AI-BASED SYSTEM AND METHOD FOR PREDICTION OF MEDICAL DIAGNOSIS

An AI-based system for providing a patient 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.

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Description
RELATED APPLICATIONS

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

This patent application is related to U.S. application Ser. No. 18/154,426 filed on Jan. 13, 2023, which claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Application No. 63/390,546 filed on Jul. 19, 2022, entitled “System and Method for Establishing Channelized Communications and Resource Management”, (Attorney Docket No.: 01666.004-PA-USN-2DC), and having inventors in common, which are incorporated herein by reference in its entirety.

This patent application is related to U.S. application Ser. No. 18/154,534 filed on Jan. 13, 2023, which claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Application No. 63/390,546 filed on Jul. 19, 2022, entitled “AI-Based System and Method for Establishing Channelized Communications”, (Attorney Docket No.: 01666.005-PA-USN-2DC), and having inventors in common, which are incorporated herein by reference in its entirety.

This patent application is related to U.S. application Ser. No. 18/154,737 filed on Jan. 13, 2023, which claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Application No. 63/390,546 filed on Jul. 19, 2022, entitled “System and Methods for Establishing and Rendering Channelized Communication Model”, (Attorney Docket No.: 01666.006-PA-USN-2DC), and having inventors in common, which are incorporated herein by reference in its entirety.

It is intended that the referenced application may be 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

The present disclosure generally relates to health organizations communications, and more particularly, to an intelligent automated system for predicting a medical diagnosis over channelized communications.

BACKGROUND

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 note, 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.

Accordingly, an AI-based automated system and method for prediction of a medical diagnosis over channelized communications withing healthcare organization are desired.

BRIEF OVERVIEW

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.

One embodiment of the present disclosure provides an AI-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.

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 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.

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.

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

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.

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

FIG. 1A illustrates a network diagram of an AI-based automated medical diagnosis prediction system, consistent with the present disclosure;

FIG. 1B illustrates a network diagram of an AI-based automated medical diagnosis prediction system implemented over a blockchain network, consistent with the present disclosure;

FIG. 1C illustrates a network diagram of an AI-based automated medical diagnosis prediction system within channelized communications of a healthcare organization, consistent with the present disclosure;

FIG. 2 illustrates a network diagram of a system including detailed features of a diagnosis server node consistent with the present disclosure;

FIG. 3A illustrates a flowchart of a method for AI-based prediction of medical diagnosis consistent with the present disclosure;

FIG. 3B illustrates a further flow chart of a method for AI-based prediction of medical diagnosis consistent with the present disclosure;

FIG. 4 illustrates deployment of a machine learning model for predictive medical diagnosis within channelized communications based on blockchain assets consistent with the present disclosure;

FIG. 5 illustrates a block diagram of a system including a computing device for performing the method of FIGS. 3A and 3B;

FIG. 6 illustrates a user interface displaying channel data for different patients including doctor name and diagnosis consistent with the present disclosure;

FIG. 7 illustrates a user interface displaying selected patient information consistent with the present disclosure;

FIG. 8 illustrates a user interface displaying medical images of the selected patient consistent with the present disclosure;

FIG. 9 illustrates a user interface displaying larger patient images with annotations consistent with the present disclosure;

FIG. 10 illustrates a user interface displaying patient channel communications among the doctors and the AI Bot, consistent with the present disclosure; and

FIG. 11 illustrates a user interface displaying treatment recommendation provided by the AI Bot connected to the AI/ML module consistent with the present disclosure.

 DETAILED DESCRIPTION

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.

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.

Thus, for example, any sequence(s) 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.

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.

Regarding applicability of 35 U.S.C. § 112, ¶6, 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.

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.”

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.

The present disclosure includes many aspects and features. Moreover, while many aspects and features relate to, and are described in, the context of processing organization data, embodiments of the present disclosure are not limited to use only in this context.

The present disclosure provides a system, method and computer-readable medium for an automated predicting of a medical diagnosis of a patient over channelized communications of a healthcare organization.

In any modern organization such as a healthcare organization, workflow-based 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 channel-based 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).

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 sub-objects (e.g., blood test, X-Ray, CT scan, MRI, etc.) may be 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.

The proposed patient object-based approach also, advantageously, centralizes and normalizes all communication about the patient. In one embodiment, an AI 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.

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.

In one embodiment, an AI 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 AI bot connected to the patient AI/ML module configured to generate predictive patient models for suggesting diagnosis and appropriate treatments.

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).

FIG. 1A illustrates a network diagram of an AI-based automated medical diagnosis prediction system, consistent with the present disclosure.

