engineer or other user to collect the data, historical (heuristics) data may be stored by the assets 530 themselves (or through an intermediary, not shown) on the blockchain 510.

[00136] This can significantly reduce the collection time needed by the host platform 520 when performing predictive model training. For example, using smart contracts, data can be directly and reliably transferred straight from its place of origin (e.g., from organizations’ entities or communication workflow records) to the blockchain 510. By using the blockchain 510 to ensure the security and ownership of the collected data, smart contracts may directly send the data from the assets to the entities that use the data for building a machine learning model. This allows for sharing of data among the assets 530. The collected data may be stored in the blockchain 510 based on a consensus mechanism. The consensus mechanism pulls in (permissioned nodes) to ensure that the data being recorded is verified and accurate. The data recorded is time-stamped, cryptographically signed, and immutable. It is therefore auditable, transparent, and secure.

[00137] Furthermore, training of the machine learning model on the collected data may take rounds of refinement and testing by the host platform 520. E ach round may be based on additional data (produce by monitoring of the organization network) or data that was not previously considered to help expand the knowledge of the machine learning model. In 502, the different training and testing steps (and the data associated therewith) may be stored on the blockchain 510 by the host platform 520. Each refinement of the machine learning model e.g., changes in variables, weights, etc.) may be stored on the blockchain 510. This provides verifiable proof of how the model was trained and what data was used to train the model. Furthermore, when the host platform 520 has achieved a finally trained model, the resulting model may be stored on the blockchain 510.

[00138] After the model has been trained, it may be deployed to a live environment where it can make channel configuration predictions/decisions based on the execution of the final trained machine learning model. In this example, data fed back from the asset 530 may be input into the machine learning model and may be used to make event predictions such as likelihood of nodes 103 being associated with pivot objects 106 [see FIG. 1A). Determinations made by the execution of the machine learning model (e.g., pivot objects and channels, etc.) at the host platform 520 may be stored on the blockchain 510 to provide auditable/verifiable proof. As one non-limiting example, the machine learning model may predict a future change of a part of the asset 530 [e.g., new objects or changes of properties of the existing objects) and create alert or a notification to reconfigure the communication channel or to add/remove a subchannel. The data behind this decision maybe stored by the host platform 520 on the blockchain 510.

[00139] FIG. 6 A illustrates a flowchart of a method 600 for establishing channelized communication model of an organization and rendering channelized communication- related data to members of the organization consistent with the present disclosure.

[00140] Referring to FIG. 6A, the method 600 may include one or more of the steps described below. FIG. 6A illustrates a flow chart of an example method executed by the channel server 102 [see FIG. 2 C) . It should be understood that method 600 depicted in FIG. 6A may include additional operations and that some of the operations described therein may be removed and/or modified without departing from the scope of the method 600. The description of the method 600 is also made with reference to the features depicted in FIG. 2C for purposes of illustration. Particularly, the processor 204 of the channel server 102 may execute some or all of the operations included in the method 600.

[00141] With reference to FIG. 6A, at block 602, the processor 204 may receive user credentials from an organization member node. At block 604, the processor 204 may identify at least one object associated with the user. At block 606, the processor 204 may extract properties from the at least one object and render the properties to the user via a UI of the organization member node. At block 608, the processor 204 may retrieve channel data of at least one channel associated with the object and render the channel data via the UI. At block 610, the processor 204 may automatically render channel communications to the user via the UI.

[00142] FIG. 6B illustrates a further flowchart of a method 601 consistent with the present disclosure. Referring to FIG. 6B, the method 601 may include one or more of the steps described below. FIG. 6B illustrates a flow chart of an example method executed by the channel server 102 (see FIG. 2Cj. It should be understood that method 601 depicted in FIG. 6B may include additional operations and that some of the operations described therein may be removed and/or modified without departing from the scope of the method 601. The description of the method 601 is also made with reference to the features depicted in FIG. 2C for purposes of illustration. Particularly, the processor 204 of the channel server 102 may execute some or all of the operations included in the method 601.

[00143] With reference to FIG. 6B, at block 614, the processor 204 may identify the at least one object associated with the user based on a subscription. At block 616, the processor 204 may monitor the at least one object to detect changes in properties of the at least one object. At block 618, the processor 204 may responsive to a detection of the changes in the properties of the at least one object, reconfigure the at least one channel associated with the object. At block 620, the processor 204 may render an alert notification of the changes in the properties of the at least one object to the user via the UI. At block 622, the processor 204 may identify the at least one object associated with the user based on a workflow. At block 624, the processor 204 may render channel forum data to the user, wherein the channel forum data is directly connected to the object and is relevant to the user. Note that the reconfiguring of the communication channel may result in addition of nodes associated with the pivot object and/or elimination of nodes no longer associated with the pivot object.

EXAMPLE EMBODIMENT

[00144] In an example embodiment, a system, method and computer-readable medium may be used for automated predicting of a medical diagnosis of a patient over channelized communications of a healthcare organization.

[00145] In any modern organization such as a healthcare organization, workflowbased communication is at the heart of all relevant communications within the organization. The disclosed embodiments are directed to a communication model where communication channels are not created by individuals, but rather generated (i.e., established) based on the entities (i.e., patient objects) that may be assessed by other healthcare organization entities such as doctors, clinical technicians, etc. This channelbased communication model automatically integrates human communication directly with the organization's patient treatment workflow in an optimal manner. Note that the individual and the subject matter are not used as the pivots in the proposed model. Instead, the patients are used as pivots or pivot objects around the private communication channels are created. The patient objects may have object or sub-objects associated with them. For example, an X-Ray, CT scan, MRI, etc. may be used as a patient object (or a sub-object) associated with the main pivot patient object. However, a patient sub-objects that is considered critical and/or worthy of a discussion amongst the team members (i.e., doctors, medical technicians, etc.) may be used as the new pivot of the communication model. The pivot object may have assigned attributes associated with a particular communication channel. Once the channelized communication is established (i.e., auto-generated on-the-fly), individuals within the healthcare organization that are associated with the patient object may simply search a database for patient objects or sub-objects of interest and may subscribe or follow that patient object. The individuals (doctors, technicians, etc.) may also use the chat channel associated with that patient object to communicate with other individuals or interested parties (i.e., other doctors that may be considered stakeholders).

[00146] For example, if the goal of a team within the medical organization is to remotely collaborate on patient's treatment and diagnosis, each patient’s object or subobjects (e.g., blood test, X-Ray, CT scan, MRI, etc.) maybe considered a pivot object around which the communication channel is created. As discussed above, the stakeholders (doctors, or other medical practitioners) may find the patient-related communication channel by searching for this patient object in the database and may connect to this patient object and to other stakeholders (doctors, or other medical practitioners) subscribed or otherwise connected to this patient object. Every single patient or his/her associated objects may have their own unique communication channels or subchannels which can be created around the pivot object for a variety of users (stakeholders) within the healthcare organization. For example, several medical practitioners can collaborate over a private channel on reading MRI or X-Ray imaging of the patient object. [00147] The proposed patient object-based approach also, advantageously, centralizes and normalizes all communication about the patient. In one embodiment, an Al machine learning approach may be used for accurate prediction and determination of patients; diagnosis based on the patient’s channel communications, patient’s sub-objects and stored diagnosis data of other patients having similar characteristics such as age, gender, race, geographic location, medical history, etc.

[00148] As a further non-limiting an example related to an Electronic Health Record system, a physician is examining an X-Ray of her patient. She decides to ask for a consultation from a radiology specialist. She may simply open up the X-Ray’s-related private chat channel and may mention or flag the radiology specialists while posing the question. The radiology specialist receives a message from this X-ray object’s chat channel, proceeds to examine the X-ray and enters his comments into the same private channel. All of these back-and-forth communications are now recorded in the private channel that may serve an attribute of the X-Ray object. Since the X-Ray is associated with a specific patient (i.e., patient object] in the database, all other attributes of that patient are known in the system and may be used as pivots for other private communication channels.

[00149] In one embodiment, an Al bot, with an access to all this information, can participate in the discussions within this chat channel with physicians, radiologists and other practitioners and may offer suggestions or insight in a significant and meaningful way since the entire domain of this object (e.g., mammogram, X-ray, blood tests etc. along with patient demographics] are available to the Al bot connected to the patient AI/ML module configured to generate predictive patient models for suggesting diagnosis and appropriate treatments.

[00150] Note that what humans are saying on the channel related to the patient object (or sub-object] becomes an intrinsic property of the patient object that can be assessed/analyzed by the AI/ML module. This enables, for example, "opinion of X-Rays” to be available in reference to the patient object, and now the AI/ML module will have access to that data and can analyze the X-Ray in view of the doctors’ comments as well. If the channel is considered to be an object itself, then channel comments may change the state of this object based on natural language processing [NLP],

[00151] FIG. 7A illustrates a network diagram of an Al-based automated medical diagnosis prediction system, consistent with the present disclosure. [00152] Referring to FIG. 7A, the example medical organization network 700 includes a diagnosis server (DS) node 702 connected to an organization network including a variety of organization user nodes 703 and patient’s objects 706 residing on healthcare organization’s server 716. As discussed above, the patient’s objects 706 are the entities of interest to the doctor s/medical practitioners - e.g., stakeholders of the nodes 703. As a non-limiting example, the patient’s objects 706 and/or sub-objects 707 may reflect a medical file (e.g., X-ray, MRI, blood tests, medical history, etc.) that is of interest to the organization’s (e.g., a hospital) doctors and clinical technicians participating in a medical evaluation/diagnosis workflow. As discussed above, the patient’s object 706 may be associated with other patient’s objects and/or sub-objects 707. For example, the X-ray or MRI images maybe represented by the sub-object 707.

[00153] The medical organization network 700 may also include remote servers (e.g., cloud servers) 704 that may host a remote patients’ database 705. This database may store medical records of patients of other medical organizations including patients’ medical history, diagnosis, medical images, test results, etc. The DS node 702 may host local patients’ database 715 for storing medical records of the patients of this medical organization (i.e., a hospital or medical center) including patients’ medical history, diagnosis, medical images, test results, etc.

[00154] The DS node 702 may host an artificial intelligence/machine learning (AI/ML) module 708 configured to generate predictive models 709 that may provide predictive parameters for making a diagnosis of the patient represented by the patient objects 706 and/or sub-objects707. The DS node 702 may access the patient objects 706 and subobjects 707 to collect medical data. The DS node 702 may access the medical records from database(s) 715 to acquire medical records of local patients having the same characteristics (i.e., age, gender, race, medical history, previous diagnosis, etc.) of the patient represented by the patient object 706. In one embodiment, the DS node 702 may also access the remote medical records from database(s) to 705 acquire medical records of patients of other medical organizations having the same characteristics (i.e., age, gender, race, medical history, previous diagnosis, geographic location, etc.) of the patient represented by the patient object 706.

