DETAILED CORRESPONDANCE
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of claims
This final office action on merits is in response to the communication received on 07/14/2026. Claims 2-3 are cancelled. Amendments to claims 1, 5, 19, and 20 are acknowledged and have been carefully considered. Claims 1 and 4-20 are pending and considered below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 and 4-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Under step 1, the analysis is based on MPEP 2106.03, and claims 1 and 4-19 are drawn to a method, and claim 20 is drawn to a system. Thus, each claim, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. §101.
Step 2A Prong One
Claims 1, 19, and 20 recite the limitations of:
analyzing at least one communication to determine an adverse health condition of the individual member (claims 1, 19, and 20);
predicting an expected interaction between the individual member and the healthcare provider and a type of interaction based on the analysis of the at least one communication (claims 1, 19, and 20);
generating a record for the individual member based on the analysis of the at least one communication, the record including: one or more lifestyle activities engaged in by the individual member, biometric data reported to the DCN regarding the individual member, information regarding one or more relationships of the individual member, and the one or more prepared questions (claim 1); automatically generating a record for the individual member, the record including: one or more lifestyle activities engaged in by the individual member, biometric data reported to the DCN regarding the individual member, information regarding one or more relationships of the individual member, and the one or more prepared questions the individual member can ask the healthcare provider (claim 19); generate the record for the individual member based on the analysis of the at least one communication (claim 20);
and determining an expected interaction between the individual member and the healthcare provided based on the analysis (claim 19).
These limitations, as drafted, are processes that, under their broadest reasonable interpretations, encompass concepts performed in the human mind, including observations, evaluations, and judgement. For example, a person can review a communication from an individual member, evaluate the information contained in the communication to determine an adverse health condition, and determine or predict from the communication that the individual member has an expected interaction with a healthcare provider and the type of that interaction. A person can also review the communication to identify, organize, and summarize information concerning the individual member, including lifestyle activities, biometric data, relationship information, and prepared questions, to generate a record using pen and paper. Even when considering the “in the DCN” or “by the computer processor” language, these limitations do not describe a particular analysis, prediction, determination, or information organization that would prevent to processes from being performed in the human mind. Instead, the DNC and computer processor merely provide a technological mechanism through which the recited evaluations and judgements are carried out. The nominal recitation of in the DCN or by the computer processor does not take the claim limitations out of the mental processes grouping. Thus, the claims recite a mental process which is an abstract idea.
Claims 1, 19, and 20 recite as a whole a method of organizing human activity (i.e., managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions) because the claim recites a method that allows users to:
provide digital communication network (DCN) for a plurality of members of a population to communicate with each other (claims 1 and 19); provides a network for members of population to interact with each other (claim 20); and
generate one or more prepared questions regarding medical care for the adverse health condition for the individual member to ask the healthcare provider during the expected interaction, the one or more prepared questions generated automatically based on the analysis of the at least one communication and the type of interaction (claims 1, 19, and 20).
These limitations manage interactions between people by providing an environment through which members of a population communicate and interact with one another and by preparing questions for an individual member to ask a healthcare provider during an healthcare interaction. This, the claims facilitate communications and interactions among members of a population and between an individual member and a healthcare provider according to information concerning the individual member and the expected health interaction. The nominal recitation that these activities occur in a DNC or are performed by a computer processor does not take the interpersonal communication and interaction management activities outside of certain methods or organizing human activity grouping. Thus, the claims recite an abstract idea.
The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Under Step 2A Prong Two
The claimed limitations, as per claim 1, include:
providing a digital communication network (DCN) for a plurality of members of a population to communicate with each other;
analyzing at least one communication in the DCN to determine an adverse health condition of the individual member;
predicting an expected interaction between the individual member and the healthcare provider and a type of interaction based on the analysis of the at least one communication;
generating one or more prepared questions regarding medical care for the adverse health condition for the individual member to ask the healthcare provider during the expected interaction, the one or more prepared questions generated automatically based on the analysis of the at least one communication and the type of interaction;
generating a record for the individual member in the DCN based on the analysis of the at least one communication, the record including:
one or more lifestyle activities engaged in by the individual member,
biometric data reported to the DCN regarding the individual member,
information regarding one or more relationships of the individual member, and the one or more prepared questions; and
providing the record to the individual member through the DCN.
The claimed limitations, as per claim 19, include:
providing a digital communication network (DCN) for a plurality of members of a population to communicate with each other;
analyzing at least one communication in the DCN to determine an adverse health condition of the individual member;
predicting an expected interaction between the individual member and the healthcare provider and a type of interaction based on the analysis of the at least one communication;
generating one or more prepared questions regarding medical care for the adverse health condition for the individual member to ask the healthcare provider during the expected interaction, the one or more prepared questions generated automatically based on the analysis of the at least one communication and the type of interaction;
automatically generating a record for the individual member in the DCN, the record including:
one or more lifestyle activities engaged in by the individual member,
biometric data reported to the DCN regarding the individual member,
information regarding one or more relationships of the individual member, and
the one or more prepared questions the individual member can ask the healthcare provider;
determining an expected interaction between the individual member and the healthcare provided based on the analysis; and
providing the record prior to the expected interaction.
The claimed limitations, as per claim 20, include:
a computer processor; a data repository in communication with the computer processor and storing: at least one communication, and a record having at least one of:
one or more lifestyle activities engaged in by the individual member,
biometric data reported to the DCN regarding the individual member,
information regarding one or more relationships of the individual member, and one or more prepared questions;
a communication analyzer which, when executed by the computer processor, analyzes the at least one communication to determine an adverse health condition of the individual member;
a digital communications network which, when executed by the computer processor, provides a network for members of population to interact with each other; and a server controller which, when executed by the computer processor:
predict an expected interaction between the individual member and the healthcare provider and a type of interaction based on the analysis of the at least one communication;
generate one or more prepared questions regarding medical care for the adverse health condition for the individual member to ask the healthcare provider during the expected interaction, the one or more prepared questions generated automatically based on the analysis of the at least one communication and the type of interaction;
generate the record for the individual member based on the analysis of the at least one communication; and
provide the record to the individual member.
Examiner Note: underlined elements indicate additional elements of the claimed invention identified as performing the steps of the claimed invention.
The judicial exception expressed in claims 1, 19, and 20 are not integrated into a practical application. The claim as a whole merely describes how to generally “apply” the concept of analyzing and organizing information concerning an individual member or managing communications and interactions between the individual member and other persons in a computer environment. The claims use computer technology to analyze communications, determine and adverse health condition, predict an expected healthcare interaction and a type of interaction, generate prepared questions for the individual member to ask the provider, generate a record containing information concerning the individual member, and facilitating communication and interactions between persons.
The claimed computer components and computer limitations (i.e., in the DCN (claims 1, 19), through the DCN (claim 1), a computer processor; a data repository in communication with the computer processor (claims 20), a communication analyzer which, when executed by the computer processor (claim 20), a digital communications network which, when executed by the computer processor (claim 20), and a server controller which, when executed by the computer processor) are recited at a high level of generality and are merely invoked as tools to perform the recited abstract processes. The claims do not recite technological mechanisms or techniques by which the computer components perform the claimed analysis, determination, prediction, question generation, or record generation, and they do not recite an improvement to the operation or function of the computer processor, data repository, communication analyzer, or server. Instead, the computer components are used to analyze, organize, and generate information carried out in the judicial exceptions. Simply implementing the abstract idea on a generic computer does not integrate the judicial exception into a practical application (MPEP 2106.05(f)). Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application.
The judicial exception expressed in claims 1, 19, and 20 are not integrated into a practical application. The claims recite the additional elements of providing the record to the individual member (claims 1 and 20), providing the record prior to the expected interaction (claim 19), and storing: at least one communication, and a record having at least one of: one or more lifestyle activities engaged in by the individual member, biometric data reported to the DCN regarding the individual member, information regarding one or more relationships of the individual member, and one or more prepared questions (claim 19). These limitations are recited at a high level of generality (i.e., as a general means of storing information used in or resulting from the recited abstract processes and providing the resulting information to an individual member), and amounts to merely insignificant extra-solution activities (MPEP 2106.05 (g)). Storing the communication merely constitutes generic storage and receiving information used in the information analysis, while providing the record prior to the expected interaction merely specifies when the resulting information is provided to the individual member and does not impose a meaningful limitation on how the abstract processes are performed. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Claims 1, 19, and 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A, the claim as a whole merely describes how to generally “apply” the concept of analyzing and organizing information concerning an individual member or managing communications and interactions between the individual member and other persons in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
For claim 1, under step 2B, the additional elements of providing the record to the individual member (claims 1 and 20), providing the record prior to the expected interaction (claim 19), and storing: at least one communication, and a record having at least one of: one or more lifestyle activities engaged in by the individual member, biometric data reported to the DCN regarding the individual member, information regarding one or more relationships of the individual member, and one or more prepared questions (claim 19) have been evaluated. The system comprising a computer processor performs a general function of receiving patient data for subsequent processing, which represents a well-understood, routine, and conventional activity in the field of data processing and information management systems. The specification discloses that the processor is used in its ordinary capacity as a generic processor performing conventional functions and does not describe any improvement to the computer itself or to the functioning of the overall computer system (see [0055]-[0057]). Also noted in Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016), merely collecting information for analysis and displaying the results without a technological improvement does not add significantly more to an abstract idea. The use of the method and system is no more than collecting information before performing analysis and displaying the result and does not integrate the abstract idea into a practical application. Therefore, the claim does not recite an inventive concept and is not patent eligible.
Claims 4, 6, 8-14, 16, and 18 recite no further additional elements, and only further narrow the abstract idea. The previously identified additional elements, individually and as a combination, do not integrate the narrowed abstract idea into a practical application for reasons similar to those explained above, and do not amount to significantly more than the narrowed abstract idea for reasons similar to those explained above.