Referring to FIG. 1A, the example medical organization network 100 includes a diagnosis server (DS) node 102 connected to an organization network including a variety of organization user nodes 103 and patient's objects 106 residing on healthcare organization's server 116. As discussed above, the patient's objects 106 are the entities of interest to the doctors/medical practitioners—i.e., stakeholders of the nodes 103. As a non-limiting example, the patient's objects/sub-objects 107 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 106 may be associated with other patient's objects/sub-objects 107. For example, the X-ray or MRI images may be represented by the sub-object 107.

The medical organization network 100 may also include remote servers (e.g., cloud servers) 104 that may host a remote patients' database 105. 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 102 may host local patients' database 115 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.

The DS node 102 may host an artificial intelligence/machine learning (AI/ML) module 108 configured to generate predictive models 109 that may provide predictive parameters for making a diagnosis of the patient represented by the patient objects 106 and sub-objects 107. The DS node 102 may access the patient objects 106 and sub-objects 107 to collect medical data. The DS node 102 may access the medical records from database(s) 115 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 106. In one embodiment, the DS node 102 may also access the remote medical records from database(s) to 105 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 106.

Then, the DS node 102 may analyze all patient-related data to generate the diagnosis or a treatment plan. In one embodiment, the DS node 102 may provide the collected patient-related data along with the medical data acquired from the databases 105 and 115 to the AI/ML module 108 configured to predict patient's diagnosis and/or treatment plan. The AI/ML module 108 may generate one or more patient-related predictive models 109 based on different classifiers generated from the patients' objects 106/107 data and the patient's records retrieved from the databases 105 and 115.

FIG. 1B illustrates a network diagram of an AI-based automated medical diagnosis prediction system implemented over a blockchain network, consistent with the present disclosure.

Referring to FIG. 1B, the example medical organization network 100′ includes a diagnosis server (DS) node 102 connected to an organization network including a variety of organization user nodes 103 and patient's objects 106 residing on healthcare organization's server 116. As discussed above, the patient's objects 106 are the entities of interest to the doctors/medical practitioners—i.e., stakeholders of the nodes 103. As a non-limiting example, the patient's objects/sub-objects 107 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 106 may be associated with other patient's objects/sub-objects 107. For example, the X-ray or MRI images may be represented by the sub-object 107.

The medical organization network 100 may also include remote servers (e.g., cloud servers) 104 that may host a remote patients' database 105. 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 102 may host local patients' database 115 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.

The DS node 102 may host an artificial intelligence/machine learning (AI/ML) module 108 configured to generate predictive models 109 that may provide predictive parameters for making a diagnosis of the patient represented by the patient objects 106 and sub-objects 107. The DS node 102 may access the patient objects 106 and sub-objects 107 to collect medical data. The DS node 102 may access the medical records from database(s) 115 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 106. In one embodiment, the DS node 102 may also access the remote medical records from database(s) 105 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 106.

Then, the DS node 102 may analyze all patient-related data to generate the diagnosis or the treatment plan. In one embodiment, the DS node 102 may provide the collected patient-related data along with the medical data acquired from the databases 105 and 115 to the AI/ML module 108 configured to predict patient's diagnosis and/or treatment plan. The AI/ML module 108 may generate one or more patient-related predictive models 109 based on different classifiers generated from the patients' objects 106/107 data and the patient's records retrieved from the databases 105 and 115.

In one disclosed embodiment, the DS node 102 may receive the confidential patient-related data from a permissioned blockchain 111 ledger 113 based on a consensus from a medical organization, nodes 103, 104 and 166 that may server as blockchain peers. Additionally, confidential patients' medical history-related information and previous diagnosis may also be acquired from the permissioned blockchain 110. The newly acquired patient's data (received from scanning the objects 106 and sub-objects 107) may be also recorded on the ledger 113 of the blockchain 111 so it can be used as training data for the predictive model(s) 109. In this implementation the DS node 102, the remote server node 104, the medical organization server 116 and the medical practitioners' devices 103 all may serve as blockchain 111 peer nodes. In one embodiment, the local patients' data 115 and the remote patients' data 105 may be duplicated on the blockchain ledger 113 for higher security and confidentiality of storage.

The AI/ML module 108 may generate a predictive model(s) 109 to predict the diagnosis parameters for the patient object 106 in response to the specific relevant pre-stored patient diagnosis-related data acquired from the blockchain 111. This way, the current diagnosis parameters may be predicted based not only on the live data acquired from scanning the patient object 106 and sub-objects 107, but also based on the previously collected patient-related data associated with the current patient represented by the patient object 106. Once the diagnosis is produces by the DS 102 node, the diagnosis may be recorded onto the blockchain ledger 113 along with the patient data for confidential storage and for future references.

FIG. 1C illustrates a network diagram of an AI-based automated medical diagnosis prediction system within channelized communications of a healthcare organization, consistent with the present disclosure.