[00155] Then, the DS node 702 may analyze all patient-related data to generate the diagnosis or a treatment plan. In one embodiment, the DS node 702 may provide the collected patient-related data along with the medical data acquired from the databases 705 and 715 to the AI/ML module 708 configured to predict patient’s diagnosis and/or treatment plan. The AI/ML module 708 may generate one or more patient-related predictive models 709 based on different classifiers generated from the patients’ objects

706 and/or sub-object 707 data and the patient’s records retrieved from the databases 705 and 715.

[00156] FIG. 7B illustrates a network diagram of an Al-based automated medical diagnosis prediction system implemented over a blockchain network, consistent with the present disclosure.

[00157] Referring to FIG. 7B, the example medical organization network 700’ includes a diagnosis server (DS) node 702 connected to an organization network including a variety of organization user nodes 703 and patient’s objects 706 residing on healthcare organization’s server 716. As discussed above, the patient’s objects 706 are the entities of interest to the doctor s/medical practitioners - e.g., stakeholders of the nodes 703. As a non-limiting example, the patient’s objects/sub-objects 707 may reflect a medical file (e.g., X-ray, MRI, blood tests, medical history, etc.] that is of interest to the organization’s (e.g., a hospital) doctors and clinical technicians participating in a medical evaluation/diagnosis workflow. As discussed above, the patient’s object 706 may be associated with other patient’s objects and/or sub-objects 707. For example, the X-ray or MRI images maybe represented by the sub-object 707.

[00158] The medical organization network 700 may also include remote servers (e.g., cloud servers) 704 that may host a remote patients’ database 705. This database may store medical records of patients of other medical organizations including patients’ medical history, diagnosis, medical images, test results, etc. The DS node 702 may host local patients’ database 715 for storing medical records of the patients of this medical organization (i.e., a hospital or medical center) including patients’ medical history, diagnosis, medical images, test results, etc.

[00159] The DS node 702 may host an artificial intelligence/machine learning (AI/ML) module 708 configured to generate predictive models 709 that may provide predictive parameters for making a diagnosis of the patient represented by the patient objects 706 and/or sub-objects 707. The DS node 702 may access the patient objects 706 and/or subobjects 707 to collect medical data. The DS node 702 may access the medical records from database(s) 715 to acquire medical records of local patients having the same characteristics (i.e., age, gender, race, medical history, previous diagnosis, etc.) of the patient represented by the patient object 706. In one embodiment, the DS node 702 may also access the remote medical records from database (s) 705 to acquire medical records of patients of other medical organizations having the same characteristics (i.e., age, gender, race, medical history, previous diagnosis, geographic location, etc J of the patient represented by the patient object 706.

[00160] Then, the DS node 702 may analyze all patient-related data to generate the diagnosis or the treatment plan. In one embodiment, the DS node 702 may provide the collected patient-related data along with the medical data acquired from the databases

705 and 715 to the AI/ML module 708 configured to predict patient’s diagnosis and/or treatment plan. The AI/ML module 708 may generate one or more patient-related predictive models 709 based on different classifiers generated from the patients’ objects

706 and/or sub-objects 707 data and the patient’s records retrieved from the databases

705 and 715.

[00161] In one disclosed embodiment, the DS node 702 may receive the confidential patient-related data from a permissioned blockchain 711 ledger 713 based on a consensus from a medical organization, nodes 703, 704 and 766 that may server as blockchain peers. Additionally, confidential patients’ medical history-related information and previous diagnosis may also be acquired from the permissioned blockchain 710. The newly acquired patient’s data (received from scanning the objects

706 and sub-objects 707) may be also recorded on the ledger 713 of the blockchain 711 so it can be used as training data for the predictive model(s) 709. In this implementation the DS node 702, the remote server node 704, the medical organization server 716 and the medical practitioners’ devices 703 all may serve as blockchain 711 peer nodes. In one embodiment, the local patients’ data 715 and the remote patients’ data 705 may be duplicated on the blockchain ledger 713 for higher security and confidentiality of storage. [00162] The AI/ML module 708 may generate a predictive model(s) 709 to predict the diagnosis parameters for the patient object 706 in response to the specific relevant prestored patient diagnosis-related data acquired from the blockchain 711. This way, the current diagnosis parameters may be predicted based not only on the live data acquired from scanning the patient object 706 and sub-objects 707, but also based on the previously collected patient-related data associated with the current patient represented by the patient object 706. Once the diagnosis is produces by the DS 702 node, the diagnosis may be recorded onto the blockchain ledger 713 along with the patient data for confidential storage and for future references.

[00163] FIG. 7C illustrates a network diagram of an Al-based automated medical diagnosis prediction system within channelized communications of a healthcare organization, consistent with the present disclosure.

[00164] Referring to FIG. 7C, the example medical organization network 700” includes a diagnosis server [DS] node 702 connected over private channels 710 and/or subchannels 712 to an organization network including a variety of organization user nodes 703 and patient’s objects 706 residing on healthcare organization’s server 716. As discussed above, the patient’s objects 706 are the entities of interest to the doctor s/medical practitioners - i.e., stakeholders of the nodes 703. As a non-limiting example, the patient’s objects/sub-objects 707 may reflect a medical file (e.g., X-ray, MRI, blood tests, medical history, etc.] that is of interest to the organization’s (e.g., a hospital] doctors and clinical technicians participating in a medical evaluation/diagnosis workflow. As discussed above, the patient’s object 706 may be associated with other patient’s objects/sub-objects 707. For example, the X-ray or MRI images may be represented by the sub-object 707. The private communication channels 710 and/or sub-channels 712 may be created solely based on the patient object 706 as shown in FIG. 7C.

[00165] The medical organization network 700 may also include remote servers (e.g., cloud servers] 704 that may host a remote patients’ database 705. This database may store medical records of patients of other medical organizations including patients’ medical history, diagnosis, medical images, test results, etc. The DS node 702 may host local patients’ database 715 for storing medical records of the patients of this medical organization (i.e., a hospital or medical center] including patients’ medical history, diagnosis, medical images, test results, etc.

[00166] The DS node 702 may host the AI/ML module 708 configured to generate predictive models 709 that may provide predictive parameters for making a diagnosis of the patient represented by the patient objects 706 and patient sub-objects 707. The DS node 702 may access the patient objects 706 and patient sub-objects 707 to collect patient-related medical data. The DS node 702 may access the medical records over the private channels 710/712 from database(s] 715 to acquire medical records of local patients having the same characteristics (i.e., age, gender, race, medical history, previous diagnosis, etc.) of the patient represented by the patient object 706. In one embodiment, the DS node 702 may also access the remote medical records from database(s) 705 to acquire medical records of patients of other medical organizations having the same characteristics (i.e., age, gender, race, medical history, previous diagnosis, geographic location, etc.) of the patient represented by the patient object 706.

[00167] Then, the DS node 702 may analyze all patient-related data to generate the diagnosis or treatment plan. In one embodiment, the DS node 702 may provide the collected patient-related data along with the medical data acquired from the databases 705 and 715 to the AI/ML module 708 configured to predict patient’s diagnosis and/or treatment plan. The AI/ML module 708 may generate one or more patient-related predictive models 709 based on different classifiers generated from the patients’ objects 706/707 data and the patient’s records retrieved from the databases 705 and 715.

[00168] In the embodiment depicted in FIG. 7C, the organization network 700" represents the channelized model of the organization network 700 (see FIG. 7A) generated based on the patient objects 706 to provide for patient-centric private communications. As can be seen from FIG. 7C, the organization network 700” may be configured to have communication channels 710 and subchannels 712 generated based on the pivot objects - i.e., the patient object(s) 706 that have properties defining what nodes 703 should be communicating with each other over the communication channels 710 and subchannels 712.

[00169] Using the pervious X-Ray example, a doctor at one of the nodes 703 may be associated with the X-Ray object/sub-object 707 over a patient’s object 706 X-Ray communication channel. The doctor at the node 703 may be interested in an opinion of another doctor or a radiologist at other nodes 703. In this case, the doctor may add a request (i.e., a new property) to the X-Ray sub-object 707 indicating another doctor or the radiologist at nodes 703 as stakeholders. Now, this doctor or the radiologist at another node(s) 703 is automatically added to this patient object 706 X-Ray communication channel (e.g., 710 or 712). This way, the channelized object-centered communication model provides for the most efficient communications where only the interested parties (i.e., stakeholders - doctors and radiologists) communicate with each other with regards to the object of interest (i.e., patient object 706) while all other communications that are not relevant to them are implemented over other communication channels 710 and/or subchannels 712. [00170] In one embodiment, an Al bot 719, with access to all channel information, can participate in the discussions within the chat channel with other users (i.e., medical providers) of nodes 703 and may offer suggestions or insight in a significant and meaningful way since the entire domain of a particular patient sub-object object 707 (e.g., an X-Ray along with patient medical records) is available to the Al bot 719 connected to the AI/ML module 708 and the predictive model(s) 709 (e.g., patient models). All of the channels’ 710/712 communications between the nodes 703 maybe acquired and used as inputs into the AI/ML module 708 for more accurate prediction of patient diagnosis parameters.

[00171] A channel server node (not shown) may use analytics data including communication workflow records data acquired through monitoring of the organization network 700 (see FIG. 7A) for accurate configuration and on-the-fly reconfiguration of the channels 710 and/or subchannels 712. The channel configuration data may be recorded in a database residing on or connected to the channel server node that may be a cloud server or an edge server. The channels’ configuration data may be used for generation or modification of the communication channels 710 and/or sub-channels 712 based on new pivot patient objects 706 and patient sub-objects 707 being introduced into the organization network 700” or modification ofthe objects 706 (i.e., adding new patient sub-objects 707). Note that the diagnosis may be added to the patient object 706 as a patient sub-object 707 as well. In one embodiment, the channels 710 may be reconfigured or additional sub-channels 712 may be added based on new nodes 703 being added to the organization network 700". For example, a new doctor may join the group, a new diagnostic equipment is added, a new product workflow (i.e., medical procedure is introduced, etc.).

[00172] The disclosed embodiments depicted in FIG. 7C provide a communications layer that forms the fabric of an informative expression (i.e., a communication of information/data) in the channel configuration database (not shown). The communication layer consists of nodes corresponding to the pivot objects in the channel configuration database. The pivot patient objects 706 correspond to any element of the database for which a dialog/communication is determined to exist or have the potential to exist. The nodes 703, in turn, are associated with the communication channels 710/712 centered around the patient objects 706 in this new communications layer which is integrated into the existing channel configuration database (or formed along with the database).