Claims 5, 7, 15, and 17 recite the additional elements of by the DNC (claim 5), generating a link to the record; and providing the link to the healthcare provider (claim 7), through the DCN (claim 15), and in the DNC (claim 17). However, these additional element amount to implementing an abstract idea on a generic computing device, or displaying a result and insignificant application (i.e., insignificant extra-solution activities). As such, these additional elements, when considered individually or in combination with the prior devices, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
Therefore, the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claims are rejected under 35 U.S.C. 101 for lacking eligible subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 and 4-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rathod (U.S. Publication 2011/0276396 A1), referred to hereinafter as Rathod, in view of Bitran et al. (U.S. Publication 2017/0039344 A1), referred to hereinafter as Bitran, and Heckerman et al. (U.S. Publication 2009/0089082 A1), referred to hereinafter as Heckerman.
Regarding claim 1, Rathod teaches a method for improving communications between an individual member, a method comprising (Rathod [0010] “Another significant objective of the present invention is to provide customized, contextual, dynamic & unified communication and dynamically or automatically or manually associate or attach or link one or more identified active links which are controlled by user and exist in social network with message and sent to determined target users, wherein said active link(s) with message enables receiving user to sell, purchase, transact, participate with same activities as sender user, communicate, collaborate, workflow with sender user, provide response, take one or more actions, access in any manner in an integrated, dynamic and unified manner.”);
providing a digital communication network (DCN) for a plurality of members of a population to communicate with each other (Rathod [0046] “In an embodiment, presenting action, activity, status, log feed comprising:monitoring & tracking user's one or more types of physical or digital activities, actions, interactions, communications, responses, events, transactions, life stream, logs, locations, behavior, movement, environment, status, states & conditions from one or more sources; storing, recording & logging said one or more types of physical or digital activities, actions, interactions, communications, responses, events, transactions, life stream, logs, locations, behavior, movement, environment, status, states & conditions from one or more sources; processing said one or more types of physical or digital activities, actions, interactions, communications, responses, events, transactions, life stream, logs, locations, behavior, movement, environment, status, states & conditions including dynamically or manually or automatically attaching one or more accessible active links, metadata & data; manually or auto determining receivers by sender and/or central server unit for sending, publishing, updating & presenting logged one or more types of physical or digital activities, actions, interactions, communications, responses, events, transactions, life stream, logs, locations, behavior, movement, environment, status, states & conditions with one or more active links, metadata & data; dynamically presenting said one or more types of physical or digital activities, actions, interactions, communications, responses, events, transactions, life stream, logs, locations, behavior, movement, environment, status, states & conditions with one or more active links, metadata & data to determined receivers; and allow to access said received or presented one or more types of physical or digital activities, actions, interactions, communications, responses, events, transactions, life stream, logs, locations, behavior, movement, environment, status, states & conditions specific one or more action or activity or status or log item(s) associate or attached one or more accessible active links, metadata & data.”, and
Rathod [0053] “In an embodiment, action source or user (sender or receiver) of action feed or action or activity or status or log item(s) comprising registered user, unregistered user, users of social network, connected or related users of user, group(s) of user, objects, animals, birds, tree, vehicles, machineries, fixture & furniture, building, structure or infrastructure, location, tourist places, non-living things, digital or automated sources & destinations, auto selection or extraction from user's life stream, auto extraction from video, auto identification, auto determination, auto detection and auto sense one or more action item(s) from one or more sources, applications, services, networks including communication networks, external networks, non-social networks, centralized or peer to peer networks, groups, devices, sensors, automations, communication systems, multi artificial intelligence agent(s), 3rd parties web sites, applications, services, devices, sensors, databases, repositories, networks, other users on behalf of user including connected or related users, friends, family members, co-workers, classmates, sellers, teachers, doctors, lawyers, professionals, and any combination thereof.”);
analyzing at least one communication in the DCN (Rathod [0058] “In one embodiment, monitoring & tracking user's one or more activities, actions, events, transactions, life stream, locations, behavior, facial expressions, emotions, movement, workflow, logs, follow-ups, environment, status & conditions based on one or more action or activity recognition research, algorithm, methods and systems, sensors, detectors, transducers, video cameras, audio recorders, imaging, RFID, barcodes, touch screens, devices including mobile, digital televisions, digital watch, digital pen, automations, scanners, robotics, computer system, computer chips or processors, instruments, speech & text recognition, video & face recognition, speech or voice sources, translating system, application, services, networks, logs, programming, and human mediated actions including analysis, logic, guess, selections, privacy settings, preferences, settings, inspection, checking, verification.”);
generating a record for the individual member in the DCN based on the analysis of the at least one communication, the record including (Rathod [0348] “The action item(s) generator 510 generates communications for each member about information that may be relevant to the member. These communications may take the form of action item(s), each action item(s) is an information message comprising one or a few lines of information about an action in the action log that is relevant to the particular member. The action item(s) are presented to a member via one or more pages of the social networking website 100, for example in each member's home page or action item(s) page. An action item(s) is a message that summarizes, condenses, or abstracts one or more member actions from the action log 525. The generated action item(s) can then be transmitted to one or more related members e.g., the member's connected users, friends, subscribers, and auto matched receivers or responders allowing the member's actions to be shared with related members. More about user action describe in U.S. patent application Ser. No. 11/995,343, titled: “A method and system for communication, publishing, grouping, advertising, searching, sharing and dynamically providing a Journal Feed” and divisional U.S. patent application Ser. No. 12/973,370, titled: “A System and method for publishing, communication and real time searching”.” and
Rathod [0132] “In one embodiment action or activity or status or log items may comprising one or more statement(s) or sentence(s), templates, selected or edited concepts, note taking methods including tree structure, charting, outlining, mapping, mind maps, timelines, unstructured notes, structured notes with customize fields or tags, details, visual notes, flow charts, cluster notes, reports, summary, story, blog, descriptions, database, message(s), paper form, multimedia content types including text, image(s), photos, symbols, diagram, presentation, video(s), extracts or part of video &, voice, map, scanned documents, calendar, script, query, keyword(s) in one or more languages, phrase(s), Boolean operators, rules, condition(s), semantic syntax, ontology with associated one or more accessible metadata or fields.”):
one or more lifestyle activities engaged in by the individual member (Rathod [0271] “In another important embodiment, present invention can monitoring, tracking, recording, storing, processing any types of user actions, activities, events, transactions, life stream, life stream, behavior of user(s) in one or more networks based on specific devices including mobile, video camera, audio recorder, detector, sensors which monitors, tracks, records, stores, processes user's physical activities, works, process, movements, locations, health, conditions, environment, status including driving vehicle, entering into particular vehicle or building, purchasing of brands, subscribing of services, viewing of television, communicate with others, selecting something, viewing something, visiting anywhere, accessing something, interact with anything, going anywhere, preparing something, eating something, buying or selling something, user surrounding environment including weather, other users or connected or related users actions, activities, events, transactions, life stream, locations, interactions with user, detecting health or conditions, activities related to any types or categories of works, jobs, sports, health, entertainment, shopping, education, training, learning, arts, travels, food, lifestyle, games, finance.”);
information regarding one or more relationships of the individual member (Rathod [0053] In an embodiment, action source or user (sender or receiver) of action feed or action or activity or status or log item(s) comprising registered user, unregistered user, users of social network, connected or related users of user, group(s) of user, objects, animals, birds, tree, vehicles, machineries, fixture & furniture, building, structure or infrastructure, location, tourist places, non-living things, digital or automated sources & destinations, auto selection or extraction from user's life stream, auto extraction from video, auto identification, auto determination, auto detection and auto sense one or more action item(s) from one or more sources, applications, services, networks including communication networks, external networks, non-social networks, centralized or peer to peer networks, groups, devices, sensors, automations, communication systems, multi artificial intelligence agent(s), 3rd parties web sites, applications, services, devices, sensors, databases, repositories, networks, other users on behalf of user including connected or related users, friends, family members, co-workers, classmates, sellers, teachers, doctors, lawyers, professionals, and any combination thereof.”,
Rathod [0132] “In one embodiment action or activity or status or log items may comprising one or more statement(s) or sentence(s), templates, selected or edited concepts, note taking methods including tree structure, charting, outlining, mapping, mind maps, timelines, unstructured notes, structured notes with customize fields or tags, details, visual notes, flow charts, cluster notes, reports, summary, story, blog, descriptions, database, message(s), paper form, multimedia content types including text, image(s), photos, symbols, diagram, presentation, video(s), extracts or part of video &, voice, map, scanned documents, calendar, script, query, keyword(s) in one or more languages, phrase(s), Boolean operators, rules, condition(s), semantic syntax, ontology with associated one or more accessible metadata or fields.”, and
Rathod [0348] “The action item(s) generator 510 generates communications for each member about information that may be relevant to the member. These communications may take the form of action item(s), each action item(s) is an information message comprising one or a few lines of information about an action in the action log that is relevant to the particular member. The action item(s) are presented to a member via one or more pages of the social networking website 100, for example in each member's home page or action item(s) page. An action item(s) is a message that summarizes, condenses, or abstracts one or more member actions from the action log 525. The generated action item(s) can then be transmitted to one or more related members e.g., the member's connected users, friends, subscribers, and auto matched receivers or responders allowing the member's actions to be shared with related members. More about user action describe in U.S. patent application Ser. No. 11/995,343, titled: “A method and system for communication, publishing, grouping, advertising, searching, sharing and dynamically providing a Journal Feed” and divisional U.S. patent application Ser. No. 12/973,370, titled: “A System and method for publishing, communication and real time searching”).”);
providing the record to the individual member through the DCN (Rathod [0132] “In one embodiment action or activity or status or log items may comprising one or more statement(s) or sentence(s), templates, selected or edited concepts, note taking methods including tree structure, charting, outlining, mapping, mind maps, timelines, unstructured notes, structured notes with customize fields or tags, details, visual notes, flow charts, cluster notes, reports, summary, story, blog, descriptions, database, message(s), paper form, multimedia content types including text, image(s), photos, symbols, diagram, presentation, video(s), extracts or part of video &, voice, map, scanned documents, calendar, script, query, keyword(s) in one or more languages, phrase(s), Boolean operators, rules, condition(s), semantic syntax, ontology with associated one or more accessible metadata or fields.”, and
Rathod [0348] “The action item(s) generator 510 generates communications for each member about information that may be relevant to the member. These communications may take the form of action item(s), each action item(s) is an information message comprising one or a few lines of information about an action in the action log that is relevant to the particular member. The action item(s) are presented to a member via one or more pages of the social networking website 100, for example in each member's home page or action item(s) page. An action item(s) is a message that summarizes, condenses, or abstracts one or more member actions from the action log 525. The generated action item(s) can then be transmitted to one or more related members e.g., the member's connected users, friends, subscribers, and auto matched receivers or responders allowing the member's actions to be shared with related members. More about user action describe in U.S. patent application Ser. No. 11/995,343, titled: “A method and system for communication, publishing, grouping, advertising, searching, sharing and dynamically providing a Journal Feed” and divisional U.S. patent application Ser. No. 12/973,370, titled: “A System and method for publishing, communication and real time searching”).