Referring to FIG. 1C, the example medical organization network 100″ includes a diagnosis server (DS) node 102 connected over private channels 110 and/or sub-channels 112 to an organization network including a variety of organization user nodes 103 and patient's objects 106 residing on healthcare organization's server 116. As discussed above, the patient's objects 106 are the entities of interest to the doctors/medical practitioners—i.e., stakeholders of the nodes 103. As a non-limiting example, the patient's objects/sub-objects 107 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 106 may be associated with other patient's objects/sub-objects 107. For example, the X-ray or MRI images may be represented by the sub-object 107. The private communication channels 110 and/or sub-channels 112 may be created solely based on the patient object 106 as shown in FIG. 1C.

The medical organization network 100 may also include remote servers (e.g., cloud servers) 104 that may host a remote patients' database 105. 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 102 may host local patients' database 115 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.

The DS node 102 may host the AI/ML module 108 configured to generate predictive models 109 that may provide predictive parameters for making a diagnosis of the patient represented by the patient objects 106 and patient sub-objects 107. The DS node 102 may access the patient objects 106 and patient sub-objects 107 to collect patient-related medical data. The DS node 102 may access the medical records over the private channels 110/112 from database(s) 115 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 106. In one embodiment, the DS node 102 may also access the remote medical records from database(s) 105 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 106.

Then, the DS node 102 may analyze all patient-related data to generate the diagnosis or treatment plan. In one embodiment, the DS node 102 may provide the collected patient-related data along with the medical data acquired from the databases 105 and 115 to the AI/ML module 108 configured to predict patient's diagnosis and/or treatment plan. The AI/ML module 108 may generate one or more patient-related predictive models 109 based on different classifiers generated from the patients' objects 106/107 data and the patient's records retrieved from the databases 105 and 115.

In the embodiment depicted in FIG. 1C, the organization network 100″ represents the channelized model of the organization network 100 (see FIG. 1A) generated based on the patient objects 106 to provide for patient-centric private communications. As can be seen from FIG. 1C, the organization network 100″ may be configured to have communication channels 110 and subchannels 112 generated based on the pivot objects—i.e., the patient object(s) 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/sub-object 107 over a patient's object 106 X-Ray communication channel. The doctor at the node 103 may be interested in an opinion of another doctor or a radiologist at other nodes 103. In this case, the doctor may add a request (i.e., a new property) to the X-Ray sub-object 107 indicating another doctor or the radiologist at nodes 103 as stakeholders. Now, this doctor or the radiologist at another node(s) 103 is automatically added to this patient object 106 X-Ray communication channel (e.g., 110 or 112). 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 106) while all other communications that are not relevant to them are implemented over other communication channels 110 and/or subchannels 112.

In one embodiment, an AI bot 119, with access to all channel information, can participate in the discussions within the chat channel with other users (i.e., medical providers) of nodes 103 and may offer suggestions or insight in a significant and meaningful way since the entire domain of a particular patient sub-object object 107 (e.g., an X-Ray along with patient medical records) is available to the AI bot 119 connected to the AI/ML module 108 and the predictive model(s) 109 (e.g., patient models). All of the channels' 110/112 communications between the nodes 103 may be acquired and used as inputs into the AI/ML module 108 for more accurate prediction of patient diagnosis parameters.

A channel server node (not shown) may use analytics data including communication workflow records data acquired through monitoring of the organization network 100 (see FIG. 1A) for accurate configuration and on-the-fly reconfiguration of the channels 110 and/or sub channels 112. 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 110 and/or sub-channels 112 based on new pivot patient objects 106 and patient sub-objects 107 being introduced into the organization network 100″ or modification of the objects 106 (i.e., adding new patient sub-objects 107). Note that the diagnosis may be added to the patient object 106 as a patient sub-object 107 as well. In one embodiment, the channels 110 may be reconfigured or additional sub-channels 112 may be added based on new nodes 103 being added to the organization network 100″. 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.).

The disclosed embodiments depicted in FIG. 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 (not shown). The communication layer consists of nodes corresponding to the pivot objects in the channel configuration database. The pivot patient 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 centered around the patient objects 106 in this new communications layer which is integrated into the existing channel configuration database (or formed along with the database).

Each node 103 is tied to the pivot patient object(s) 106. The nodes 103 can be grouped together, or related to each other, based on the underlying relationship of the pivot patient objects 106 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 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 patient objects 106 associated therewith. As the underlying pivot patient object 106 undergoes the medical workflow process, the stakeholders at the nodes 103 are kept apprised and are able to follow the patient pivot object 106 through its medical workflow process, as it goes from Tier to Tier, and as it becomes associated/unassociated with other nodes 103 or groups of entities.

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 106 within the channel configuration database. Note that the pivot patient objects 106 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.

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

    • Output of Textual and Graphical Representations related to the database;
    • Input of Parameters or Operational commands.

The node may allow multiple types of expression:

    • i. Expression from a user about the node and/or the critical object associated with the node; and
    • ii. Expression from the object regarding any changes made thereto.

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:

    • 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
    • pivot points serving as points of data relay between the targeted objects in the database.