[00173] Each node 703 is tied to the pivot patient object(s) 706. The nodes 703 can be grouped together, or related to each other, based on the underlying relationship of the pivot patient objects 706 they are associated with (i.e., based on parent, siblings, children relationship). Such grouping may be reflected in the UIs as "tiers of nodes.” The nodes 703, in turn, may serve as relays of information between the nodes themselves and to the end-users (i.e., interested stakeholders) of the underlying pivot patient objects 706 associated therewith. As the underlying pivot patient object 706 undergoes the medical workflow process, the stakeholders at the nodes 703 are kept apprised and are able to follow the patient pivot object 706 through its medical workflow process, as it goes from Tier to Tier, and as it becomes associated/unassociated with other nodes 703 or groups of entities.

[00174] According to the disclosed embodiments, the integration of the communications layer into the channel configuration database includes forming nodes of expression (both informative expression and actionable expression) that are based on identified pivot points associated with the pivot patient objects 706 within the channel configuration database. Note that the pivot patient objects 706 are the critical objects representing any objects in the database that require discussion by organization members (i.e., stakeholders - doctors, practitioners, etc.) or present data for communication to the organization members.

[00175] As discussed above, the nodes 703 are associated with the patient pivot objects 706 in the channel configuration database. A node of an informative expression is a forum for the input or output of:

[00176] - Output of Textual and Graphical Representations related to the database;

[00177] - Input of Parameters or Operational commands.

[00178] The node may allow multiple types of expression:

[00179] i. Expression from a user about the node and/or the critical object associated with the node; and

[00180] ii. Expression from the object regarding any changes made thereto.

[00181] Expression may be implemented in human-readable terms, such as natural language. Expression may be attributed to a user who caused the change of the pivot object. In one embodiment, expression may be received from another node or pivot object that affects this pivot object:

[00182] - i.e., informative expressions generated by the node and presented to the user, relating to a status /activity of the underlying pivot object or pivot objects related thereto (or derivative expression associated therewith); and

[00183] - pivot points serving as points of data relay between the targeted objects in the database.

[00184] In one embodiment, any entity (pivot object) that organization members can discuss with verbal communication, has an additional attribute that contains a dialog about the pivot object associated with the dialog. The pivot points provide access to: [00185] 1. Data and parameters associated with the entities/pivot objects;

[00186] 2. Surrounding data/relevant data to the entities/pivot objects.

[00187] The pivot point may ensure that all contextually relevant information is presented at the node (e.g., nodes 703) so that the user would not need to navigate through the underlying database system to obtain relevant information. The nodes may enable publishing of informative expression associated with the related pivot object(s). The channelized communication system may generate the informative expression to be presented within the node based on an activity within the node (e.g., changes to a property of the pivot patient object 706 associated with the node - e.g., patient is now diagnoses as an oncology patient and is assigned to a different medical group); and an activity related to the node (e.g., changes to a property of a different pivot object that affects this pivot object).

[00188] The channelized communication system may communicate the state of the pivot object by the expression that may be attributed to a user who caused a change in the critical object, but still be presented by the node itself. In one embodiment, the nodes may be structured into tiers based on one or more of:

[00189] i. The underlying structure of the pivot objects;

[00190] ii. One or more workflows associated with the pivot objects.

[00191] In one embodiment, the nodes may form a network of multi-directional channeling of the informative expression (i.e., data). In this way, the nodes form relays of informative expression. In turn, the network of nodes forms the fabric of the channelized communication layer that provides a new-age of communication of informative expressions relating to pivot objects in the channel configuration database. Note that the stakeholders of each node 703 may self-identify and/or may be selected based on user activity monitored by the channel server. The channel server may identify pivot objects 706 and the associated stakeholders, and automatically add the stakeholders to the corresponding nodes 703. The channelized communication system may provide access to the informative expressions of each node 703 to its stakeholders. The channelized communication system may enable the stakeholders to add expressions into the Input Port of the node 703. The expression may be propagated through the relays in the node network as needed. Rules engine may be used to determine when to propagate the expression and/or to which connected nodes 703 the expression should be propagated. Alerts or notifications may be provided to the stakeholders when the node 703 or an associated patient pivot object 706 are updated (e.g., when one or more informative expressions are published],

[00192] According to the disclosed embodiments, conducting communication of informative expression within the channelized communication system is implemented as follows. In one embodiment, each node 703 may track its activity (e.g., activity of a pivot object 706 associated with the node 703). Each node 703 may "talk” to related nodes to receive updates on their activity. Relations may be transitory - i.e., relations may have set beginning and/or ending times, and relations may form/break when the corresponding stakeholders follow and "unfollow” the organization network. Furthermore, relations may be identified by a user and may be identified by the channelized communication system. Each node 703 may be configured to alert (i.e., provide an indication) stakeholders in a derivative data point (e.g., a related node) of associated activity of the node and related activity of the related node.

[00193] FIG. 8 illustrates a network diagram of a system including detailed features of a diagnosis server (DS) node 702 consistent with the present disclosure.

[00194] Referring to FIG. 8, the example network 800 includes the DS server node 702 configured to host the AI/ML module 708 configured to generate patient-related predictive models 709. As discussed above with respect to FIGs. 7A-C, the DS node 702 may host the AI/ML module 708 configured to generate patient-related predictive models 709 that may provide predictive parameters for making a diagnosis of the patient represented by the patient objects 706 and sub-objects 707 (see FIGs. 7A). The DS node 702 may access the patient objects 706 and sub-objects 707 to collect medical data. The DS node 702 may access the medical records from database(s) to acquire medical records of local patients having the same characteristics (i.e., age, gender, race, medical history, previous diagnosis, etc.) of the patient represented by the patient object 706. In one embodiment, the DS node 702 may also access the remote medical records from database(s) to acquire medical records of patients of other medical organizations having the same characteristics (i.e., age, gender, race, medical history, previous diagnosis, geographic location, etc.) of the patient represented by the patient object 706.

[00195] Then, the DS node 702 may provide the collected patient-related data along with the medical data acquired from the databases to the AI/ML module 708 configured to predict patient’s diagnosis and/or treatment plan. The AI/ML module 708 may generate one or more patient-related predictive models 709 based on different classifiers generated from the patients’ objects/sub-objects 706/707 data and the patient’s records retrieved from the databases or from a blockchain ledger.

[00196] The DS node 702 may be configured to feed the real-time patient’s data 801 acquired from the patients’ objects and patients’ records data 802 acquired from databases (or from blockchain 711 ledger 713) into the AI/ML module 708 configured to output diagnosis parameters for the patent associated with the patient object for which private communication channels 710 and subchannels 712 (see FIG. 7C) are created.

[00197] While this example describes in detail only one DS server node 702, multiple such nodes maybe connected to the network. It should be understood that the DS server node 702 may include additional components and that some of the components described herein may be removed and/or modified without departing from a scope of the DS server node 702 disclosed herein. The DS server node 702 may be a computing device or a server computer, or the like, and may include a processor 804, which may be a semiconductor-based microprocessor, a central processing unit (CPU), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), and/or another hardware device. Although a single processor 804 is depicted, it should be understood thatthe DS server node 702 may include multiple processors, multiple cores, or the like, without departing from the scope of the D S server node 702 system.

[00198] The DS server node 702 may also include a non-transitory computer readable medium 812 that may have stored thereon machine-readable instructions executable by the processor 804. Examples ofthe machine-readable instructions are shown as 814-824 and are further discussed below. Examples of the non-transitory computer readable medium 812 may include an electronic, magnetic, optical, or other physical storage device that contains or stores executable instructions. For example, the non-transitory computer readable medium 812 may be a Random-Access memory (RAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a hard disk, an optical disc, or other type of storage device.

[00199] The processor 804 may fetch, decode, and execute the machine-readable instructions 814 to identify at least one patient object associated with a patient. The processor 804 may fetch, decode, and execute the machine-readable instructions 816 to monitor the at least one patient object to collect patient’s data 801. The processor 804 may fetch, decode, and execute the machine-readable instructions 818 to access a communication channel associated with the at least one patient object to extract communications’ data of the plurality of provider nodes. The processor 804 may fetch, decode, and execute the machine-readable instructions 820 to generate a plurality of classifiers based on the collected patient’s data and the extracted communications’ data. The processor 804 may fetch, decode, and execute the machine-readable instructions 822 to feed the plurality of classifiers into an AI/ML module 708 configured to output patient diagnosis-related data into the communication channel. The processor 804 may fetch, decode, and execute the machine-readable instructions 824 to update the patient object based on the diagnosis-related data.

[00200] FIG. 9A illustrates a flowchart of a method 900 for Al -based prediction of medical diagnosis consistent with the present disclosure.

[00201] Referring to FIG. 9A, the method 900 may include one or more of the steps described below. FIG. 9A illustrates a flow chart of an example method executed by the DS node 702 (see FIG. 8). It should be understood that method 900 depicted in FIG. 9A may include additional operations and that some of the operations described therein may be removed and/or modified without departing from the scope of the method 900. The description of the method 900 is also made with reference to the features depicted in FIG. 8 for purposes of illustration. Particularly, the processor 804 of the DS node 702 may execute some or all of the operations included in the method 900.

[00202] With reference to FIG. 9A, at block 902, the processor 804 may identify at least one patient object associated with a patient. At block 904, the processor 804 may monitor the at least one patient object to collect patient’s data. At block 906, the processor 804 may access a communication channel associated with the at least one patient object to extract communications' data of the plurality of provider nodes. At block 908, the processor 804 may generate a plurality of classifiers based on the collected patient’s data and the extracted communications’ data. At block 910, the processor 804 may feed the plurality of classifiers into an AI/ML module configured to output patient diagnosis- related data into the communication channel. At block 912, the processor 804 may update the patient object based on the diagnosis-related data.

[00203] FIG. 9B illustrates a further flowchart of a method 900’ for an Al-based automated medical diagnosis prediction consistent with the present disclosure. Referring to FIG. 9B, the method 901 may include one or more of the steps described below.

[00204] FIG. 9B illustrates a flow chart of an example method executed by the DS node 702 [see FIG. 8). It should be understood that method 900’ depicted in FIG. 9B may include additional operations and that some of the operations described therein may be removed and/or modified without departing from the scope of the method 900’. The description of the method 900’ is also made with reference to the features depicted in FIG. 8 for purposes of illustration. Particularly, the processor 804 of the DS node 702 may execute some or all of the operations included in the method 900’.