Rathod fails to explicitly teach a healthcare provider; to determine an adverse health condition of the individual member; predicting an expected interaction between the individual member and the healthcare provider and a type of interaction based on the analysis of the at least one communication; generating one or more prepared questions regarding medical care for the adverse health condition for the individual member to ask the healthcare provider during the expected interaction; the one or more prepared questions generated automatically based on the analysis of the at least one communication and the type of interaction; biometric data reported to the DCN regarding the individual member; the one or more prepared questions;
Bitran teaches a healthcare provider (Bitran [0078] “In this aspect, the method may further comprise determining scheduling information for each of the plurality of health care providers and outputting on the display an appointment scheduling interface with available timeslots at the plurality of recommended healthcare providers for the recommended health care service, receiving a user selection of a timeslot and healthcare provider, and transmitting the user selected timeslot and user ID of the user to the selected healthcare provider to make an appointment. In this aspect, the method may further comprise outputting on the display a graphical representation of the plurality of health care providers, including a map indicating the location of the plurality of health care providers, and a route to at least one of the health care providers.”, and
Bitran [0044] “Referring to FIG. 4, to further assist patients in seeking proper medical attention, the health recommender 25 may be configured to display on the computing device an appointment scheduling interface 100 with available timeslots 102 a, 102 b, 102 c, and 102 d at a recommended healthcare provider, receive a user selection of a selected timeslot 102 a, and transmit the user selected timeslot and user ID of the user to the healthcare provider to make an appointment. For example, if the health recommender 25 recommends visiting the ER, the computing device 12 may be configured to dial 911. In other situations, if the health recommender 25 recommends scheduling an appointment with the primary care physician, the interface may direct the user to the patient's preferred primary care provider on the patient's network provider or HMO to conveniently schedule an appointment.”);
to determine an adverse health condition of the individual member (Bitran [0018] “Non-medical data may include the following: search history may include a user's search queries entered in a search engine interface such as a browser displaying a search engine web page or a search application executed on the client computing device. The download history may include, for example, applications installed, or files downloaded from a download website, including songs, videos, games, etc. Each of these applications and files may have metadata associated with them, such as categories, genres, etc., which can be used to build user profile 32, discussed below. The browse history may include a list of websites, and particular pages within websites visited by a user using a browser executed on the client computing device. The browse history may also include in-application browsing of application specific databases, such as a shopping application that is configured to enable a user to browse a vendor's catalog. The contacts include names and contact information for individuals or organizations saved in a user contact database on client computing device 12, or retrieved from an external site, such as a social network website. The social network data may include a user's social media posts, friends list, a list of social network entities “liked” by the user, check-ins made by the user at locations via a social network program, posts written by the user, etc. Purchase history may include information gleaned from an ecommerce transaction between the client computing device 12 and an ecommerce platform, regarding products purchased by a user, including product descriptions, time and date of purchase, price paid, user feedback on those purchases, etc. The non-medical data may also comprise at least one of weather data, allergen concentration, pathogen concentration, UV index, and air quality (including air pollen and pollutant concentration measurements).”,
Bitran [0020] “User data 26 is transmitted from the electronic personal assistant application program 24 to the personal assistant interpretation engine 28 executed on server system 14. The personal assistant user data interpretation engine 28 performs various operations on the received user data 26, including parsing and converting the user data 26 into a format and structure that enables the first correlator 36 to process the user data 26. The interpretation engine 28 may also isolate pieces of information from the non-medical data that are relevant to the health of the user. For example, the interpretation engine 28 may isolate reports of a user's illness in a social media post. After performing various operations on the user data 26, the interpretation engine 28 sends the user data 26 for each individual user to a first correlator 36.”,
Bitran [0035] “Referring to FIG.2, the personal assistant application program 24 may further include a health recommender 25, which is configured to identify at least one health condition of the user, determine a health recommendation that is based at least on the combined time and location based data, a user's health insurance information, user electronic medical record 42, the identified health condition, and output the health recommendation to a display associated with the computing device. The health recommender 25 is instantiated in the application 24 in the client computing device 12 and communicates with the electronic personal assistant application server 66, which receives the user personal history 30, user electronic medical record 42, and combined time and location-data including the first aggregated history 38 and the second aggregated history 54. Based upon the above inputs, which may also comprise user input from the user, a healthcare provider that reported the signs, a search query entered by the user, a browsing history of the user, and/or sensor data received from a sensor associated with the computing device 12, the health recommender 25 may identify the health condition. The health recommender 25 may determine a health recommendation implementing a machine-learning algorithm 29 that iteratively processes at least one of signs, symptoms, health records, genomic data, online behavior data, search queries, location data, user feedback, health insurance information, and context information of one or more users to inform and modify the algorithm over time. It will be appreciated that the inputs of the health recommender 25 are not limited to the above, and may encompass other inputs that are relevant to determining a user's health recommendations.”, and
Bitran [0041] “The documentation of data by the health recommender 25 may not be limited to signs and symptoms dictated by the user, and may also include signs and/or symptoms that are identified by a search query 76 entered by the user, and browsing history of the user. For example, in addition to noting the shortness of breath and inhaler uses of the asthma patient, the health recommender 25 may also note that the user has queried asthma attacks on the search engine. The health recommender 25 may interpret this browsing history as a cue to prioritize asthma in its health recommendations. Further, at least the timing of the user's search query may be used to determine the acuity and severity of the signs and/or symptoms. For example, the health recommender 25 may infer that an elderly patient who searches “dark stools” following bowel surgery may have some residual gastrointestinal bleeding that may need to be closely monitored and documented for later review.”);
predicting an expected interaction between the individual member and the healthcare provider and a type of interaction based on the analysis of the at least one communication (Bitran [0039] “The algorithm 29 may also classify the patient's signs and symptoms into tiers according to acuity and severity, so that the patient may be directed to the appropriate healthcare professionals. For example, the health recommender 25 may advise a patient with a mild, self-limiting headache to try an NSAID medication at home. An asthma patient whose inhalers are losing their effectiveness may be asked to schedule an appointment with a doctor. On the other hand, a heart failure patient with acutely worsening shortness of breath would be asked to visit the ER immediately. It will be appreciated that the health recommender 25 will receive a user's health insurance information that is retrieved by the health insurance retriever 27 and make referrals to healthcare providers and medical services based upon the specific health insurance and individual preferences of the user. For example, the health recommender 25 may apply search filters to narrow the list of recommended specialists to in-network healthcare providers, hospitals, and clinics. When a user is presented with a narrowed list that has certain results excluded because they are not covered by user's health insurance, a selector may be provided for the user to view excluded results as well, in case the user desires to see the full results, including both in-network and out-of-network results. The user may adjust individual preferences in the user settings 44 to include or exclude certain services that the health recommender 25 may recommend, such as alternative medicine clinics and chiropractic treatment centers. The user may further adjust individual preferences to include or exclude medical services that the health recommender 25 may recommend based upon financial factors, including deductibles, out-of-pocket expenses, and copayments.”,
Birtan [0054] “FIG. 9 illustrates an example of a method 400 for determining and outputting health recommendations for a user. At 402, the method 400 includes retrieving user input from the user, a healthcare provider reporting the sign, a search query entered by the user, a browsing history of the user, and/or sensor data received from a sensor associated with the computing device. At 404, the method includes identifying at least one health condition of the user based upon the retrieving user input from the user, a healthcare provider reporting the sign, a search query entered by the user, a browsing history of the user, and/or sensor data received from a sensor associated with the computing device. At 406, the method 400 may further include presenting a series of health related questions to the user and receiving user responses to the questions. Optionally, the user responses may then be sent to a recommended healthcare provider; the series of health related questions may be revised and updated by a healthcare provider. At 408, the method 400 may further include classifying the health condition into a tier according to acuity and severity. At 410, the method may further include determining the acuity and severity of the health condition based at least on a timing of the user's search query, where the health condition is identified by a search query entered by the user.”,
Bitran [0055] “At 412, the method 400 includes retrieving combined time and location-based data from aggregated histories of a plurality of users, the user electronic medical record, and identified health condition, which inform a machine-learning algorithm that is implemented by a health recommender. At 414, the method 400 includes determining a health recommendation at least based on the combined data, the user electronic medical record, and the identified health condition. The health recommendation may include at least a referral to a healthcare provider and/or medical service based at least on the health insurance information and the user's preferences. At 416, the method 400 includes outputting the health recommendation to a display associated with the computing device. At 418, the method 400 includes display on the computing device an appointment scheduling interface with available timeslots at a recommended healthcare provider. At 420, the method 400 includes receiving a user selection of a selected timeslot. At 422, the method 400 includes transmitting the user selected timeslot and user ID of the user to the healthcare provider to make an appointment. At 424, the method 400 includes displaying on the computing device a follow up message to the user after the appointment, in which the user is asked to provide user feedback.”, and
Bitran [0059] “Based at least on the identified health condition, the user's electronic medical record 42, and the user's geolocation data, the health recommender 25 determines a health recommendation, including at least a recommended health service. In this example, the health recommender recommends a flu shot and identifies a plurality of health care providers 502 a, 502 b, and 502 c that deliver the recommended health care service in the vicinity of the user currently, or at a user's predicted location during an available timeslot in the future according to the user's calendar in a calendar program executed on the client computing device 12. The health insurance retriever 27 retrieves the user's health insurance information, which indicates whether the flu shot service (recommended health care service) is covered by the user's health insurance plan at each of the plurality of health care providers 502 a, 502 b, and 502 c. The health recommender 25 determines whether the flu shot service (recommended health care service) is covered by the user's health insurance plan at each of the plurality of health care providers 502 a, 502 b, and 502 c, and then outputs on the display associated with the client computing device 12 a graphical representation of the health recommendation, the plurality of health care providers 502 a, 502 b, and 502 c, and an indication of whether the recommended health care service is covered by the user's health insurance plan at each of the plurality of health care providers. In this example, the covered services are indicated by the phrase “Cvrd Srvc”, while the service that is not covered is indicated by the phrase “Not a covered service.” This graphical representation may include a map 504 indicating the location of the plurality of health care providers 502 a, 502 b, and 502 c, and a route to at least one of the health care providers. The travel distance to each of the plurality of health care providers may also be displayed (for example, “2 mins by foot”, “5 mins by car”).”);