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:

    • 1. Data and parameters associated with the entities/pivot objects;
    • 2. Surrounding data/relevant data to the entities/pivot objects.

The pivot point may ensure that all contextually relevant information is presented at the node (e.g., nodes 103) 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 106 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).

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:

    • i. The underlying structure of the pivot objects;
    • ii. One or more workflows associated with the pivot objects.

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. The channel server may identify pivot objects 106 and the associated stakeholders, and automatically add the stakeholders to the corresponding nodes 103. The channelized communication system may provide access to the informative expressions of each node 103 to its stakeholders. The 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 stakeholders when the node 103 or an associated patient pivot object 106 are updated (e.g., when one or more informative expressions are published).

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 (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.

FIG. 2 illustrates a network diagram of a system including detailed features of a diagnosis server (DS) node 102 consistent with the present disclosure.

Referring to FIG. 2, the example network 200 includes the DS server node 102 configured to host the AI/ML module 108 configured to generate patient-related predictive models 109. As discussed above with respect to FIGS. 1A-C, the DS node 102 may host the AI/ML module 108 configured to generate patient-related predictive models 109 that may provide predictive parameters for making a diagnosis of the patient represented by the patient objects 106 and sub-objects 107 (see FIGS. 1A). The DS node 102 may access the patient objects 106 and sub-objects 107 to collect medical data. The DS node 102 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 106. In one embodiment, the DS node 102 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 106.

Then, the DS node 102 may provide the collected patient-related data along with the medical data acquired from the databases to the AI/ML module 108 configured to predict patient's diagnosis and/or treatment plan. The AI/ML module 108 may generate one or more patient-related predictive models 109 based on different classifiers generated from the patients' objects/sub-objects 106/107 data and the patient's records retrieved from the databases or from a blockchain ledger.

The DS node 102 may be configured to feed the real-time patient's data 201 acquired from the patients' objects and patients' records data 202 acquired from databases (or from blockchain 111 ledger 113) into the AI/ML module 108 configured to output diagnosis parameters for the patent associated with the patient object for which private communication channels 110 and sub channels 112 (see FIG. 1C) are created.

While this example describes in detail only one DS server node 102, multiple such nodes may be connected to the network. It should be understood that the DS 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 DS server node 102 disclosed herein. The DS 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 DS server node 102 may include multiple processors, multiple cores, or the like, without departing from the scope of the DS server node 102 system.

The DS 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 214-224 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.

The processor 204 may fetch, decode, and execute the machine-readable instructions 214 to identify at least one patient object associated with a patient. The processor 204 may fetch, decode, and execute the machine-readable instructions 216 to monitor the at least one patient object to collect patient's data 201. The processor 204 may fetch, decode, and execute the machine-readable instructions 218 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 204 may fetch, decode, and execute the machine-readable instructions 220 to generate a plurality of classifiers based on the collected patient's data and the extracted communications' data. The processor 204 may fetch, decode, and execute the machine-readable instructions 222 to feed the plurality of classifiers into an AI/ML module 108 configured to output patient diagnosis-related data into the communication channel. The processor 204 may fetch, decode, and execute the machine-readable instructions 224 to update the patient object based on the diagnosis-related data.

FIG. 3A illustrates a flowchart of a method for AI-based prediction of medical diagnosis consistent with the present disclosure.

Referring to FIG. 3A, the method 300 may include one or more of the steps described below. FIG. 3A illustrates a flow chart of an example method executed by the DS node 102 (see FIG. 2). 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 for purposes of illustration. Particularly, the processor 204 of the DS node 102 may execute some or all of the operations included in the method 300.

With reference to FIG. 3A, at block 302, the processor 204 may identify at least one patient object associated with a patient. At block 304, the processor 204 may monitor the at least one patient object to collect patient's data. At block 306, the processor 204 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 308, the processor 204 may generate a plurality of classifiers based on the collected patient's data and the extracted communications' data. At block 310, the processor 204 may feed the plurality of classifiers into an AI/ML module configured to output patient diagnosis-related data into the communication channel. At block 312, the processor 204 may update the patient object based on the diagnosis-related data.

FIG. 3B illustrates a further flowchart of a method for an AI-based automated medical diagnosis prediction 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 DS node 102 (see FIG. 2). It should be understood that method 300′ 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 300′. The description of the method 300′ is also made with reference to the features depicted in FIG. 2 for purposes of illustration. Particularly, the processor 204 of the DS node 102 may execute some or all of the operations included in the method 300′.

With reference to FIG. 3B, at block 314, the processor 204 may receive a diagnosis request for the patient over the communication channel created based on the patient object. At block 316, the processor 204 may access at least one object connected to the patient object to acquire medical data of the patient. At block 318, the processor 204 may generate at least one classifier based on the medical data of the patient. At block 320, the processor 204 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 322, the processor 204 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.