[00205] With reference to FIG. 9B, at block 914, the processor 804 may receive a diagnosis request for the patient over the communication channel created based on the patient object. At block 916, the processor 804 may access at least one object connected to the patient object to acquire medical data of the patient. At block 918, the processor 804 may generate at least one classifier based on the medical data of the patient. At block 920, the processor 804 may extract the communications’ data of the channel comprising messaging of medical providers related to the at least one patient object. An extraction of the communications’ data may include an application of a natural language processing [NLP], At block 922, the processor 804 may continuously monitor the at least one patient object and the communication channel associated with the at least one patient object to update the plurality of classifiers.

[00206] At block 924, the processor 804 may feed the plurality of classifiers into the AI/ML module configured to generate a patient-related predictive model designed to produce a patient diagnosis. At block 926, the processor 804 may provide the patient diagnosis into the channel by converting the patient diagnosis using an application of a natural language processing [NLP]. At block 928, the processor 804 may generate a patient diagnosis object connected to the at least one patient object. [00207] At block 930, the processor 804 may detect changes in the at least one patient object and, responsive to a detection of the changes in the at least one patient object, provide the changes to the AI/ML module configured to generate updated diagnosis- related data. At block 932, the processor 804 may acquire patients’ records from local and remote databases and to generate at least one classifier based on the collected patient’s data and the patients’ records. At block 934, the processor 804 may record the collected patient’s data and the diagnosis-related data onto a blockchain.

[00208] FIG. 10 illustrates deployment of a machine learning model 709 for prediction of patient’s diagnosis-related communications based on blockchain assets consistent with the present disclosure.

[00209] In one disclosed embodiment, the patients’ diagnosis may be generated by an AI/ML module 708 that may use training data sets to improve accuracy of the prediction of the patient’s diagnosis. The parameters used in training data sets may be stored in patients’ databases 705 and 705 (see FIG. 7 A], In one embodiment, a neural network may be used for patients’ modeling and prediction. The neural network may use medical organization nodes 703 and patient objects 706 (see FIG. 7A-7C] as neurons.

[00210] In another embodiment, the AI/ML module 708 may use a decentralized storage such as a blockchain 711 that is a distributed storage system, which includes multiple nodes that communicate with each other. The decentralized storage includes an append-only immutable data structure resembling a distributed ledger capable of maintaining records between mutually untrusted parties. The untrusted parties are referred to herein as peers or peer nodes. Each peer maintains a copy of the parameters] records and no single peer can modify the records without a consensus being reached among the distributed peers. For example, the peers may execute a consensus protocol to validate blockchain storage transactions, group the storage transactions into blocks, and build a hash chain over the blocks. This process forms the ledger 713 by ordering the storage transactions, as is necessary, for consistency. In various embodiments, a permissioned and/or a permissionless blockchain can be used. In a public or permissionless blockchain, anyone can participate without a specific identity. Public blockchains can involve assets and use consensus based on various protocols such as Proof of Work (PoW). On the other hand, a permissioned blockchain provides secure interactions among a group of entities which share a common goal such as communicating with respect to an object based on an organization’s workflow. [00211] The disclosed embodiments may utilize a permissioned (private) blockchain that operates arbitrary programmable logic, tailored to a decentralized storage scheme and referred to as "smart contracts” or "chaincodes.” In some cases, specialized chaincodes may exist for management functions and parameters which are referred to as system chaincodes. The system can further utilize smart contracts that are trusted distributed applications which leverage tamper-proof properties of the blockchain database and an underlying agreement between nodes, which is referred to as an endorsement or endorsement policy. Blockchain transactions associated with this application can be "endorsed” before being committed to the blockchain while transactions, which are not endorsed, are disregarded. An endorsement policy allows chaincodes to specify endorsers for a transaction in the form of a set of peer nodes that are necessary for endorsement. When a client sends the transaction to the peers specified in the endorsement policy, the transaction is executed to validate the transaction. After a validation, the transactions enter an ordering phase in which a consensus protocol is used to produce an ordered sequence of endorsed transactions grouped into blocks.

[00212] In the example depicted in FIG. 10, a host platform 1020 builds and deploys a machine learning model for predictive monitoring of assets 1030 (e.g., patients’ diagnosis or treatment recommendations). Here, the host platform 1020 may be a cloud platform, an industrial server, a web server, a personal computer, a user device, and the like. Assets 1030 can represent patient diagnosis or treatment parameters. The blockchain 1010 can be used to significantly improve both a training process 1002 of the machine learning model and a channelized communication predictive process 1005 based on a trained machine learning model. For example, in 1002, rather than requiring a data scientist/engineer or other user to collect the data, historical (heuristics) patients’ data may be stored by the assets 1030 themselves (or through an intermediary, not shown) on the blockchain 1010.

[00213] This can significantly reduce the collection time needed by the host platform 1020 when performing predictive model training. For example, using smart contracts, data can be directly and reliably transferred straight from its place of origin (e.g., from practitioners’ entities or patient-related communication records) to the blockchain 1010. By using the blockchain 1010 to ensure the security and ownership of the collected data, smart contracts may directly send the data from the assets to the entities that use the data for building a machine learning model. This allows for sharing of data among the assets 1030. The collected data may be stored in the blockchain 1010 based on a consensus mechanism. The consensus mechanism pulls in (permissioned nodes) to ensure that the data being recorded is verified and accurate. The data recorded is time-stamped, cryptographically signed, and immutable. It is therefore auditable, transparent, and secure.

[00214] Furthermore, training of the machine learning model on the collected data may take rounds of refinement and testing by the host platform 1020. Each round may be based on additional data (produce by monitoring of the patient-related channels) or data that was not previously considered to help expand the knowledge of the machine learning model. In 1002, the different training and testing steps (and the data associated therewith) may be stored on the blockchain 1010 by the host platform 1020. Each refinement of the machine learning model {e.g., changes in variables, weights, etc.) may be stored on the blockchain 1010 as well. This provides verifiable proof of how the model was trained and what data was used to train the model. Furthermore, when the host platform 1020 has achieved a finally trained model, the resulting model maybe stored on the blockchain 1010.

[00215] After the model has been trained, it may be deployed to a live environment where it can make patient diagnosis predictions/treatment decisions based on the execution of the final trained machine learning model. In this example, data fed back from the asset 1030 may be input into the machine learning model and may be used to make event predictions such as likelihood of nodes 703 being associated with patients’ (pivot) objects 706 (see FIGs. 7A-7C). Determinations made by the execution of the machine learning model (e.g., patients’ diagnosis, treatment plans, etc.) at the host platform 1020 maybe stored on the blockchain 1010 to provide auditable /verifiable proof. As one nonlimiting example, the machine learning model may predict a future change of a part of the asset 1030 (e.g., new patient objects/sub-objects or changes of properties of the existing patients’ objects) and create alert or a notification to reconfigure the communication channel or to add/remove a sub-channel. The data behind this decision may be stored by the host platform 1020 on the blockchain 1010.

[00216] FIGs. 11-16 show examples of user interfaces of the system for Al-based prediction of medical diagnosis consistent with the present disclosure.

[00217] FIG. 11 illustrates a UI 1100 displaying channel data for different patients including doctor name and diagnosis. [00218] FIG. 12 illustrates a UI 1200 displaying selected patient information in the window on the right of the screen.

[00219] FIG. 13 illustrates a UI 1300 displaying medical images of the selected patient. [00220] FIG. 14 illustrates a UI 1400 displaying larger patient images with annotations.

[00221] FIG. 15 illustrates a UI 1500 displaying patient channel communications among the doctors and the Al Bot.

[00222] FIG. 16 illustrates a UI 1600 displaying treatment recommendation provided by the Al Bot connected to the AI/ML module discussed above.

[00223] The proposed claims provide a new and improved system architecture that is designed to improve the functioning of computing systems at transmitting key data and information between critical objects of a multi-dimensional object-based records system. Existing architecture provides an outdated methodology for facilitating the communication of data within computing systems. Embodiments contemplate the Provision of a UI capable of providing the same.

[00224] Consistent with embodiments of the present disclosure, a communications layer may be adapted into a new or pre-existing objective-oriented data model / workflow capable database [collectively referred to as ‘Database’]. Non-limiting examples of a compatible database (as used herein) would be, for example, a record management system with clients, accounts, patients, projects, matters, and other file types, which may further include CRMs and production related work-flow capable systems. Each of the aforementioned database parameters may be referred to as data elements.

[00225] Still consistent with embodiments of the present disclosure, the communications layer may enable the composition, recordation, transmission, and receipt of messages related to various elements of the database to which the communications layer is adapted. In this instance, the communications layer may be represented as the layer through which an informative expression (i.e., a communication of information/data) is communicated in the database.

[00226] In various embodiments, the communications layer may be comprised of nodes, which corresponds to the critical objects in the database. The critical objects correspond to any element of the database for which a dialog/communication is determined to exist or have the potential to exist. The stage of determining which database elements may be construed as, or generated as, critical objects are discussed throughout the present disclosure.

[00227] Still consistent with various embodiments of the present disclosure, the nodes, in turn, may effectively serve the ‘channels’ of communication in this new communications layer which is integrated into the existing database (or formed along with the database). In this way, each node may be tied to the critical objects. It should be understood that the terms critical object and 'entities’ may refer to one another.

[00228] Still consistent with various embodiments the present disclosure, the nodes may be grouped together, or related to each other, based on the underlying relationship of the critical objects they are associated with (e.g., parent, siblings, cousins, children). With reference to a User-Interface presentation, in some embodiments, such grouping may be reflected in the UI as ‘tiers’ of nodes.

[00229] The nodes may be configured to serve as ‘relays’ of information (informative expressions) between the nodes themselves and to end-users who are interested stakeholders of the underlying objects associated therewith (e.g., data elements in the database, determined to be a critical object in the communications layer). As the underlying object undergoes a workflow process associated with the database, the communication layer may be configured to keep the stakeholders apprised. In this way, the stakeholders may be enabled to follow the object through its workflow process, as it goes from Tier to Tier, and as it becomes associated / unassociated with other entities or groups of entities.