based on the analysis of the at least one communication and the type of interaction (Bitran [0020] “User data 26 is transmitted from the electronic personal assistant application program 24 to the personal assistant interpretation engine 28 executed on server system 14. The personal assistant user data interpretation engine 28 performs various operations on the received user data 26, including parsing and converting the user data 26 into a format and structure that enables the first correlator 36 to process the user data 26. The interpretation engine 28 may also isolate pieces of information from the non-medical data that are relevant to the health of the user. For example, the interpretation engine 28 may isolate reports of a user's illness in a social media post. After performing various operations on the user data 26, the interpretation engine 28 sends the user data 26 for each individual user to a first correlator 36.”,
Bitran [0035] “Referring to FIG.2, the personal assistant application program 24 may further include a health recommender 25, which is configured to identify at least one health condition of the user, determine a health recommendation that is based at least on the combined time and location based data, a user's health insurance information, user electronic medical record 42, the identified health condition, and output the health recommendation to a display associated with the computing device. The health recommender 25 is instantiated in the application 24 in the client computing device 12 and communicates with the electronic personal assistant application server 66, which receives the user personal history 30, user electronic medical record 42, and combined time and location-data including the first aggregated history 38 and the second aggregated history 54. Based upon the above inputs, which may also comprise user input from the user, a healthcare provider that reported the signs, a search query entered by the user, a browsing history of the user, and/or sensor data received from a sensor associated with the computing device 12, the health recommender 25 may identify the health condition. The health recommender 25 may determine a health recommendation implementing a machine-learning algorithm 29 that iteratively processes at least one of signs, symptoms, health records, genomic data, online behavior data, search queries, location data, user feedback, health insurance information, and context information of one or more users to inform and modify the algorithm over time. It will be appreciated that the inputs of the health recommender 25 are not limited to the above, and may encompass other inputs that are relevant to determining a user's health recommendations.”,
Bitran [0039] “The algorithm 29 may also classify the patient's signs and symptoms into tiers according to acuity and severity, so that the patient may be directed to the appropriate healthcare professionals. For example, the health recommender 25 may advise a patient with a mild, self-limiting headache to try an NSAID medication at home. An asthma patient whose inhalers are losing their effectiveness may be asked to schedule an appointment with a doctor. On the other hand, a heart failure patient with acutely worsening shortness of breath would be asked to visit the ER immediately. It will be appreciated that the health recommender 25 will receive a user's health insurance information that is retrieved by the health insurance retriever 27 and make referrals to healthcare providers and medical services based upon the specific health insurance and individual preferences of the user. For example, the health recommender 25 may apply search filters to narrow the list of recommended specialists to in-network healthcare providers, hospitals, and clinics. When a user is presented with a narrowed list that has certain results excluded because they are not covered by user's health insurance, a selector may be provided for the user to view excluded results as well, in case the user desires to see the full results, including both in-network and out-of-network results. The user may adjust individual preferences in the user settings 44 to include or exclude certain services that the health recommender 25 may recommend, such as alternative medicine clinics and chiropractic treatment centers. The user may further adjust individual preferences to include or exclude medical services that the health recommender 25 may recommend based upon financial factors, including deductibles, out-of-pocket expenses, and copayments.”, and
Bitran [0062] “Like the embodiment described in FIG. 4, the health recommender 25 may be configured to display on the computing device an appointment scheduling interface 100 with available timeslots 102 a, 102 b, 102 c, and 102 d at a recommended healthcare provider for the recommended health care service, receive a user selection of a selected timeslot 102 a, and transmit the user selected timeslot and user ID of the user to the healthcare provider to make an appointment. The health recommender 25 may further determine an availability of the user to receive the recommended health care service based at least on a calendar. For example, if the health recommender 25 recommends receiving a flu shot, an appointment scheduling interface may show available timeslots for flu shots at one healthcare provider. In the illustrated example, the health recommender may be programmed to present the interface shown in FIG. 10, only when the user's calendar in the calendar program executed on the client computing device indicates that the user has availability currently to receive the recommended health care service. Alternatively, the health care recommender may search for available timeslots in the future based on the user's calendar and present a map of service locations surrounding a user's predicted future location during the available timeslot.”);
biometric data reported to the DCN regarding the individual member (Birtan [0016] Client computing device 12 is configured to execute an electronic personal assistant application program 24. It will be appreciated that other instances of the electronic personal assistant application program 24 may be executed on the other client computing devices 16 as well, all of which are associated with a user account on server system 14. Subject to authorization by a user, the electronic personal assistant program 24 is configured to passively monitor various user data 26 on the client computing device 12 and other client computing devices 16, such as location data, search history, download history, browsing history, contacts, social network data, calendar data, biometric data, medical device data, purchase history, etc.”,
Bitran [0019] “The medical data may comprise at least one of user's electronic medical record 42, biometric data, and medical device data. Biometric data may include a variety of data sensed by sensors on client computing device 12, medical device 22, or other client computing devices 16, such as pedometer information, heart rate and blood pressure, duration and timing of sleep cycles, body temperature, galvanic skin response, etc. Additional biometric data is discussed below in relation to the wristwatch embodiment of the wearable computing device 18.”,
Bitran [0046] Referring to FIG. 6, one possible implementation of the health recommender 25 is illustrated for a patient with trouble sleeping. In this example, the health recommender 25 is configured to display on the computing device 12 an interface 200 for making health recommendations. A biometric sleep sensor, which is a wearable computing device 18, detects 4 hours and 7 minutes of sleep and only 1 hour and 43 minutes of restful sleep from a patient suffering from insomnia. The user data 26 containing the biometric sleep data is sent to the interpretation engine 28, which interprets the biometric data and parses it into sleep data that is transmitted and stored in the user personal history 30. The health recommender 25 identifies the signs of insomnia from the sleep data that is retrieved from the user personal history 30. The machine-learning algorithm 29 implemented by the health recommender 25 is configured to process a variety of data from various sources, including other signs and symptoms reported by the user, health records, online behavior data, location data, and context information to determine a health recommendation 202. In this example, based on context information, the health recommender 25 notes that the user is maintaining a busy schedule that may be contributing to a lack of sleep, and the health recommender 25 makes a recommendation to consider schedule changes. The recommendations may be also be structured as a holistic lifestyle plan that is ongoing—as illustrated in FIG. 7, the health recommender 25 may formulate a sleep improvement plan for an insomnia patient and issue a recommendation 204 to discourage caffeine consumption as part of the plan. The recommendation 204 may be formulated according to a user's health insurance information that is retrieved by the health insurance retriever 12, especially if the HMO or network provider of the user endorses specific preventative lifestyle programs that promote healthy lifestyle changes that encompass occupational safety, sleep habits, diet, and exercise.”),
Heckerman teaches generating one or more prepared questions regarding medical care for the adverse health condition for the individual member to ask the healthcare provider during the expected interaction (Heckerman [0007] “The subject innovation relates to systems and/or methods that facilitate generating a question to ask a medical professional during a medical examination. A patient can be assisted with the automated or semi automated system, comprised of a combination of computerized hardware and/or software with interactive components. One of the interactive components can be a counselor component that directly generates user assistance in a form of user communicated counseling. The counselor component can automatically generate a question to elicit an answer from a medical professional based upon a portion of medical data. The counselor component can be utilized with a portable device in which a patient can be communicated a question that is to be asked to a medical professional. By automatically supplying the patient with medical questions via a portable device, an appointment between the medical professional and the patient is greatly enhanced since the patient can ask any possible questions he or she may have and the medical professional answers all of the patients questions. The counselor component can utilize various techniques in order to identify relevant and/or most suitable questions such as a value of information (VOI) computation algorithm or a predetermined decision tree.”,
Heckerman [0027] “For example, most patients do not ask efficient questions or all the questions they would like during an appointment with a medical professional. The subject innovation solves such problems by automatically providing questions to a patient in order for the patient to pose towards a medical professional during an appointment. Thus, in one instance, a patient can have an appointment with a doctor for his or her back pain. Medical data related to the patient and/or the diagnosis (in this example the diagnosis is back pain) can be evaluated in order to generate a question to elicit an answer from the doctor. The question can be, for example, “what treatment do you recommend?” This question can be directed to the doctor and the patient can await an answer. Once the answer is received, an additional question can be generated based on a received response. By automatically identifying questions, the appointment with the doctor and patient is optimized and more efficient. Moreover, the doctor and the patient both benefit since the doctor is answering all of the patients questions in a timely manner and the patient is getting all the information they desire.”,
Heckerman [0029] “In accordance with an aspect of the subject innovation, the counselor component 102 can employ value of information (VOI) computations to facilitate identifying or generating questions. Specifically, the counselor component 102 can utilize technical algorithms associated with determining the value of information, wherein such algorithms can identify a question to ask a doctor. For instance, the counselor component 102 can generate any suitable number of questions based on medical data and/or an answer received from the medical professional 104. By utilizing value of information computations, the questions can be organized in a hierarchical manner based on importance for each specific appointment or situation. VOI algorithms pinpoint questions that provide the most information or, more generally, the most value to the patient, given the current state of information about that patient. In general, the counselor component 102 can generate a question by utilizing a dictionary, a branching logic (e.g., decision tree) algorithm, or machine-learning algorithms that use VOI computations to identify those questions whose answers are most informative in an information-theoretic or decision-analytic sense.”,
Heckerman [0030] “The counselor component 102 can evaluate medical data in order to provide at least one question directed toward the medical professional 104. For example, the medical data can be any suitable data related to healthcare such as, but not limited to, a diagnosis, a prognosis, a medical record, a symptom, a medical evaluation, a prior medical condition, a medical condition, a disease, a virus, a blood type, an allergy, a test result, a blood pressure reading, a heart rate, an x-ray, an MRI, a scan, a CAT scan, a blood work result, a medical chart, a reading from a medical device, or a portion of information from a medical facility. Moreover, it is to be appreciated and understood that the counselor component 102 can automatically generate a question based on the medical data prior to an appointment, dynamically during an appointment, and/or any suitable combination thereof. In an additional example, the counselor component 102 can enable the patient 106 to sort (e.g., filter, delete, re-arrange order of questions, etc.) the automatically generated questions for an appointment (discussed in more detail in FIG. 7).”