At block 324, the processor 204 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 326, the processor 204 may provide the patient diagnosis into the channel by converting the patient diagnosis using an application of a natural language processing (NLP). At block 328, the processor 204 may generate a patient diagnosis object connected to the at least one patient object.

At block 330, the processor 204 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 332, the processor 204 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 334, the processor 204 may record the collected patient's data and the diagnosis-related data onto a blockchain.

FIG. 4 illustrates deployment of a machine learning model 109 for prediction of patient's diagnosis-related communications based on blockchain assets consistent with the present disclosure.

In one disclosed embodiment, the patients' diagnosis may be generated by an AI/ML module 108 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 105 and 105 (see FIG. 1A). In one embodiment, a neural network may be used for patients' modeling and prediction. The neural network may use medical organization nodes 103 and patient objects 106 (see FIG. 1A-1C) as neurons.

In another embodiment, the AI/ML module 108 may use a decentralized storage such as a blockchain 111 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 113 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.

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.

In the example depicted in FIG. 4, a host platform 420 builds and deploys a machine learning model for predictive monitoring of assets 430 (e.g., patients' diagnosis or treatment recommendations). Here, the host platform 420 may be a cloud platform, an industrial server, a web server, a personal computer, a user device, and the like. Assets 430 can represent patient diagnosis or treatment parameters. The blockchain 410 can be used to significantly improve both a training process 402 of the machine learning model and a channelized communication predictive process 405 based on a trained machine learning model. For example, in 402, rather than requiring a data scientist/engineer or other user to collect the data, historical (heuristics) patients' data may be stored by the assets 430 themselves (or through an intermediary, not shown) on the blockchain 410.

This can significantly reduce the collection time needed by the host platform 420 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 410. By using the blockchain 410 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 430. The collected data may be stored in the blockchain 410 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.

Furthermore, training of the machine learning model on the collected data may take rounds of refinement and testing by the host platform 420. 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 402, the different training and testing steps (and the data associated therewith) may be stored on the blockchain 410 by the host platform 420. Each refinement of the machine learning model (e.g., changes in variables, weights, etc.) may be stored on the blockchain 410 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 420 has achieved a finally trained model, the resulting model may be stored on the blockchain 410.

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 430 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 patients' (pivot) objects 106 (see FIGS. 1A-1C). Determinations made by the execution of the machine learning model (e.g., patients' diagnosis, treatment plans, etc.) at the host platform 420 may be stored on the blockchain 410 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 430 (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 420 on the blockchain 410.

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.

Consistent with embodiments of the present disclosure, a communications layer may be adapted into a new or pre-existing objective-oriented data model/work-flow 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.

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.

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.

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.

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.

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.

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/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 may be 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).

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.

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.

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.

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

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

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

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.

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.

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.

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

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

    • 1. Connecting to a Project Management System/EHR/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 AI into the process

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.

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.).

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.

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, AI—and machine learning).

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.

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. 5 illustrates an example computing device (e.g., a server node) 500, which may represent or be integrated in any of the above-described components, etc.

FIG. 5 illustrates a block diagram of a system including computing device 400. The computing device 500 may comprise, but not be limited to the following:

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;

A supercomputer, an exa-scale supercomputer, a mainframe, or a quantum computer;

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;

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;

The DS node 102 (see FIG. 2) may be hosted on a centralized server or on a cloud computing service. Although method 300 has been described to be performed by the DS node 102 implemented on a computing device 500, it should be understood that, in some embodiments, different operations may be performed by a plurality of the computing devices 500 in operative communication at least one network.

Embodiments of the present disclosure may comprise a computing device having a central processing unit (CPU) 520, a bus 530, a memory unit 540, a power supply unit (PSU) 550, and one or more Input/Output (I/O) units. The CPU 520 coupled to the memory unit 540 and the plurality of I/O units 560 via the bus 530, all of which are powered by the PSU 550. 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 of the presently disclosed units is configured to perform the stages any method disclosed herein.

Consistent with an embodiment of the disclosure, the aforementioned CPU 520, the bus 530, the memory unit 550, a PSU 550, and the plurality of I/O units 560 may be implemented in a computing device, such as computing device 500. Any suitable combination of hardware, software, or firmware may be used to implement the aforementioned units. For example, the CPU 520, the bus 530, and the memory unit 550 may be implemented with computing device 500 or any of other computing devices 500, in combination with computing device 500. The aforementioned system, device, and components are examples and other systems, devices, and components may comprise the aforementioned CPU 520, the bus 530, the memory unit 550, consistent with embodiments of the disclosure.

At least one computing device 500 may be embodied as any of the computing elements illustrated in all of the attached figures, including the channel node 102 (FIG. 2). A computing device 500 does not need to be electronic, nor even have a CPU 520, nor bus 530, nor memory unit 540. The definition of the computing device 500 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 500, especially if the processing is purposeful.