[00230] Accordingly, in various embodiments, aspects of the present disclosure may provide at least the following. a. The integration of the communications layer into the Database forming nodes of expression (both informative expression and actionable expression) that are based on identified pivot points within the database. i. The identified pivot points corresponding to [critical] objects in the database ii. The critical objects can be any objects in the database that require discussion by humans, or present data for communication to humans (see b(ii)) b. The nodes being tied to/integrated with critical objects in the database. i. A node of informative expression is a forum for the input & output of:

1. Output of Textual & Graphical Representations related to the Database

2. Input of Parameters or Operational commands ii. The node can allow multiple types of expression:

1. Expression from a human about the node and/or the critical object associated with the node

2. Expression from the object regarding any changes made thereto a. Expression may be in human-readable terms, such as natural language b. Expression may be attributed to a human who caused the change

3. Expression from another node or critical object that affects this critical object: a. i.e., informative expressions generated by the node and presented to the human, relating to a status/activity of the underlying object or objects related thereto (or derivative expression associated therewith] c. The pivot points serving as points of data relay between the targeted objects in the database. This enables the conversation to follow the entity, as the entity travels through the workflow, and the user to engage in dialog in one place with all relevant information. i. Any entity (critical object] that humans can discuss with verbal communication, should have an additional attribute that contains dialog about the critical object associated with the dialog. ii. The Pivot Points provide access to:

1. Data and parameters associated with the entities / critical objects

2. Surrounding data I relevant data to the entities / critical objects 3. Pivot points connect key participants iii. Ensures all contextually relevant information is presented at the node, so that the user would not need to navigate through the underlying database system to obtain relevant information. d. The nodes enabling the publishing of informative expression associated with the related critical object(s) i. Wherein the system generates the informative expression to be presented within the node based on:

1. Activity within the node (e.g., changes to a property of the critical object associated with the node); and

2. Activity related to the node (e.g., changes to a property of a different critical object that affects this critical object) ii. Communicates the state of the object iii. The expression may be attributed to a human who caused a change in the critical object, but still be presented by the node itself. e. The nodes being structured into tiers based on one or more of: i. The underlying structure of the critical objects ii. One or more workflows associated with the critical objects iii. Also the chatter going on between parties f. The nodes forming a network of multi-directional channeling of the informative expression (i.e., data) i. In this way, the nodes form relays of informative expression ii. In turn, the network of nodes forms the fabric of the communication layer that provides a new-age of communication of informative expressions relating to critical objects in the Database g. Additional Stages (outside of independent): i. The identification of stakeholders for each node

1. Stakeholders may self-identify and/or maybe selected based on user activity

2. The system can identify critical objects and the associated stakeholders, and automatically add the stakeholders to the corresponding nodes ii. Providing access to the informative expressions of each node to its stakeholders. iii. Enabling the stakeholders to add expressions into the Input Port of the Node.

1. The expression being propagated through the relays in the node network) as needed a. Rules engine may be used to determine: i. when to propagate the expression; and/or ii. to which connected nodes the expression should be propagated iv. Providing alerts to the stakeholders when the node is updated (e.g., when one or more informative expressions are published).

[00231] Embodiments of the present disclosure may provide systems and methods for information to be communicated within the database and up to the stakeholders, between the stakeholders, and back down from the stakeholders into the database. The following aspects relate to, at least in part, communications layer’s process of conducting the transfer of information between the nodes and the users.

[00232] Accordingly, in various embodiments, aspects of the present disclosure may provide at least the following. a. Each node tracks its activity (e.g., activity of a critical object associated with the node) b. Each node talks to related nodes to receive updates on their activity v. Relations can be transitory

1. Relations may have set beginning and/or ending times

2. Relations may form/break, and the corresponding stakeholders can follow and ‘unfollow’ this network. vi. Relations may be identified by a user vii. Relations may be identified by the system c. Each node alerts stakeholders in a derivative data point (e.g., a related node) of associated activity of the node and related activity of the related node. [00233] Embodiments of the present disclosure may provide methods and systems for integrating the communications layer of the present disclosure into existing database systems. Non-limiting examples of a compatible systems (as used herein) would be, for example, a record management system with clients, accounts, patients, projects, matters, and other file types, which may further include CRMs and production related work-flow capable systems. Each of the aforementioned database parameters may be referred to as data elements.

[00234] Accordingly, in various embodiments, aspects of the present disclosure may provide at least the following. a. Integration into [Database] i. Purpose of this Database is Project Management ii. Integration is performed with the goal of providing a communication layer into the Database

1. Expression layer includes multiple forums for publishing the expression

2. each forum is associated with (e.g., an attribute of) a corresponding critical object b. Identifying critical objects iii. Platform studies the database to identify and extract critical objects - e.g., an object that moves through a workflow tracked by the [Database] iv. Critical objects, in turn, serve as pivot points into the expression layer that is being constructed on TOP of the [Database] v. Establishing communication pivot points associated with the identified critical objects

3. By creating the forums associated with each of the critical objects c. Giving each of the critical objects a forum of informative expression vi. The communications layer limited to these pivot points, and the users cannot generate an expression forum without first generating a critical object in the [Database] vii. The objects speak for themselves and study the system to relay what is happening 4. Objects provide status update when one or more object properties change

5. Objects may attribute their speech to a user who caused the change in the object d. Organizing the communication pivot points into classes [or ‘tiers” if more appropriate term] of communication viii. Identifying Workflows within the Project Management System ix. Because the communication pivot points are tied to the critical objects, the organization of the communication pivot points may mirror the organization of the critical objects in the [Database] e. Tracking activity related to the critical objects x. The activity being associated with:

6. The critical objects

7. Any objects related to the critical objects a. Relations may be permanent or transitory

8. A related class of critical objects a. Parent b. Sibling c. Child f. Expressing, through the forum of informative expression tied to the critical object, a derivative expression associated with the tracked activity

[00235] Accordingly, in various embodiments, aspects of the present disclosure may provide at least the following.

[00236] Embodiments of the present disclosure may provide methods and systems for optimizing production-related communication of information related to critical entities in the production.

[00237] Here, embodiments of the present disclosure may provide for the establishing a forum (or channel) of communication based on entity and various objects therein, as a matter of workflow-based entities rather than free-form channel creation. In this way, channels now spawn off the entity/objects therein, rather than free-form end-user creation of channels as provided in conventional systems.

[00238] In some embodiments, critical objects may be tied to a workflow from their conception rather than be conceived then later tied to a workflow. In further embodiments, work-flows may be generated first within a project management solution or other computing platform. In turn, the communications lawyer may establish nodes that spawn based on the critical entities/objects identified to be within those workflows. In this way, those certain parameters of the workflow (e.g., entities/objects) serve as the pivot points for the communication forums.

[00239] Still consistent with various embodiments, a stake holder of a critical object can be informed on the workflow’s overall [state of operation / status updates / relevant communication] from the perspective of the critical object they follow. Thus, a stakeholder may be enabled to view the entire workflow from the relevant perspective of the critical object they follow.

[00240] Aspects of the embodiments disclosed herein present disclosure eliminate the conventional need to:

• create channels of communication

• organize channels of communication

• track channels of communication

• integrate channels of communication with Project Management Workflow

• update the channels of communication with updates in corresponding Project Management Workflows

• receive messages that a stakeholder does not need

[00241] Aspects of the embodiments herein provide such technical advantages by, for example, but not limited to:

1. Connecting to a Project Management System / EHR I Accounting Systems

2. Identifying Entities/Objects in the System

3. Identifying Stakeholders

4. Tracking their Workflows within the System

5. Spawning Forums of Communication based on the Pivot Points (Entities within workflows)

6. Updating (via, for example, notifications) the Forums based on the Entities progress

7. Integrating Al into the process

[00242] Embodiments of the present disclosure may provide a user interface (UI). Conventional systems provide a Dashboard UI of Channels for a user to participate in. In contrast, and according to various embodiments, a user rather need to only select critical objects to follow, and the corresponding channel notifications will be provided dynamically to the user in a user interface. This, in turn, eliminates the need for a user to choose and follow channels. Rather, all the relevant messages applicable to objects that the user follows is provided to a user in an inbox-like interface. Since the messages are provided to the user, the user no longer needs to access an object record and search for a communication thread related thereto.

[00243] Accordingly, in various embodiments, aspects of the present disclosure may provide at least the following. a) Enabling users to join/follow forums based on their stake /interest in an object or entity. a. Instead of users joining channels, users not follow objects /entities - and then, are notified of the updates related thereto. b. the channels of communication that they interface with are automatically provided to them based on the progress of the object/entity that they follow. b) Provide an interface for managing these communications generated by the system in following the entities through the workflows. c) Connecting to a Project Management software to identify 'entities'. These 'entities' serve as 'channels.' Channel creation is disclosed throughout the present disclosure. d) Tiered Organization - The interface is organized by tiers (nugget, sprint, project, build, etc.).

[00244] Accordingly, in various embodiments, aspects of the present disclosure may provide at least the following. a. A user may be enable d to identify a node to join, or auto-assigned to a node, viii. Search for Objects within the Database ix. Subscribe to the Nodes of the Communication Layer associated with those Objects x. As the subscribed objects tie into other classes (tiers) then, in turn, the user may get subscriptions to those nodes as well.

1. May receive all communications from the related objects, or only those which affect the object to which the user is subscribed 2. Use of rules engine to determine when user receives notifications form related objects xi. As the object ties are broken, then, in turn, user loses their subscription to those nodes. xii. Adding users to a channel based on their relationship to the critical entity.

[00245] In this way, if the original entity a user is following gets associated with another entity, embodiments may enable the user to auto-follow that other entity, when it's relevant (using, for example, Al - and machine learning],

[00246] The above embodiments of the present disclosure may be implemented in hardware, in a computer-readable instructions executed by a processor, in firmware, or in a combination of the above. The computer computer-readable instructions may be embodied on a computer-readable medium, such as a storage medium. For example, the computer computer-readable instructions may reside in random access memory ("RAM”), flash memory, read-only memory ("ROM”), erasable programmable read-only memory ("EPROM”), electrically erasable programmable read-only memory ("EEPROM”), registers, hard disk, a removable disk, a compact disk read-only memory ("CD-ROM”), or any other form of storage medium known in the art.

[00247] An exemplary storage medium may be coupled to the processor such that the processor may read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application specific integrated circuit ("ASIC”). In the alternative embodiment, the processor and the storage medium may reside as discrete components. For example, FIG. 17 illustrates an example computing device (e.g., a server node) 1700, which may represent or be integrated in any of the above-described components, etc.