Heckerman [0034] “The device 202 can be any suitable device that can receive a question and communicate such question. For instance, the device 202 can be a smartphone, a portable device, a cell phone, a mobile communication device, a portable digital assistant (PDA), a laptop, a pocket PC, a desktop, a gaming device, a portable media player, a media device, a tablet PC, a handheld, a wireless browsing device, an electronic organizer, a gaming console, a device with Internet connectivity, etc. In one example, the counselor component 102 can dynamically create a question which can be displayed or communicated to the patient 106. The patient 106 can ask the question to the medical professional 104 in order to receive a response, a statement, and/or an answer. In one aspect, the patient 106 can input the answer or response from the medical professional 104 into the device 202 to allow the counselor component 102 to generate an additional question. In another aspect, the device 202 and/or the counselor component 102 can automatically receive the answer or response from the medical professional 104. For instance, the device 202 and/or the counselor component 102 can utilize voice recognition to comprehend and track the response or answer given by the medical professional 104”);
the one or more prepared questions generated automatically (Heckerman [0030] “The counselor component 102 can evaluate medical data in order to provide at least one question directed toward the medical professional 104. For example, the medical data can be any suitable data related to healthcare such as, but not limited to, a diagnosis, a prognosis, a medical record, a symptom, a medical evaluation, a prior medical condition, a medical condition, a disease, a virus, a blood type, an allergy, a test result, a blood pressure reading, a heart rate, an x-ray, an MRI, a scan, a CAT scan, a blood work result, a medical chart, a reading from a medical device, or a portion of information from a medical facility. Moreover, it is to be appreciated and understood that the counselor component 102 can automatically generate a question based on the medical data prior to an appointment, dynamically during an appointment, and/or any suitable combination thereof. In an additional example, the counselor component 102 can enable the patient 106 to sort (e.g., filter, delete, re-arrange order of questions, etc.) the automatically generated questions for an appointment (discussed in more detail in FIG. 7).”,
Heckerman [0026] “Now turning to the figures, FIG. 1 illustrates a system 100 that facilitates generating a question to ask a medical professional during a medical examination. The system 100 can include a counselor component 102 that can receive a portion of medical data via an interface component 108 (discussed in more detail below) and automatically generate a question that is to elicit an answer from a medical professional 104. In particular, the counselor component 102 can dynamically generate a question for a patient 106 to ask the medical professional 104 during an appointment based at least in part upon evaluation of a portion of medical data. The counselor component 102 can further generate additional questions based upon an answer from the medical professional 104, wherein the answer can be in response to a question previously automatically generated. It is to be appreciated that the medical professional can be any suitable medical related entity such as, but not limited to, a doctor, a nurse, a specialist, a surgeon, a medical student, a resident, a medical assistant, etc.”,
Heckerman [0029] “In accordance with an aspect of the subject innovation, the counselor component 102 can employ value of information (VOI) computations to facilitate identifying or generating questions. Specifically, the counselor component 102 can utilize technical algorithms associated with determining the value of information, wherein such algorithms can identify a question to ask a doctor. For instance, the counselor component 102 can generate any suitable number of questions based on medical data and/or an answer received from the medical professional 104. By utilizing value of information computations, the questions can be organized in a hierarchical manner based on importance for each specific appointment or situation. VOI algorithms pinpoint questions that provide the most information or, more generally, the most value to the patient, given the current state of information about that patient. In general, the counselor component 102 can generate a question by utilizing a dictionary, a branching logic (e.g., decision tree) algorithm, or machine-learning algorithms that use VOI computations to identify those questions whose answers are most informative in an information-theoretic or decision-analytic sense.”).
the one or more prepared questions (Heckerman [0007] “The subject innovation relates to systems and/or methods that facilitate generating a question to ask a medical professional during a medical examination. A patient can be assisted with the automated or semi automated system, comprised of a combination of computerized hardware and/or software with interactive components. One of the interactive components can be a counselor component that directly generates user assistance in a form of user communicated counseling. The counselor component can automatically generate a question to elicit an answer from a medical professional based upon a portion of medical data. The counselor component can be utilized with a portable device in which a patient can be communicated a question that is to be asked to a medical professional. By automatically supplying the patient with medical questions via a portable device, an appointment between the medical professional and the patient is greatly enhanced since the patient can ask any possible questions he or she may have and the medical professional answers all of the patients questions. The counselor component can utilize various techniques in order to identify relevant and/or most suitable questions such as a value of information (VOI) computation algorithm or a predetermined decision tree.”, and
Heckerman [0030] “The counselor component 102 can evaluate medical data in order to provide at least one question directed toward the medical professional 104. For example, the medical data can be any suitable data related to healthcare such as, but not limited to, a diagnosis, a prognosis, a medical record, a symptom, a medical evaluation, a prior medical condition, a medical condition, a disease, a virus, a blood type, an allergy, a test result, a blood pressure reading, a heart rate, an x-ray, an MRI, a scan, a CAT scan, a blood work result, a medical chart, a reading from a medical device, or a portion of information from a medical facility. Moreover, it is to be appreciated and understood that the counselor component 102 can automatically generate a question based on the medical data prior to an appointment, dynamically during an appointment, and/or any suitable combination thereof. In an additional example, the counselor component 102 can enable the patient 106 to sort (e.g., filter, delete, re-arrange order of questions, etc.) the automatically generated questions for an appointment (discussed in more detail in FIG. 7).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of Rathod to incorporate the health analysis and recommendation functionality taught by Bitran, so that the health relevant information obtained from the analyzed member communications is used to identify an adverse health condition and determine an appropriate healthcare interaction and type of interaction. Rathod teaches monitoring and analyzing user activities and interactions for generating and presenting information associated with the user, while Bitran teaches extracting health relevant information from electronic user data, including reports of illness in social media communications, identifying a health condition based on such information, and determining an appropriate healthcare response based on the identified condition, including home treatment, an appointment with a healthcare provider, or emergency care. One of ordinary skill in the art would have been motivated to incorporate Bitran's health analysis and recommendation functionality into Rathod's communication analysis system to enable the information obtained from the user's communications to be used to identify health concerns and determine an appropriate healthcare interaction for addressing those concerns, which provide health recommendations and healthcare interactions that are responsive to the user's identified condition.
It would further have been obvious to one of ordinary skill in the art to modify the combined system of Rathod and Bitran to incorporate Heckerman's automatic generation of prepared questions for the individual member to ask the healthcare provider during the healthcare interaction. Heckerman teaches automatically generating questions for a patient to ask a medical professional during an appointment based on evaluation of the patient's medical data and further teaches tailoring the relevance or importance of such questions to each specific appointment or situation. Because the combined teachings of Rathod and Bitran provide health information derived from analyzed member communications and determine an appropriate healthcare interaction based on the identified health condition, one of ordinary skill in the art would have been motivated to use the resulting health information and determined type of healthcare interaction in Heckerman's question generation process so that the automatically prepared questions would be relevant to both the member's identified health condition and the healthcare interaction for which the member is being prepared. This modification would have predictably improved the usefulness of the healthcare interaction by providing the member with appointment specific questions concerning the identified health condition to discuss with the healthcare provider.
Regarding claim 4, Rathod, Bitran, and Heckerman teach the invention in claim 1, as discussed above, and further teach wherein the record is generated prior to an expected interaction with the healthcare provider by the individual member (Rathod [0132] “In one embodiment action or activity or status or log items may comprising one or more statement(s) or sentence(s), templates, selected or edited concepts, note taking methods including tree structure, charting, outlining, mapping, mind maps, timelines, unstructured notes, structured notes with customize fields or tags, details, visual notes, flow charts, cluster notes, reports, summary, story, blog, descriptions, database, message(s), paper form, multimedia content types including text, image(s), photos, symbols, diagram, presentation, video(s), extracts or part of video &, voice, map, scanned documents, calendar, script, query, keyword(s) in one or more languages, phrase(s), Boolean operators, rules, condition(s), semantic syntax, ontology with associated one or more accessible metadata or fields.” and
Bitran [0078] “In this aspect, the method may further comprise determining scheduling information for each of the plurality of health care providers and outputting on the display an appointment scheduling interface with available timeslots at the plurality of recommended healthcare providers for the recommended health care service, receiving a user selection of a timeslot and healthcare provider, and transmitting the user selected timeslot and user ID of the user to the selected healthcare provider to make an appointment. In this aspect, the method may further comprise outputting on the display a graphical representation of the plurality of health care providers, including a map indicating the location of the plurality of health care providers, and a route to at least one of the health care providers.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to generate the record prior to an expected interaction with a healthcare provider by modifying Rathod in view of Bitran. Rathod teaches generating user records or summaries (action items) representing user activities and information. Bitran teaches determining and scheduling interactions with healthcare providers, including selecting providers and appointment times, thereby establishing an expected healthcare interaction. It would have been obvious to generate or provide the user record prior to this scheduled interaction so that relevant user information is available in advance of the appointment, as pre-visit preparation using available user data is a well-known and beneficial practice in healthcare systems to improve communication and efficiency during the interaction. This modification represents a predictable use of prior art elements according to their established functions and would have yielded no more than expected results.