With reference to FIG. 5, a system consistent with an embodiment of the disclosure may include a computing device, such as computing device 500. In a basic configuration, computing device 500 may include at least one clock module 510, at least one CPU 520, at least one bus 530, and at least one memory unit 540, at least one PSU 550, and at least one I/O 560 module, wherein I/O module may be comprised of, but not limited to a non-volatile storage sub-module 561, a communication sub-module 562, a sensors sub-module 563, and a peripherals sub-module 565.

A system consistent with an embodiment of the disclosure the computing device 500 may include the clock module 510 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 520, the central component of modern computers, which relies on a clock. The only exceptions are asynchronous circuits such as asynchronous CPUs. The clock 510 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.

Many computing devices 500 use a ltipli“clock muer” which multiplies a lower frequency external clock to the appropriate clock rate of the CPU 520. This allows the CPU 520 to operate at a much higher frequency than the rest of the computer, which affords performance gains in situations where the CPU 520 does not need to wait on an external factor (like memory 540 or input/output 560). Some embodiments of the clock 510 may include dynamic frequency change, where, the time between clock edges can vary widely from one edge to the next and back again.

A system consistent with an embodiment of the disclosure the computing device 500 may include the CPU unit 520 comprising at least one CPU Core 521. A plurality of CPU cores 521 may comprise identical CPU cores 521, such as, but not limited to, homogeneous multi-core systems. It is also possible for the plurality of CPU cores 521 to comprise different CPU cores 521, such as, but not limited to, heterogeneous multi-core systems, big.LITTLE systems and some AMD accelerated processing units (APU). The CPU unit 520 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 520 may run multiple instructions on separate CPU cores 521 at the same time. The CPU unit 520 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 500, for example, but not limited to, the clock 510, the CPU 520, the bus 530, the memory 550, and I/O 560.

The CPU unit 520 may contain cache 522 such as, but not limited to, a level 1 cache, level 2 cache, level 3 cache or combination thereof. The aforementioned cache 522 may or may not be shared amongst a plurality of CPU cores 521. The cache 522 sharing comprises at least one of message passing and inter-core communication methods may be used for the at least one CPU Core 521 to communicate with the cache 522. The inter-core communication methods may comprise, but not limited to, bus, ring, two-dimensional mesh, and crossbar. The aforementioned CPU unit 520 may employ symmetric multiprocessing (SMP) design.

The plurality of the aforementioned CPU cores 521 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 521 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 521, for example, but not limited to Instruction-level parallelism (ILP) such as, but not limited to, superscalar pipelining, and Thread-level parallelism (TLP).

Consistent with the embodiments of the present disclosure, the aforementioned computing device 500 may employ a communication system that transfers data between components inside the aforementioned computing device 500, and/or the plurality of computing devices 500. The aforementioned communication system will be known to a person having ordinary skill in the art as a bus 530. The bus 530 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 530 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 530 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 530 may comprise a plurality of embodiments, for example, but not limited to:

    • Internal data bus (data bus) 531/Memory bus
    • Control bus 532
    • Address bus 533
    • System Management Bus (SMBus)
    • Front-Side-Bus (FSB)
    • External Bus Interface (EBI)
    • Local bus
    • Expansion bus
    • Lightning bus
    • Controller Area Network (CAN bus)
    • Camera Link
    • ExpressCard
    • 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.
    • Small Computer System Interface (SCSI)/Serial Attached SCSI (SAS)
    • HyperTransport
    • InfiniBand
    • RapidIO
    • Mobile Industry Processor Interface (MIPI)
    • Coherent Processor Interface (CAPI)
    • Plug-n-play
    • 1-Wire
    • 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 IO, Thunderbolt/Mini DisplayPort, Mobile PCIe (M-PCIe), U.2, and Non-Volatile Memory Express (NVMe)/Non-Volatile Memory Host Controller Interface Specification (NVMHCIS).
    • Industry Standard Architecture (ISA), including embodiments such as, but not limited to Extended ISA (EISA), PC/XT-bus/PC/AT-bus/PC/105 bus (e.g., PC/105-Plus, PCI/105-Express, PCI/105, and PCI-105), and Low Pin Count (LPC).
    • Music Instrument Digital Interface (MIDI)
    • 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).