[00248] FIG. 17 illustrates a block diagram of a system including computing device 1700. The computing device 1700 may comprise, but not be limited to the following: [00249] Mobile computing device, such as, but is not limited to, a laptop, a tablet, a smartphone, a drone, a wearable, an embedded device, a handheld device, an Arduino, an industrial device, or a remotely operable recording device;

[00250] A supercomputer, an exa-scale supercomputer, a mainframe, or a quantum computer; [00251] A minicomputer, wherein the minicomputer computing device comprises, but is not limited to, an IBM AS500 / iSeries / System I, A DEC VAX / PDP, a HP3000, a Honeywell-Bull DPS, a Texas Instruments TI-990, or a Wang Laboratories VS Series;

[00252] A microcomputer, wherein the microcomputer computing device comprises, but is not limited to, a server, wherein a server may be rack mounted, a workstation, an industrial device, a raspberry pi, a desktop, or an embedded device;

[00253] The channel server node 102 (see FIGs. 2A-C) and/or DS node 702 (See FIGs. 7A-C)may be hosted on a centralized server or on a cloud computing service. Although the above-described methods are described as being performed by the channel server node 102 implemented on a computing device 1700, it should be understood that, in some embodiments, different operations may be performed by a plurality of the computing devices 1700 in operative communication at least one network.

[00254] Embodiments of the present disclosure may comprise a computing device having a central processing unit (CPU) 1720, a bus 1730, a memory unit 1740, a power supply unit (PSU) 1750, and one or more Input / Output (I/O) units. The CPU 1720 coupled to the memory unit 1740 and the plurality of I/O units 1760 via the bus 1730, all of which are powered by the PSU 1750. It should be understood that, in some embodiments, each disclosed unit may actually be a plurality of such units for the purposes of redundancy, high availability, and/or performance. The combination ofthe presently disclosed units is configured to perform the stages any method disclosed herein.

[00255] Consistent with an embodiment of the disclosure, the aforementioned CPU 1720, the bus 1730, the memory unit 1750, a PSU 1750, and the plurality of I/O units 1760 may be implemented in a computing device, such as computing device 1700. Any suitable combination of hardware, software, or firmware may be used to implement the aforementioned units. For example, the CPU 1720, the bus 1730, and the memory unit 1750 may be implemented with computing device 1700 or any of other computing devices 1700, in combination with computing device 1700. The aforementioned system, device, and components are examples and other systems, devices, and components may comprise the aforementioned CPU 1720, the bus 1730, the memory unit 1750, consistent with embodiments ofthe disclosure.

[00256] At least one computing device 1700 may be embodied as any of the computing elements illustrated in all of the attached figures, including the channel node 102 (FIG. 2A-C). A computing device 1700 does not need to be electronic, nor even have a CPU 1720, nor bus 1730, nor memory unit 1740. The definition of the computing device 1700 to a person having ordinary skill in the art is "A device that computes, especially a programmable [usually] electronic machine that performs high-speed mathematical or logical operations or that assembles, stores, correlates, or otherwise processes information." Any device which processes information qualifies as a computing device 1700, especially if the processing is purposeful.

[00257] With reference to FIG. 17, a system consistent with an embodiment of the disclosure may include a computing device, such as computing device 1700. In a basic configuration, computing device 1700 may include at least one clock module 1710, at least one CPU 1720, at least one bus 1730, and at least one memory unit 1740, at least one PSU 1750, and atleast one I/O 1760 module, wherein I/O module may be comprised of, but not limited to a non-volatile storage sub-module 1761, a communication submodule 1762, a sensors sub-module 1763, and a peripherals sub-module 1765.

[00258] A system consistent with an embodiment of the disclosure the computing device 1700 may include the clock module 1710 may be known to a person having ordinary skill in the art as a clock generator, which produces clock signals. Clock signal is a particular type of signal that oscillates between a high and a low state and is used like a metronome to coordinate actions of digital circuits. Most integrated circuits [ICs] of sufficient complexity use a clock signal in order to synchronize different parts of the circuit, cycling at a rate slower than the worst-case internal propagation delays. The preeminent example of the aforementioned integrated circuit is the CPU 1720, the central component of modern computers, which relies on a clock. The only exceptions are asynchronous circuits such as asynchronous CPUs. The clock 1710 can comprise a plurality of embodiments, such as, but not limited to, single-phase clock which transmits all clock signals on effectively 1 wire, two-phase clock which distributes clock signals on two wires, each with non-overlapping pulses, and four-phase clock which distributes clock signals on 5 wires.

[00259] Many computing devices 1700 use a "clock multiplier" which multiplies a lower frequency external clock to the appropriate clock rate of the CPU 1720. This allows the CPU 1720 to operate at a much higher frequency than the rest of the computer, which affords performance gains in situations where the CPU 1720 does not need to wait on an external factor [like memory 1740 or input/output 1760], Some embodiments of the clock 1710 may include dynamic frequency change, where, the time between clock edges can vary widely from one edge to the next and back again.

[00260] A system consistent with an embodiment of the disclosure the computing device 1700 may include the CPU unit 1720 comprising at least one CPU Core 1721. A plurality of CPU cores 1721 may comprise identical CPU cores 1721, such as, but not limited to, homogeneous multi-core systems. It is also possible for the plurality of CPU cores 1721 to comprise different CPU cores 1721, such as, but not limited to, heterogeneous multi-core systems, big.LITTLE systems and some AMD accelerated processing units (APU). The CPU unit 1720 reads and executes program instructions which may be used across many application domains, for example, but not limited to, general purpose computing, embedded computing, network computing, digital signal processing (DSP), and graphics processing (GPU). The CPU unit 1720 may run multiple instructions on separate CPU cores 1721 at the same time. The CPU unit 1720 may be integrated into at least one of a single integrated circuit die and multiple dies in a single chip package. The single integrated circuit die and multiple dies in a single chip package may contain a plurality of other aspects of the computing device 1700, for example, but not limited to, the clock 1710, the CPU 1720, the bus 1730, the memory 1750, and I/O 1760.

[00261] The CPU unit 1720 may contain cache 1722 such as, but not limited to, a level 1 cache, level 2 cache, level 3 cache or combination thereof. The aforementioned cache 1722 may or may not be shared amongst a plurality of CPU cores 1721. The cache 1722 sharing comprises at least one of message passing and inter-core communication methods may be used for the at least one CPU Core 1721 to communicate with the cache 1722. The inter-core communication methods may comprise, but not limited to, bus, ring, two-dimensional mesh, and crossbar. The aforementioned CPU unit 1720 may employ symmetric multiprocessing (SMP) design.

[00262] The plurality of the aforementioned CPU cores 1721 may comprise soft microprocessor cores on a single field programmable gate array (FPGA), such as semiconductor intellectual property cores (IP Core). The plurality of CPU cores 1721 architecture may be based on at least one of, but not limited to, Complex instruction set computing (CISC), Zero instruction set computing (ZISC), and Reduced instruction set computing (RISC). At least one of the performance-enhancing methods may be employed by the plurality of the CPU cores 1721, for example, but not limited to Instruction-level parallelism (ILP) such as, but not limited to, superscalar pipelining, and Thread-level parallelism (TLP).

[00263] Consistent with the embodiments of the present disclosure, the aforementioned computing device 1700 may employ a communication system that transfers data between components inside the aforementioned computing device 1700, and/or the plurality of computing devices 1700. The aforementioned communication system will be known to a person having ordinary skill in the art as a bus 1730. The bus 1730 may embody internal and/or external plurality of hardware and software components, for example, but not limited to a wire, optical fiber, communication protocols, and any physical arrangement that provides the same logical function as a parallel electrical bus. The bus 1730 may comprise at least one of, but not limited to a parallel bus, wherein the parallel bus carry data words in parallel on multiple wires, and a serial bus, wherein the serial bus carry data in bit-serial form. The bus 1730 may embody a plurality of topologies, for example, but not limited to, a multidrop / electrical parallel topology, a daisy chain topology, and a connected by switched hubs, such as USB bus. The bus 1730 may comprise a plurality of embodiments, for example, but not limited to:

[00264] - Internal data bus (data bus) 1731 / Memory bus

[00265] - Control bus 1732

[00266] - Address bus 1733

[00267] - System Management Bus (SMBus)

[00268] - Front-Side-Bus (FSB)

[00269] - External Bus Interface (EBI)

[00270] - Local bus

[00271] - Expansion bus

[00272] - Lightning bus

[00273] - Controller Area Network (CAN bus)

[00274] - Camera Link

[00275] - ExpressCard

[00276] -Advanced Technology management Attachment (ATA), including embodiments and derivatives such as, but not limited to, Integrated Drive Electronics (IDE) / Enhanced IDE (EIDE), ATA Packet Interface (ATAPI), Ultra-Direct Memory Access (UDMA), Ultra ATA (UATA) / Parallel ATA (PATA) / Serial ATA (SATA), CompactFlash (CF) interface, Consumer Electronics ATA (CE-ATA) / Fiber Attached Technology Adapted (FATA), Advanced Host Controller Interface (AHCI), SATA Express (SATAe) / External SATA (eSATA), including the powered embodiment eSATAp / Mini-SATA (mSATA), and Next Generation Form Factor (NGFF) / M.2.

[00277] -Small Computer System Interface (SCSI) / Serial Attached SCSI (SAS)

[00278] -HyperTransport

[00279] -InfiniBand

[00280] - RapidlO

[00281] - Mobile Industry Processor Interface (MIPI)

[00282] - Coherent Processor Interface (CAPI)

[00283] - Plug-n-play

[00284] -1-Wire

[00285] - Peripheral Component Interconnect (PCI), including embodiments such as, but not limited to, Accelerated Graphics Port (AGP), Peripheral Component Interconnect extended (PCI-X), Peripheral Component Interconnect Express (PCI-e) (e.g., PCI Express Mini Card, PCI Express M.2 [Mini PCIe v2], PCI Express External Cabling [ePCIe], and PCI Express OCuLink [Optical Copper{Cu} Link]), Express Card, AdvancedTCA, AMC, Universal 10, Thunderbolt / Mini DisplayPort, Mobile PCIe (M-PCIe), U.2, and Non- Volatile Memory Express (NVMe) / Non-Volatile Memory Host Controller Interface Specification (NVMHCIS).

[00286] - Industry Standard Architecture (ISA), including embodiments such as, but not limited to Extended ISA (EISA), PC/XT -bus / PC/AT-bus I PC/105 bus (e.g., PC/105- Plus, PCI/105-Express, PCI/105, and PCI-105), and Low Pin Count (LPC).

[00287] - Music Instrument Digital Interface (MIDI)

[00288] - Universal Serial Bus (USB), including embodiments such as, but not limited to, Media Transfer Protocol (MTP) / Mobile High-Definition Link (MHL), Device Firmware Upgrade (DFU), wireless USB, InterChip USB, IEEE 1395 Interface / Firewire, Thunderbolt, and extensible Host Controller Interface (xHCI).