Regarding claim 5, Rathod, Bitran, and Heckerman teach the invention in claim 4, as discussed above, and further teach wherein the expected interaction is determined by the DCN (Bitran [0032] “The interpretation engine 28 may then subsequently synthesize the various pieces of data collected as described above, drawing inferences as to their relevance for the purpose of providing health recommendations. The interpretation engine 28 is configured to parse the user data 26 to organize and structure them into medically relevant information that can then be used as a basis for adding to the user personal history 30 for subsequently making health recommendations. The user personal history 30 comprises a body of various user data 26 that is personally specific to the user, which may include medically relevant information.” and Bitran [0078] “In this aspect, the method may further comprise determining scheduling information for each of the plurality of health care providers and outputting on the display an appointment scheduling interface with available timeslots at the plurality of recommended healthcare providers for the recommended health care service, receiving a user selection of a timeslot and healthcare provider, and transmitting the user selected timeslot and user ID of the user to the selected healthcare provider to make an appointment. In this aspect, the method may further comprise outputting on the display a graphical representation of the plurality of health care providers, including a map indicating the location of the plurality of health care providers, and a route to at least one of the health care providers.” and
Rathod [0058] “In one embodiment, monitoring & tracking user's one or more activities, actions, events, transactions, life stream, locations, behavior, facial expressions, emotions, movement, workflow, logs, follow-ups, environment, status & conditions based on one or more action or activity recognition research, algorithm, methods and systems, sensors, detectors, transducers, video cameras, audio recorders, imaging, RFID, barcodes, touch screens, devices including mobile, digital televisions, digital watch, digital pen, automations, scanners, robotics, computer system, computer chips or processors, instruments, speech & text recognition, video & face recognition, speech or voice sources, translating system, application, services, networks, logs, programming, and human mediated actions including analysis, logic, guess, selections, privacy settings, preferences, settings, inspection, checking, verification.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to determine an expected interaction with a healthcare provider based on analysis of user communications by modifying Rathod in view of Bitran. Rathod teaches analyzing user communications, activities, and interactions, including monitoring and processing the communications using analytical techniques. Bitran teaches using an interpretation engine to analyze user data and draw medically relevant inferences to generate health recommendations, and further teaches determining and scheduling interactions with healthcare providers, including selecting providers and appointment times. It would have been obvious to use the analyzed communications of Rathod as input to Bitran’s interpretation and recommendation processes to determine when an interaction with a healthcare provider is expected or appropriate, as healthcare systems routinely use available user data to guide and initiate provider interactions. This modification represents a predictable use of prior art elements according to their established functions and would have yielded no more than expected results.
Regarding claim 6, Rathod, Bitran, and Heckerman teach the invention in claim 1, as discussed above, and further teach wherein the record is prepared in at least one of a written format and a digital format (Rathod [0132] “In one embodiment action or activity or status or log items may comprising one or more statement(s) or sentence(s), templates, selected or edited concepts, note taking methods including tree structure, charting, outlining, mapping, mind maps, timelines, unstructured notes, structured notes with customize fields or tags, details, visual notes, flow charts, cluster notes, reports, summary, story, blog, descriptions, database, message(s), paper form, multimedia content types including text, image(s), photos, symbols, diagram, presentation, video(s), extracts or part of video &, voice, map, scanned documents, calendar, script, query, keyword(s) in one or more languages, phrase(s), Boolean operators, rules, condition(s), semantic syntax, ontology with associated one or more accessible metadata or fields.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to prepare the record in at least one of a written format and a digital format in view of Rathod. Rathod teaches generating user records or summaries (action items) in various formats, including written forms such as statements, reports, summaries, and blogs, as well as digital forms such as database entries, messages, and multimedia content. These disclosures encompass both written and digital formats for presenting user information. Selecting one or more of these known formats for presenting the generated record would have been a routine design choice and a predictable use of prior art elements according to their established functions, yielding no more than expected results.
Regarding claim 7, Rathod, Bitran, and Heckerman teach the invention in claim 1, as discussed above, and further teach further comprising: generating a link to the record; and providing the link to the healthcare provider (Rathod [0142] “In one embodiment, response(s) of action or activity or status or log item(s) comprises any types of resources and one or more multimedia contents including text, messages, emails, communications, web links, connections, videos, images, photos, albums, graphics, audio, voice, files, scanned documents, databases, applications, services, updated resources from internal or external sources.”, and Rathod [0198] “generating a plurality of action item(s) regarding one or more of the physical or digital activities, actions, interactions, communications, responses, events, transactions, life stream, logs, locations, behavior, movement, environment, status, states & conditions of each user with or without associating one or more active links with said action item(s), wherein said active links enable receiver or responder users to communicate & collaborate with sender, access active links, provide response(s) & service(s) to sender, enabling workflow, e-commerce transaction and participate in at least one of the activities as performed by the sender”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to generate a link to the record and provide the link to a healthcare provider in view of Rathod. Rathod teaches generating and associating active links with user-generated content, such as action items representing user activities and communications, where such links enable access to the associated information and facilitate sharing and interaction among users. It would have been obvious to use such links to provide access to the generated user record and to transmit or share the link with a healthcare provider, as providing a link to access user information represents a well-known and efficient method of sharing digital content with another party. This modification represents a predictable use of prior art elements according to their established functions and would have yielded no more than expected results.
Regarding claim 8, Rathod, Bitran, and Heckerman teach the invention in claim 1, as discussed above, and further teach wherein the lifestyle activities are prepared in at least one of a narrative form, a first person perspective, and a list form (Rathod [0271] “In another important embodiment, present invention can monitoring, tracking, recording, storing, processing any types of user actions, activities, events, transactions, life stream, life stream, behavior of user(s) in one or more networks based on specific devices including mobile, video camera, audio recorder, detector, sensors which monitors, tracks, records, stores, processes user's physical activities, works, process, movements, locations, health, conditions, environment, status including driving vehicle, entering into particular vehicle or building, purchasing of brands, subscribing of services, viewing of television, communicate with others, selecting something, viewing something, visiting anywhere, accessing something, interact with anything, going anywhere, preparing something, eating something, buying or selling something, user surrounding environment including weather, other users or connected or related users actions, activities, events, transactions, life stream, locations, interactions with user, detecting health or conditions, activities related to any types or categories of works, jobs, sports, health, entertainment, shopping, education, training, learning, arts, travels, food, lifestyle, games, finance.” and Rathod [0132] “In one embodiment action or activity or status or log items may comprising one or more statement(s) or sentence(s), templates, selected or edited concepts, note taking methods including tree structure, charting, outlining, mapping, mind maps, timelines, unstructured notes, structured notes with customize fields or tags, details, visual notes, flow charts, cluster notes, reports, summary, story, blog, descriptions, database, message(s), paper form, multimedia content types including text, image(s), photos, symbols, diagram, presentation, video(s), extracts or part of video &, voice, map, scanned documents, calendar, script, query, keyword(s) in one or more languages, phrase(s), Boolean operators, rules, condition(s), semantic syntax, ontology with associated one or more accessible metadata or fields.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to prepare lifestyle activities in at least one of a narrative form, a first-person perspective, and a list form in view of Rathod. Rathod teaches monitoring and recording user activities, including various lifestyle activities, and further teaches generating user content in multiple presentation formats, including narrative forms such as statements, summaries, stories, and blogs, as well as structured formats such as templates and structured notes. These disclosures encompass narrative-style and list presentations of user information. Selecting one or more of these known formats, including narrative or structured and list formats, for presenting lifestyle activities would have been a routine design choice and a predictable use of prior art elements according to their established functions, yielding no more than expected results.
Regarding claim 9, Rathod, Bitran, and Heckerman teach the invention in claim 8, as discussed above, and further teach wherein the list form comprises at least one section of new activities, activities continued, activities dropped, and activities contemplated (Rathod [0271] “In another important embodiment, present invention can monitoring, tracking, recording, storing, processing any types of user actions, activities, events, transactions, life stream, life stream, behavior of user(s) in one or more networks based on specific devices including mobile, video camera, audio recorder, detector, sensors which monitors, tracks, records, stores, processes user's physical activities, works, process, movements, locations, health, conditions, environment, status including driving vehicle, entering into particular vehicle or building, purchasing of brands, subscribing of services, viewing of television, communicate with others, selecting something, viewing something, visiting anywhere, accessing something, interact with anything, going anywhere, preparing something, eating something, buying or selling something, user surrounding environment including weather, other users or connected or related users actions, activities, events, transactions, life stream, locations, interactions with user, detecting health or conditions, activities related to any types or categories of works, jobs, sports, health, entertainment, shopping, education, training, learning, arts, travels, food, lifestyle, games, finance.” and Rathod [0132] “In one embodiment action or activity or status or log items may comprising one or more statement(s) or sentence(s), templates, selected or edited concepts, note taking methods including tree structure, charting, outlining, mapping, mind maps, timelines, unstructured notes, structured notes with customize fields or tags, details, visual notes, flow charts, cluster notes, reports, summary, story, blog, descriptions, database, message(s), paper form, multimedia content types including text, image(s), photos, symbols, diagram, presentation, video(s), extracts or part of video &, voice, map, scanned documents, calendar, script, query, keyword(s) in one or more languages, phrase(s), Boolean operators, rules, condition(s), semantic syntax, ontology with associated one or more accessible metadata or fields.” and Rathod [0347] “The action logger 520 includes data describing the member performing the action, the date & time the action occurred, an identifier for the member who performed the action, an identifier for the member to whom the action was directed, an identifier for the categories of action performed, an identifier for an object acted on by the action (e.g., an application), content associated with the action, identifying one or more objects associate with actions, dynamically identifying and associating one or more active links & applications or application features with action, who-what-where-when-how-where about the action occurred and/or other data describing the action. The action logger 520 can communicate with the all active links, objects, applications, services, groups, networks, data & content stores of network related to actions and/or user related to action. The action logger 525 can organize the stored action data according to an action identifier which uniquely identifies each stored action.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to organize the list of activities into sections such as new activities, activities continued, activities dropped, and activities contemplated in view of Rathod. Rathod teaches monitoring and recording user activities, maintaining a running list or feed of such activities over time, and organizing such activities using categorized and structured data, including identifiers for categories and temporal information such as date and time, as well as presenting such information in structured formats. Given that Rathod tracks and categorizes activities over time, it would have been obvious to further categorize such activities based on their temporal or status progression, such as newly initiated activities, ongoing activities, discontinued activities, and contemplated activities, as this represents a conventional way of organizing time activity data to improve clarity and usability. This modification constitutes a routine design choice and a predictable use of prior art elements according to their established functions, yielding no more than expected results.