Consistent with the embodiments of the present disclosure, the aforementioned computing device 500 may employ hardware integrated circuits that store information for immediate use in the computing device 500, know to the person having ordinary skill in the art as primary storage or memory 540. The memory 540 operates at high speed, distinguishing it from the non-volatile storage sub-module 561, 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 540, may be transferred to secondary storage via techniques such as, but not limited to, virtual memory and swap. The memory 540 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 500. The memory 540 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:

    • 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) 552, CPU Cache memory 525, Advanced Random-Access Memory (A-RAM), and other types of primary storage such as Random-Access Memory (RAM).
    • Non-volatile memory which can retain stored information even after power is removed, for example, but not limited to, Read-Only Memory (ROM) 553, Programmable ROM (PROM) 555, Erasable PROM (EPROM) 555, Electrically Erasable PROM (EEPROM) 556 (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.
    • 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 non-volatile 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).
    • Consistent with the embodiments of the present disclosure, the aforementioned computing device 500 may employ the communication system between an information processing system, such as the computing device 500, and the outside world, for example, but not limited to, human, environment, and another computing device 500. The aforementioned communication system will be known to a person having ordinary skill in the art as I/O 560. The I/O module 560 regulates a plurality of inputs and outputs with regard to the computing device 500, wherein the inputs are a plurality of signals and data received by the computing device 500, and the outputs are the plurality of signals and data sent from the computing device 500. The I/O module 560 interfaces a plurality of hardware, such as, but not limited to, non-volatile storage 561, communication devices 562, sensors 563, and peripherals 565. The plurality of hardware is used by the at least one of, but not limited to, human, environment, and another computing device 500 to communicate with the present computing device 500. The I/O module 560 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).
    • Consistent with the embodiments of the present disclosure, the aforementioned computing device 500 may employ the non-volatile storage sub-module 561, which may be 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 561 may not be accessed directly by the CPU 520 without using intermediate area in the memory 540. The non-volatile storage sub-module 561 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 561 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:
    • Optical storage, for example, but not limited to, Compact Disk (CD) (CD-ROM/CD-R/CD-RW), Digital Versatile Disk (DVD) (DVD-ROM/DVD-R/DVD+R/DVD-RW/DVD+RW/DVD±RW/DVD+R DL/DVD-RAM/HD-DVD), Blu-ray Disk (BD) (BD-ROM/BD-R/BD-RE/BD-R DL/BD-RE DL), and Ultra-Density Optical (UDO).
    • 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.
    • Magnetic storage such as, but not limited to, Hard Disk Drive (HDD), tape drive, carousel memory, and Card Random-Access Memory (CRAM).
    • Phase-change memory
    • Holographic data storage such as Holographic Versatile Disk (HVD)
    • Molecular Memory
    • Deoxyribonucleic Acid (DNA) digital data storage

Consistent with the embodiments of the present disclosure, the aforementioned computing device 500 may employ the communication sub-module 562 as a subset of the I/O 560, 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 500 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 500 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 500. 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.

Two nodes can be said are networked together, when one computing device 500 is able to exchange information with the other computing device 500, whether or not they have a direct connection with each other. The communication sub-module 562 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 500, 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 [IPv5], 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]).

The communication sub-module 562 may comprise a plurality of size, topology, traffic control mechanism and organizational intent. The communication sub-module 562 may comprise a plurality of embodiments, such as, but not limited to:

    • Wired communications, such as, but not limited to, coaxial cable, phone lines, twisted pair cables (ethernet), and InfiniBand.
    • Wireless communications, such as, but not limited to, communications satellites, cellular systems, radio frequency/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).
    • Parallel communications, such as, but not limited to, LPT ports.
    • Serial communications, such as, but not limited to, RS-232 and USB.
    • Fiber Optic communications, such as, but not limited to, Single-mode optical fiber (SMF) and Multi-mode optical fiber (MMF).
    • Power Line and wireless communications

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).

Consistent with the embodiments of the present disclosure, the aforementioned computing device 500 may employ the sensors sub-module 563 as a subset of the I/O 560. The sensors sub-module 563 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 500. 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 563 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 must be employed to interface the said device with the computing device 500. The sensors may be subject to a plurality of deviations that limit sensor accuracy. The sensors sub-module 563 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:

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 nano-sensors).

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.

    • Acoustic, sound and vibration sensors, such as, but not limited to, microphone, lace sensor (guitar pickup), seismometer, sound locator, geophone, and hydrophone.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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/resistance thermometer, silicon bandgap temperature sensor, thermistor, and thermocouple.
    • 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.

Consistent with the embodiments of the present disclosure, the aforementioned computing device 500 may employ the peripherals sub-module 562 as a subset of the I/O 560. The peripheral sub-module 565 comprises ancillary devices uses to put information into and get information out of the computing device 500. There are 3 categories of devices comprising the peripheral sub-module 565, which exist based on their relationship with the computing device 500, input devices, output devices, and input/output devices. Input devices send at least one of data and instructions to the computing device 500. Input devices can be categorized based on, but not limited to:

    • Modality of input, such as, but not limited to, mechanical motion, audio, visual, and tactile.
    • 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.
    • 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.

Output devices provide output from the computing device 500. 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 of the aforementioned peripheral sub-module 565:

Input Devices

    • Human Interface Devices (HID), such as, but not limited to, pointing device (e.g., mouse, touchpad, joystick, touchscreen, game controller/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).
    • 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.
    • Video Input devices are used to digitize images or video from the outside world into the computing device 500. 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.
    • 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 500 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.
    • Data Acquisition (DAQ) devices convert at least one of analog signals and physical parameters to digital values for processing by the computing device 500. 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).