[00289] Consistent with the embodiments of the present disclosure, the aforementioned computing device 1700 may employ hardware integrated circuits that store information for immediate use in the computing device 1700, know to the person having ordinary skill in the art as primary storage or memory 1740. The memory 1740 operates at high speed, distinguishing it from the non-volatile storage sub-module 1761, which may be referred to as secondary or tertiary storage, which provides slow-to-access information but offers higher capacities at lower cost. The contents contained in memory 1740, maybe transferred to secondary storage via techniques such as, but not limited to, virtual memory and swap. The memory 1740 may be associated with addressable semiconductor memory, such as integrated circuits consisting of silicon-based transistors, used for example as primary storage but also other purposes in the computing device 1700. The memory 1740 may comprise a plurality of embodiments, such as, but not limited to volatile memory, non-volatile memory, and semi-volatile memory. It should be understood by a person having ordinary skill in the art that the ensuing are non-limiting examples of the aforementioned memory:

[00290] - Volatile memory which requires power to maintain stored information, for example, but not limited to, Dynamic Random-Access Memory (DRAM) 551, Static Random-Access Memory (SRAM) 1752, CPU Cache memory 1725, Advanced Random- Access Memory (A-RAM), and other types of primary storage such as Random-Access Memory (RAM).

[00291] - Non-volatile memory which can retain stored information even after power is removed, for example, but not limited to, Read-Only Memory (ROM) 1753, Programmable ROM (PROM) 1755, Erasable PROM (EPROM) 1755, Electrically Erasable PROM (EEPROM) 1756 (e.g., flash memory and Electrically Alterable PROM [EAPROM]), Mask ROM (MROM), One Time Programable (OTP) ROM / Write Once Read Many (WORM), Ferroelectric RAM (FeRAM), Parallel Random-Access Machine (PRAM), Split- Transfer Torque RAM (STT-RAM), Silicon Oxime Nitride Oxide Silicon (SONOS), Resistive RAM (RRAM), Nano RAM (NRAM), 3D XPoint, Domain-Wall Memory (DWM), and millipede memory.

[00292] - Semi-volatile memory which may have some limited non-volatile duration after power is removed but loses data after said duration has passed. Semi-volatile memory provides high performance, durability, and other valuable characteristics typically associated with volatile memory, while providing some benefits of true nonvolatile memory. The semi-volatile memory may comprise volatile and non-volatile memory and/or volatile memory with battery to provide power after power is removed. The semi-volatile memory may comprise, but not limited to spin-transfer torque RAM (STT-RAM). [00293] - Consistent with the embodiments of the present disclosure, the aforementioned computing device 1700 may employ the communication system between an information processing system, such as the computing device 1700, and the outside world, for example, but not limited to, human, environment, and another computing device 1700. The aforementioned communication system will be known to a person having ordinary skill in the art as I/O 1760. The I/O module 1760 regulates a plurality of inputs and outputs with regard to the computing device 1700, wherein the inputs are a plurality of signals and data received by the computing device 1700, and the outputs are the plurality of signals and data sent from the computing device 1700. The I/O module 1760 interfaces a plurality of hardware, such as, but not limited to, nonvolatile storage 1761, communication devices 1762, sensors 1763, and peripherals 1765. The plurality of hardware is used by the at least one of, but not limited to, human, environment, and another computing device 1700 to communicate with the present computing device 1700. The I/O module 1760 may comprise a plurality of forms, for example, but not limited to channel I/O, port mapped I/O, asynchronous I/O, and Direct Memory Access (DMA).

[00294] - Consistent with the embodiments of the present disclosure, the aforementioned computing device 1700 may employ the non-volatile storage submodule 1761, which maybe referred to by a person having ordinary skill in the art as one of secondary storage, external memory, tertiary storage, off-line storage, and auxiliary storage. The non-volatile storage sub-module 1761 may not be accessed directly by the CPU 1720 without using intermediate area in the memory 1740. The non-volatile storage sub-module 1761 does not lose data when power is removed and may be two orders of magnitude less costly than storage used in memory module, at the expense of speed and latency. The non-volatile storage sub-module 1761 may comprise a plurality of forms, such as, but not limited to, Direct Attached Storage (DAS), Network Attached Storage (NAS), Storage Area Network (SAN), nearline storage, Massive Array of Idle Disks (MAID), Redundant Array of Independent Disks (RAID), device mirroring, off-line storage, and robotic storage. The non-volatile storage sub-module (461) may comprise a plurality of embodiments, such as, but not limited to:

[00295] - Optical storage, for example, but not limited to, Compact Disk (CD) (CD-ROM I CD-R / CD-RW), Digital Versatile Disk (DVD) (DVD-ROM / DVD-R / DVD+R / DVD-RW / DVD+RW / DVD±RW I DVD+R DL I DVD-RAM / HD-DVD), Blu-ray Disk (BD) (BD-ROM I BD-R / BD-RE / BD-R DL / BD-RE DL), and Ultra-Density Optical (UDO).

[00296] - Semiconductor storage, for example, but not limited to, flash memory, such as, but not limited to, USB flash drive, Memory card, Subscriber Identity Module (SIM) card, Secure Digital (SD) card, Smart Card, CompactFlash (CF) card, Solid-State Drive (SSD) and memristor.

[00297] - Magnetic storage such as, but not limited to, Hard Disk Drive (HDD), tape drive, carousel memory, and Card Random-Access Memory (CRAM).

[00298] - Phase-change memory

[00299] - Holographic data storage such as Holographic Versatile Disk (HVD).

[00300] - Molecular Memory

[00301] - Deoxyribonucleic Acid (DNA) digital data storage

[00302] Consistent with the embodiments of the present disclosure, the aforementioned computing device 1700 may employ the communication sub-module 1762 as a subset of the I/O 1760, which may be referred to by a person having ordinary skill in the art as at least one of, but not limited to, computer network, data network, and network. The network allows computing devices 1700 to exchange data using connections, which may be known to a person having ordinary skill in the art as data links, between network nodes. The nodes comprise network computer devices 1700 that originate, route, and terminate data. The nodes are identified by network addresses and can include a plurality of hosts consistent with the embodiments of a computing device 1700. The aforementioned embodiments include, but not limited to personal computers, phones, servers, drones, and networking devices such as, but not limited to, hubs, switches, routers, modems, and firewalls.

[00303] Two nodes can be said are networked together, when one computing device 1700 is able to exchange information with the other computing device 1700, whether or not they have a direct connection with each other. The communication sub-module 1762 supports a plurality of applications and services, such as, but not limited to World Wide Web (WWW), digital video and audio, shared use of application and storage computing devices 1700, printers/scanners/fax machines, email/online chat/instant messaging, remote control, distributed computing, etc. The network may comprise a plurality of transmission mediums, such as, but not limited to conductive wire, fiber optics, and wireless. The network may comprise a plurality of communications protocols to organize network traffic, wherein application-specific communications protocols are layered, may be known to a person having ordinary skill in the art as carried as payload, over other more general communications protocols. The plurality of communications protocols may comprise, but not limited to, IEEE 802, ethernet, Wireless LAN (WLAN / Wi-Fi), Internet Protocol (IP) suite (e.g., TCP/IP, UDP, Internet Protocol version 5 [IPv6], and Internet Protocol version 6 [IPv6]), Synchronous Optical Networking (SONET)/Synchronous Digital Hierarchy (SDH), Asynchronous Transfer Mode (ATM), and cellular standards (e.g., Global System for Mobile Communications [GSM], General Packet Radio Service [GPRS], Code-Division Multiple Access [CDMA], and Integrated Digital Enhanced Network [IDEN]).

[00304] The communication sub-module 1762 may comprise a plurality of size, topology, traffic control mechanism and organizational intent. The communication submodule 1762 may comprise a plurality of embodiments, such as, but not limited to: [00305] - Wired communications, such as, but not limited to, coaxial cable, phone lines, twisted pair cables (ethernet), and InfiniBand.

[00306] - Wireless communications, such as, but not limited to, communications satellites, cellular systems, radio frequency I spread spectrum technologies, IEEE 802.11 Wi-Fi, Bluetooth, NFC, free-space optical communications, terrestrial microwave, and Infrared (IR) communications. Wherein cellular systems embody technologies such as, but not limited to, 3G,5G (such as WiMax and LTE), and 5G (short and long wavelength). [00307] - Parallel communications, such as, but not limited to, LPT ports.

[00308] - Serial communications, such as, but not limited to, RS-232 and USB.

[00309] - Fiber Optic communications, such as, but not limited to, Single-mode optical fiber (SMF) and Multi-mode optical fiber (MMF).

[00310] - Power Line and wireless communications

[00311] The aforementioned network may comprise a plurality of layouts, such as, but not limited to, bus network such as ethernet, star network such as Wi-Fi, ring network, mesh network, fully connected network, and tree network. The network can be characterized by its physical capacity or its organizational purpose. Use of the network, including user authorization and access rights, differ accordingly. The characterization may include, but not limited to nanoscale network, Personal Area Network (PAN), Local Area Network (LAN), Home Area Network (HAN), Storage Area Network (SAN), Campus Area Network (CAN), backbone network, Metropolitan Area Network (MAN), Wide Area Network (WAN), enterprise private network, Virtual Private Network (VPN), and Global Area Network (GAN).