Regarding claim 10, Rathod, Bitran, and Heckerman teach the invention in claim 1, as discussed above, and further teach wherein the biometric data is derived in part from at least one of biometric data and psychometric data submitted to the DCN by the individual member (Bitran [0019] “The medical data may comprise at least one of user's electronic medical record 42, biometric data, and medical device data. Biometric data may include a variety of data sensed by sensors on client computing device 12, medical device 22, or other client computing devices 16, such as pedometer information, heart rate and blood pressure, duration and timing of sleep cycles, body temperature, galvanic skin response, etc. Additional biometric data is discussed below in relation to the wristwatch embodiment of the wearable computing device 18. Medical device data may include data from medical device 22. Such data may include, for example, inhaler usage data from an electronic inhaler device, blood test results including blood sugar levels from an electronic blood sugar monitor, insulin pumping data from an electronic insulin pump, pulse oximetry data from an electronic pulse oximeter, gene expression data, etc. The blood test results may also comprise at least one of drug concentration, blood count, and metabolite concentration. It will be appreciated that these specific examples are merely illustrative and that other types of user data specifically not discussed above may also be monitored.” and
Rathod [0411] “plurality of ways to receives, imports, maintains & stores action or activity or status or log item(s) from plurality of sources of action or activity or status or log item(s) at the central unit or action server comprising receive action or activity or status or log item(s) from automatically and/or manually generated or drafted, monitored & tracked from plurality of sources including user, connected users of user, applications, services, communication channels, networks, groups, databases of present network and/or external domains and/or physical domains or places or locations or manually drafted and/or selected and/or send by user or connected, matched, related users of user or imports from external domains, web sites, web pages, databases, repositories, applications, services, devices, sensors and any digital sources.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to derive biometric data from biometric data submitted by a user in view of Bitran and Rathod. Bitran teaches collecting and utilizing biometric data from user-associated devices and sensors, such as heart rate, sleep data, and other physiological measurements, as part of user medical data. Rathod teaches receiving, importing, and storing user data from various sources, including user devices, sensors, and user-generated inputs within a communication network. It would have been obvious to use biometric data associated with a user as input to a communication network system and to derive user information from such data, since collecting and processing user-provided biometric data in a networked system represents a well-known and routine practice. This modification constitutes a predictable use of prior art elements according to their established functions and would have yielded no more than expected results.
Regarding claim 11, Rathod, Bitran, and Heckerman teach the invention in claim 10, as discussed above, and further teach wherein at least one of the biometric data and the psychometric data is derived from a biometric sample (Bitran [0019] “The medical data may comprise at least one of user's electronic medical record 42, biometric data, and medical device data. Biometric data may include a variety of data sensed by sensors on client computing device 12, medical device 22, or other client computing devices 16, such as pedometer information, heart rate and blood pressure, duration and timing of sleep cycles, body temperature, galvanic skin response, etc. Additional biometric data is discussed below in relation to the wristwatch embodiment of the wearable computing device 18. Medical device data may include data from medical device 22. Such data may include, for example, inhaler usage data from an electronic inhaler device, blood test results including blood sugar levels from an electronic blood sugar monitor, insulin pumping data from an electronic insulin pump, pulse oximetry data from an electronic pulse oximeter, gene expression data, etc. The blood test results may also comprise at least one of drug concentration, blood count, and metabolite concentration. It will be appreciated that these specific examples are merely illustrative and that other types of user data specifically not discussed above may also be monitored.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to derive biometric data from a biometric sample in view of Bitran. Bitran teaches collecting and utilizing biometric data, including physiological measurements and medical data such as blood test results, gene expression data, and other laboratory-derived information. This data is inherently derived from biological samples, such as blood or other bodily samples, as blood test results and similar medical measurements necessarily require obtaining and analyzing a sample from the individual. It would have been obvious to derive biometric data from such samples, as this represents a well-known and routine practice in medical and healthcare systems. This modification constitutes a predictable use of prior art elements according to their established functions and would have yielded no more than expected results.
Regarding claim 12, Rathod, Bitran, and Heckerman teach the invention in claim 11, as discussed above, and further teach wherein the biometric sample is obtained from at least one of a home test, a wearable device, a psychometric instrument, a psychosocial instrument, and a psychological instrument (Bitran [0019] “The medical data may comprise at least one of user's electronic medical record 42, biometric data, and medical device data. Biometric data may include a variety of data sensed by sensors on client computing device 12, medical device 22, or other client computing devices 16, such as pedometer information, heart rate and blood pressure, duration and timing of sleep cycles, body temperature, galvanic skin response, etc. Additional biometric data is discussed below in relation to the wristwatch embodiment of the wearable computing device 18. Medical device data may include data from medical device 22. Such data may include, for example, inhaler usage data from an electronic inhaler device, blood test results including blood sugar levels from an electronic blood sugar monitor, insulin pumping data from an electronic insulin pump, pulse oximetry data from an electronic pulse oximeter, gene expression data, etc. The blood test results may also comprise at least one of drug concentration, blood count, and metabolite concentration. It will be appreciated that these specific examples are merely illustrative and that other types of user data specifically not discussed above may also be monitored.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to obtain a biometric sample from a wearable device in view of Bitran. Bitran teaches collecting biometric data from user-associated devices and sensors, including physiological measurements such as heart rate, sleep data, and other metrics obtained from client computing devices and medical devices. These devices include wearable or user-associated devices that monitor physiological conditions and provide biometric data derived from the user. It would have been obvious to obtain biometric data or samples from such wearable devices, as using wearable or sensor devices to collect physiological data represents a well-known and routine practice in healthcare systems. This modification constitutes a predictable use of prior art elements according to their established functions and would have yielded no more than expected results.
Regarding claim 13, Rathod, Bitran, and Heckerman teach the invention in claim 12, as discussed above, and further teach wherein the wearable device comprises at least one of an accelerometer, a HR monitor, a HRV monitor, a continuous glucose monitor, a continuous ketone monitor, a skin temperature monitor (Bitran [0019] “The medical data may comprise at least one of user's electronic medical record 42, biometric data, and medical device data. Biometric data may include a variety of data sensed by sensors on client computing device 12, medical device 22, or other client computing devices 16, such as pedometer information, heart rate and blood pressure, duration and timing of sleep cycles, body temperature, galvanic skin response, etc. Additional biometric data is discussed below in relation to the wristwatch embodiment of the wearable computing device 18. Medical device data may include data from medical device 22. Such data may include, for example, inhaler usage data from an electronic inhaler device, blood test results including blood sugar levels from an electronic blood sugar monitor, insulin pumping data from an electronic insulin pump, pulse oximetry data from an electronic pulse oximeter, gene expression data, etc. The blood test results may also comprise at least one of drug concentration, blood count, and metabolite concentration. It will be appreciated that these specific examples are merely illustrative and that other types of user data specifically not discussed above may also be monitored.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize a wearable device comprising at least one of the claimed sensors in view of Bitran. Bitran teaches collecting biometric data from user-associated devices and sensors, including physiological measurements such as heart rate, body temperature, and pedometer data, which correspond to wearable sensors such as heart rate monitors, temperature monitors, and accelerometer devices. It would have been obvious to implement these known sensors in wearable devices to collect biometric data, as the use of wearable devices incorporating physiological sensors represents a well-known and routine practice in healthcare and monitoring systems. The selection of particular sensors, such as heart rate or motion sensors, constitutes a predictable use of prior art elements according to their established functions and would have yielded no more than expected results.
Regarding claim 14, Rathod, Bitran, and Heckerman teach the invention in claim 11, as discussed above, and further teach wherein the biometric sample comprises at least one of a saliva sample, a blood sample, a breath sample and a stool sample (Bitran [0019] “The medical data may comprise at least one of user's electronic medical record 42, biometric data, and medical device data. Biometric data may include a variety of data sensed by sensors on client computing device 12, medical device 22, or other client computing devices 16, such as pedometer information, heart rate and blood pressure, duration and timing of sleep cycles, body temperature, galvanic skin response, etc. Additional biometric data is discussed below in relation to the wristwatch embodiment of the wearable computing device 18. Medical device data may include data from medical device 22. Such data may include, for example, inhaler usage data from an electronic inhaler device, blood test results including blood sugar levels from an electronic blood sugar monitor, insulin pumping data from an electronic insulin pump, pulse oximetry data from an electronic pulse oximeter, gene expression data, etc. The blood test results may also comprise at least one of drug concentration, blood count, and metabolite concentration. It will be appreciated that these specific examples are merely illustrative and that other types of user data specifically not discussed above may also be monitored.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to obtain a biometric sample comprising at least one of the claimed samples in view of Bitran. Bitran teaches collecting and utilizing medical and biometric data, including blood test results and other laboratory-derived measurements. Such data is inherently derived from biological samples, such as blood samples, since blood test results necessarily require obtaining and analyzing a sample from the individual. It would have been obvious to obtain biometric samples, such as blood samples, as part of collecting biometric data, as this represents a well-known and routine practice in healthcare systems. The selection of particular types of samples constitutes a predictable use of prior art elements according to their established functions and would have yielded no more than expected results
Regarding claim 15, Rathod, Bitran, and Heckerman teach the invention in claim 12, as discussed above, and further teach further comprising providing the home test through the DCN (Bitran [0019] “The medical data may comprise at least one of user's electronic medical record 42, biometric data, and medical device data. Biometric data may include a variety of data sensed by sensors on client computing device 12, medical device 22, or other client computing devices 16, such as pedometer information, heart rate and blood pressure, duration and timing of sleep cycles, body temperature, galvanic skin response, etc. Additional biometric data is discussed below in relation to the wristwatch embodiment of the wearable computing device 18. Medical device data may include data from medical device 22. Such data may include, for example, inhaler usage data from an electronic inhaler device, blood test results including blood sugar levels from an electronic blood sugar monitor, insulin pumping data from an electronic insulin pump, pulse oximetry data from an electronic pulse oximeter, gene expression data, etc. The blood test results may also comprise at least one of drug concentration, blood count, and metabolite concentration. It will be appreciated that these specific examples are merely illustrative and that other types of user data specifically not discussed above may also be monitored.” and
Rathod [0275] “In one embodiment, for example system can automatically monitor, track, record, store, process health type action or status related one or more action or activity or status or log item(s) based on sensors, medical devices, video camera, voice detection via mobile or voice enabled technologies, scanned documents & text recognitions, transactions, life stream, input by health service providers or doctors or sender and associate one or more active links or user manually input health related information including health reports, food intake types, medicines currently user use and like and manually associate one or more active links and send to related target receivers including family doctors, specialist doctors, hospitals, medical stores, other marketing agencies, connected users including family, friends, co-workers, like minded users, subscribers and like, wherein said action item(s) associate one or more active links enables said receivers to consult sender, sell medicines or products & services, communicate with sender and like.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to provide a home test through the digital communication network in view of Bitran and Rathod. Bitran teaches collecting and utilizing medical and biometric data obtained from various medical devices and diagnostic tests, including blood test results and other physiological measurements, thereby evidencing the use of testing mechanisms to obtain user health data. Rathod teaches a network-based system that processes and distributes health-related information and associates such information with active links that enable users to access healthcare-related services, products, and interactions with providers through the network. It would have been obvious to a person of ordinary skill in the art to extend Rathod’s network-based delivery of health resources to include providing diagnostic tools, such as home tests, through the network to facilitate convenient acquisition of biometric data as taught by Bitran. Such a modification represents the predictable use of prior art elements according to their established functions, using a communication network to deliver healthcare-related tools and services, and would have yielded no more than expected results.