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

    • 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).

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

    • 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.
    • Other devices such as Digital to Analog Converter (DAC)

Input/Output Devices may further comprise, but not be limited to, touchscreens, networking device (e.g., devices disclosed in network 562 sub-module), data storage device (non-volatile storage 561), facsimile (FAX), and graphics/sound cards.

FIGS. 6-11 show examples of user interfaces of the system for AI-based prediction of medical diagnosis consistent with the present disclosure.

FIG. 6 illustrates a UI 600 displaying channel data for different patients including doctor name and diagnosis.

FIG. 7 illustrates a UI 700 displaying selected patient information in the window on the right of the screen.

FIG. 8 illustrates a UI 800 displaying medical images of the selected patient.

FIG. 9 illustrates a UI 900 displaying larger patient images with annotations.

FIG. 10 illustrates a UI 1000 displaying patient channel communications among the doctors and the AI Bot.

FIG. 11 illustrates a UI 1100 displaying treatment recommendation provided by the AI Bot connected to the AI/ML module discussed above.

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.

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.

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.

Claims

1. A system, comprising:

a processor of a diagnosis server node connected to at least one health organization network comprising a plurality of provider nodes;
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 plurality of 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.

2. The system of claim 1, wherein the instructions further cause the processor to receive a diagnosis request for the patient over the communication channel created based on the patient object.

3. The system of claim 1, wherein the instructions further cause the processor to access at least one object connected to the patient object to acquire medical data of the patient.

4. The system of claim 3, wherein the instructions further cause the processor to generate at least one classifier based on the medical data of the patient.

5. The system of claim 1, wherein the instructions further cause the processor to extract the communications' data of the channel comprising messaging of medical providers related to the at least one patient object, wherein an extraction of the communications' data comprises an application of a natural language processing.

6. The system of claim 1, wherein the instructions further cause the processor to 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.

7. The system of claim 1, wherein the instructions further cause the processor to feed the plurality of classifiers into the AI/ML module configured to generate a patient-related predictive model designed to produce a patient diagnosis.

8. The system of claim 7, wherein the instructions further cause the processor to provide the patient diagnosis into the channel by converting the patient diagnosis using an application of a natural language processing.

9. The system of claim 7, wherein the instructions further cause the processor to generate a patient diagnosis object connected to the at least one patient object.

10. The system of claim 1, wherein the instructions further cause the processor to 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.

11. The system of claim 1, wherein the instructions further cause the processor to 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.

12. The system of claim 1, wherein the instructions further cause the processor to record the collected patient's data and the diagnosis-related data onto a blockchain.

13. A method, comprising:

identifying, by a diagnosis server (DS) node, at least one patient object associated with a patient;
monitoring, by the DS node, the at least one patient object to collect patient's data;
accessing, by the DS node, a communication channel associated with the at least one patient object to extract communications' data of a plurality of provider nodes;
generating, by the DS node, a plurality of classifiers based on the collected patient's data and the extracted communications' data;
feeding, by the DS node, 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.

14. The method of claim 13, further comprising accessing at least one object connected to the patient object to acquire medical data of the patient and generating at least one classifier based on the medical data of the patient.

15. The method of claim 13, further comprising receiving a diagnosis request for the patient over the communication channel created based on the patient object.

16. The method of claim 13, further comprising continuously monitoring the at least one patient object and the communication channel associated with the at least one patient object to update the plurality of classifiers.

17. The method of claim 13, further comprising detecting changes in the at least one patient object and, responsive to a detection of the changes in the at least one patient object, providing the changes to the AI/ML module configured to generate updated diagnosis-related data.

18. A non-transitory computer readable medium comprising instructions, that when read by a processor, cause the processor to perform:

identifying at least one patient object associated with a patient;
monitoring, by the DS, the at least one patient object to collect patient's data;
accessing, by the DS node, a communication channel associated with the at least one patient object to extract communications' data of a plurality of provider nodes;
generating, by the DS node, a plurality of classifiers based on the collected patient's data and the extracted communications' data;
feeding, by the DS node, 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.

19. The non-transitory computer readable medium of claim 18 comprising instructions, that when read by a processor, cause the processor to 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.

20. The non-transitory computer readable medium of claim 18 comprising instructions, that when read by a processor, cause the processor to 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.

Patent History
Publication number: 20240029883
Type: Application
Filed: Jan 13, 2023
Publication Date: Jan 25, 2024
Inventor: Massoud Alibakhsh (Sandy Springs, GA)
Application Number: 18/154,780
Classifications
International Classification: G16H 50/20 (20060101); G16H 80/00 (20060101); G06F 40/40 (20060101);