[00312] Consistent with the embodiments of the present disclosure, the aforementioned computing device 1700 may employ the sensors sub-module 1763 as a subset of the I/O 1760. The sensors sub-module 1763 comprises at least one of the devices, modules, and subsystems whose purpose is to detect events or changes in its environment and send the information to the computing device 1700. Sensors are sensitive to the measured property, are not sensitive to any property not measured, but may be encountered in its application, and do not significantly influence the measured property. The sensors sub-module 1763 may comprise a plurality of digital devices and analog devices, wherein if an analog device is used, an Analog to Digital (A-to-D) converter mustbe employed to interface the said device with the computing device 1700. The sensors may be subject to a plurality of deviations that limit sensor accuracy. The sensors sub-module 1763 may comprise a plurality of embodiments, such as, but not limited to, chemical sensors, automotive sensors, acoustic/ sound/vibration sensors, electric current/electric potential/magnetic/radio sensors, environmental/ weather/moisture/humidity sensors, flow/fluid velocity sensors, ionizing radiation/ particle sensors, navigation sensors, position/angle/displacement/distance/speed/ acceleration sensors, imaging/optical/light sensors, pressure sensors, force/density/level sensors, thermal/temperature sensors, and proximity/presence sensors. It should be understood by a person having ordinary skill in the art that the ensuing are non-limiting examples of the aforementioned sensors:

[00313] Chemical sensors, such as, but not limited to, breathalyzer, carbon dioxide sensor, carbon monoxide/smoke detector, catalytic bead sensor, chemical field-effect transistor, chemiresistor, electrochemical gas sensor, electronic nose, electrolyte- insulator-semiconductor sensor, energy-dispersive X-ray spectroscopy, fluorescent chloride sensors, holographic sensor, hydrocarbon dew point analyzer, hydrogen sensor, hydrogen sulfide sensor, infrared point sensor, ion-selective electrode, nondispersive infrared sensor, microwave chemistry sensor, nitrogen oxide sensor, olfactometer, optode, oxygen sensor, ozone monitor, pellistor, pH glass electrode, potentiometric sensor, redox electrode, zinc oxide nanorod sensor, and biosensors (such as nanosensors). [00314] Automotive sensors, such as, but not limited to, air flow meter/mass airflow sensor, air-fuel ratio meter, AFR sensor, blind spot monitor, engine coolant/exhaust gas/ cylinder head/transmission fluid temperature sensor, hall effect sensor, wheel/automatic transmission/turbine/vehicle speed sensor, airbag sensors, brake fluid/engine crankcase/fuel/oil/tire pressure sensor, camshaft/crankshaft/throttle position sensor, fuel /oil level sensor, knock sensor, light sensor, MAP sensor, oxygen sensor [o2], parking sensor, radar sensor, torque sensor, variable reluctance sensor, and water-in-fuel sensor.

[00315] - Acoustic, sound and vibration sensors, such as, but not limited to, microphone, lace sensor (guitar pickup], seismometer, sound locator, geophone, and hydrophone.

[00316] - Electric current, electric potential, magnetic, and radio sensors, such as, but not limited to, current sensor, Daly detector, electroscope, electron multiplier, faraday cup, galvanometer, hall effect sensor, hall probe, magnetic anomaly detector, magnetometer, magnetoresistance, MEMS magnetic field sensor, metal detector, planar hall sensor, radio direction finder, and voltage detector.

[00317] - Environmental, weather, moisture, and humidity sensors, such as, but not limited to, actinometer, air pollution sensor, bedwetting alarm, ceilometer, dew warning, electrochemical gas sensor, fish counter, frequency domain sensor, gas detector, hook gauge evaporimeter, humistor, hygrometer, leaf sensor, lysimeter, pyranometer, pyrgeometer, psychrometer, rain gauge, rain sensor, seismometers, SNOTEL, snow gauge, soil moisture sensor, stream gauge, and tide gauge.

[00318] - Flow and fluid velocity sensors, such as, but not limited to, air flow meter, anemometer, flow sensor, gas meter, mass flow sensor, and water meter.

[00319] - Ionizing radiation and particle sensors, such as, but not limited to, cloud chamber, Geiger counter, Geiger-Muller tube, ionization chamber, neutron detection, proportional counter, scintillation counter, semiconductor detector, and thermoluminescent dosimeter.

[00320] - Navigation sensors, such as, but not limited to, air speed indicator, altimeter, attitude indicator, depth gauge, fluxgate compass, gyroscope, inertial navigation system, inertial reference unit, magnetic compass, MHD sensor, ring laser gyroscope, turn coordinator, variometer, vibrating structure gyroscope, and yaw rate sensor. [00321] - Position, angle, displacement, distance, speed, and acceleration sensors, such as, but not limited to, accelerometer, displacement sensor, flex sensor, free fall sensor, gravimeter, impact sensor, laser rangefinder, LIDAR, odometer, photoelectric sensor, position sensor such as, but not limited to, GPS or Glonass, angular rate sensor, shock detector, ultrasonic sensor, tilt sensor, tachometer, ultra-wideband radar, variable reluctance sensor, and velocity receiver.

[00322] - Imaging, optical and light sensors, such as, but not limited to, CMOS sensor, LiDAR, multi-spectral light sensor, colorimeter, contact image sensor, electro-optical sensor, infra-red sensor, kinetic inductance detector, LED as light sensor, light- addressable potentiometric sensor, Nichols radiometer, fiber-optic sensors, optical position sensor, thermopile laser sensor, photodetector, photodiode, photomultiplier tubes, phototransistor, photoelectric sensor, photoionization detector, photomultiplier, photoresistor, photoswitch, phototube, scintillometer, Shack-Hartmann, single-photon avalanche diode, superconducting nanowire single-photon detector, transition edge sensor, visible light photon counter, and wavefront sensor.

[00323] - Pressure sensors, such as, but not limited to, barograph, barometer, boost gauge, bourdon gauge, hot filament ionization gauge, ionization gauge, McLeod gauge, Oscillating U-tube, permanent downhole gauge, piezometer, Pirani gauge, pressure sensor, pressure gauge, tactile sensor, and time pressure gauge.

[00324] - Force, Density, and Level sensors, such as, but not limited to, bhangmeter, hydrometer, force gauge or force sensor, level sensor, load cell, magnetic level or nuclear density sensor or strain gauge, piezo-capacitive pressure sensor, piezoelectric sensor, torque sensor, and viscometer.

[00325] - Thermal and temperature sensors, such as, but not limited to, bolometer, bimetallic strip, calorimeter, exhaust gas temperature gauge, flame detection / pyrometer, Gardon gauge, Golay cell, heat flux sensor, microbolometer, microwave radiometer, net radiometer, infrared / quartz I resistance thermometer, silicon bandgap temperature sensor, thermistor, and thermocouple.

[00326] - Proximity and presence sensors, such as, but not limited to, alarm sensor, doppler radar, motion detector, occupancy sensor, proximity sensor, passive infrared sensor, reed switch, stud finder, triangulation sensor, touch switch, and wired glove.

[00327] Consistent with the embodiments of the present disclosure, the aforementioned computing device 1700 may employ the peripherals sub-module 1762 as a subsetofthe I/O 1760. The peripheral sub-module 1765 comprises ancillary devices uses to put information into and get information out of the computing device 1700. There are 3 categories of devices comprising the peripheral sub-module 1765, which exist based on their relationship with the computing device 1700, input devices, output devices, and input / output devices. Input devices send at least one of data and instructions to the computing device 1700. Input devices can be categorized based on, but not limited to:

[00328] - Modality of input, such as, but not limited to, mechanical motion, audio, visual, and tactile.

[00329] - Whether the input is discrete, such as but not limited to, pressing a key, or continuous such as, but not limited to position of a mouse.

[00330] - The number of degrees of freedom involved, such as, but not limited to, two- dimensional mice vs three-dimensional mice used for Computer-Aided Design (CAD) applications.

[00331] Output devices provide output from the computing device 1700. Output devices convert electronically generated information into a form that can be presented to humans. Input /output devices perform that perform both input and output functions. It should be understood by a person having ordinary skill in the art that the ensuing are non-limiting embodiments ofthe aforementioned peripheral sub-module 1765: [00332] Input Devices

[00333] - Human Interface Devices (HID), such as, but not limited to, pointing device

(e.g., mouse, touchpad, joystick, touchscreen, game controller I gamepad, remote, light pen, light gun, Wii remote, jog dial, shuttle, and knob), keyboard, graphics tablet, digital pen, gesture recognition devices, magnetic ink character recognition, Sip-and-Puff (SNP) device, and Language Acquisition Device (LAD).

[00334] - High degree of freedom devices, that require up to six degrees of freedom such as, but not limited to, camera gimbals, Cave Automatic Virtual Environment (CAVE), and virtual reality systems.

[00335] - Video Input devices are used to digitize images or video from the outside world into the computing device 1700. The information can be stored in a multitude of formats depending on the user's requirement. Examples of types of video input devices include, but not limited to, digital camera, digital camcorder, portable media player, webcam, Microsoft Kinect, image scanner, fingerprint scanner, barcode reader, 3D scanner, laser rangefinder, eye gaze tracker, computed tomography, magnetic resonance imaging, positron emission tomography, medical ultrasonography, TV tuner, and iris scanner.

[00336] - Audio input devices are used to capture sound. In some cases, an audio output device can be used as an input device, in order to capture produced sound. Audio input devices allow a user to send audio signals to the computing device 1700 for at least one of processing, recording, and carrying out commands. Devices such as microphones allow users to speak to the computer in order to record a voice message or navigate software. Aside from recording, audio input devices are also used with speech recognition software. Examples of types of audio input devices include, but not limited to microphone, Musical Instrumental Digital Interface [MIDI] devices such as, but not limited to a keyboard, and headset.

[00337] - Data Acquisition (DAQ) devices convert at least one of analog signals and physical parameters to digital values for processing by the computing device 1700. Examples of DAQ devices may include, but not limited to, Analog to Digital Converter (ADC), data logger, signal conditioning circuitry, multiplexer, and Time to Digital Converter (TDC).

[00338] Output Devices may further comprise, but not be limited to:

[00339] -Display devices, which convert electrical information into visual form, such as, but not limited to, monitor, TV, projector, and Computer Output Microfilm (COM). Display devices can use a plurality of underlying technologies, such as, but not limited to, Cathode-Ray Tube (CRT), Thin-Film Transistor (TFT), Liquid Crystal Display (LCD), Organic Light-Emitting Diode (OLED), MicroLED, E Ink Display (ePaper) and Refreshable Braille Display (Braille Terminal).

[00340] Printers, such as, but not limited to, inkjet printers, laser printers, 3D printers, solid ink printers and plotters.

[00341] - Audio and Video (AV) devices, such as, but not limited to, speakers, headphones, amplifiers and lights, which include lamps, strobes, DJ lighting, stage lighting, architectural lighting, special effect lighting, and lasers.

[00342] - Other devices such as Digital to Analog Converter (DAC)

[00343] Input/Output Devices may further comprise, but not be limited to, touchscreens, networking device (e.g., devices disclosed in network 1762 sub-module), data storage device (non-volatile storage 1761), facsimile (FAX), and graphics / sound cards.

[00344] All rights including copyrights in the code included herein are vested in and the property of the Applicant. The Applicant retains and reserves all rights in the code included herein, and grants permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose.

[00345] While the specification includes examples, the disclosure’s scope is indicated by the following claims. Furthermore, while the specification has been described in language specific to structural features and/or methodological acts, the claims are not limited to the features or acts described above. Rather, the specific features and acts described above are disclosed as examples for embodiments of the disclosure.

[00346] Insofar as the description above and the accompanying drawing disclose any additional subject matter that is not within the scope of the claims below, the disclosures are not dedicated to the public and the right to file one or more applications to claims such additional disclosures is reserved.