Regarding claim 16, Rathod, Bitran, and Heckerman teach the invention in claim 1, as discussed above, and further teach wherein the record further includes analytics based on a combination of a plurality of biometric readings and changes in the biometric readings over time (Rathod [0348] “The action item(s) generator 510 generates communications for each member about information that may be relevant to the member. These communications may take the form of action item(s), each action item(s) is an information message comprising one or a few lines of information about an action in the action log that is relevant to the particular member. The action item(s) are presented to a member via one or more pages of the social networking website 100, for example in each member's home page or action item(s) page. An action item(s) is a message that summarizes, condenses, or abstracts one or more member actions from the action log 525. The generated action item(s) can then be transmitted to one or more related members e.g., the member's connected users, friends, subscribers, and auto matched receivers or responders allowing the member's actions to be shared with related members. More about user action describe in U.S. patent application Ser. No. 11/995,343, titled: “A method and system for communication, publishing, grouping, advertising, searching, sharing and dynamically providing a Journal Feed” and divisional U.S. patent application Ser. No. 12/973,370, titled: “A System and method for publishing, communication and real time searching”.” and
Bitran [0019] “The medical data may comprise at least one of user's electronic medical record 42, biometric data, and medical device data. Biometric data may include a variety of data sensed by sensors on client computing device 12, medical device 22, or other client computing devices 16, such as pedometer information, heart rate and blood pressure, duration and timing of sleep cycles, body temperature, galvanic skin response, etc. Additional biometric data is discussed below in relation to the wristwatch embodiment of the wearable computing device 18. Medical device data may include data from medical device 22. Such data may include, for example, inhaler usage data from an electronic inhaler device, blood test results including blood sugar levels from an electronic blood sugar monitor, insulin pumping data from an electronic insulin pump, pulse oximetry data from an electronic pulse oximeter, gene expression data, etc. The blood test results may also comprise at least one of drug concentration, blood count, and metabolite concentration. It will be appreciated that these specific examples are merely illustrative and that other types of user data specifically not discussed above may also be monitored.” and Bitran [0046] “Referring to FIG. 6, one possible implementation of the health recommender 25 is illustrated for a patient with trouble sleeping. In this example, the health recommender 25 is configured to display on the computing device 12 an interface 200 for making health recommendations. A biometric sleep sensor, which is a wearable computing device 18, detects 4 hours and 7 minutes of sleep and only 1 hour and 43 minutes of restful sleep from a patient suffering from insomnia. The user data 26 containing the biometric sleep data is sent to the interpretation engine 28, which interprets the biometric data and parses it into sleep data that is transmitted and stored in the user personal history 30. The health recommender 25 identifies the signs of insomnia from the sleep data that is retrieved from the user personal history 30. The machine-learning algorithm 29 implemented by the health recommender 25 is configured to process a variety of data from various sources, including other signs and symptoms reported by the user, health records, online behavior data, location data, and context information to determine a health recommendation 202.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to include analytics based on a combination of a plurality of biometric readings and changes in those readings over time in view of Bitran and Rathod. Bitran teaches collecting multiple types of biometric data from wearable and medical devices, including physiological measurements such as sleep duration, heart rate, and other health metrics, and further teaches processing such data using an interpretation engine and machine learning algorithms to generate health-related insights based on the user’s biometric data over time. Such processing inherently involves analyzing multiple readings and evaluating variations or changes in those readings to identify conditions such as insomnia. Rathod teaches generating summaries and representations of user-related data, including condensing and abstracting user activity information into a record or feed. It would have been obvious to incorporate analyzed biometric trends and changes over time into such a record to provide meaningful health insights, as analyzing temporal changes in biometric data represents a well-known and routine practice in health monitoring systems. This combination constitutes the predictable use of prior art elements according to their established functions and would have yielded no more than expected results.
Regarding claim 17, Rathod, Bitran, and Heckerman teach the invention in claim 1, as discussed above, and further teach wherein the information regarding relationships is determined by analyzing at least one of contents of posts in the DCN and the at least one communications in the DCN (Rathod 0058] “In one embodiment, monitoring & tracking user's one or more activities, actions, events, transactions, life stream, locations, behavior, facial expressions, emotions, movement, workflow, logs, follow-ups, environment, status & conditions based on one or more action or activity recognition research, algorithm, methods and systems, sensors, detectors, transducers, video cameras, audio recorders, imaging, RFID, barcodes, touch screens, devices including mobile, digital televisions, digital watch, digital pen, automations, scanners, robotics, computer system, computer chips or processors, instruments, speech & text recognition, video & face recognition, speech or voice sources, translating system, application, services, networks, logs, programming, and human mediated actions including analysis, logic, guess, selections, privacy settings, preferences, settings, inspection, checking, verification.” and Rathod [0292] “In one embodiment, action or Activity or Status or Log Feed shows the latest actions or activities or status or logs that user's friends are taking on social network, external domain, outside world, physical actions, such as photo uploads, comments, posts, profile changes, and others.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to determine relationship information by analyzing contents of posts and communications in a digital communication network in view of Rathod. Rathod teaches a social networking system in which user activities, including posts, comments, and interactions among connected users, are monitored and presented in activity feeds reflecting relationships between users such as friends or connected members. Rathod further teaches analyzing user activities, communications, and interactions using various analytical techniques to track and interpret user behavior and relationships within the network. It would have been obvious to analyze such posts and communications to determine relationship information between users, as deriving social connections and relationships from user interactions represents a well-known and routine practice in social networking systems. This modification constitutes the predictable use of prior art elements according to their established functions and would have yielded no more than expected results.
Regarding claim 18, Rathod, Bitran, and Heckerman teach the invention in claim 1, as discussed above, and further teach wherein the method is used to at least one of: manage health care cost of the population; reduce health care risk in the population; and slow progression of an adverse health condition (Bitran [0035] “Referring to FIG.2, the personal assistant application program 24 may further include a health recommender 25, which is configured to identify at least one health condition of the user, determine a health recommendation that is based at least on the combined time and location based data, a user's health insurance information, user electronic medical record 42, the identified health condition, and output the health recommendation to a display associated with the computing device. The health recommender 25 is instantiated in the application 24 in the client computing device 12 and communicates with the electronic personal assistant application server 66, which receives the user personal history 30, user electronic medical record 42, and combined time and location-data including the first aggregated history 38 and the second aggregated history 54. Based upon the above inputs, which may also comprise user input from the user, a healthcare provider that reported the signs, a search query entered by the user, a browsing history of the user, and/or sensor data received from a sensor associated with the computing device 12, the health recommender 25 may identify the health condition.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to determine relationship information by analyzing contents of posts and communications in a digital communication network in view of Rathod. Rathod teaches a social networking system in which user activities, including posts, comments, and interactions among connected users, are monitored and presented in activity feeds reflecting relationships between users such as friends or connected members. Rathod further teaches analyzing user activities, communications, and interactions using various analytical techniques to track and interpret user behavior and relationships within the network. It would have been obvious to analyze such posts and communications to determine relationship information between users, as deriving social connections and relationships from user interactions represents a well-known and routine practice in social networking systems. This modification constitutes the predictable use of prior art elements according to their established functions and would have yielded no more than expected results.
Claims 19 and 20 are analogous to claim 1, thus claims 19 and 20 similarly analyzed and rejected in a manner consistent with the rejection of claim 1.
Response to Arguments
Applicant’s arguments and amendments, see Remarks/Amendments submitted on 07/14/2026 with respect to the rejection of the claims have been carefully considered and is addressed below.
Claim Rejections - 35 USC § 101
The Applicant's arguments and amendments to the claims have been fully considered but are not persuasive to overcome the rejection under 35 U.S.C. 101. The Applicant states that the amended claims integrate the judicial exceptions into a practical application because the claims improve communication between an individual member and a healthcare provider by analyzing communications to determine an adverse health condition, predicting an expected healthcare interaction and type of interaction, and generating questions based on the analyzed information and type of interaction for the individual member to ask the healthcare provider. However, the asserted improvements result from the abstract processes themselves. Generating questions that are relevant to an individual's health condition and an anticipated healthcare interaction can facilitate communication between the individual and healthcare provider, but this is an improvement to the content and organization of information and to the communication facilitated by that information, instead of an improvement to the functioning of a computer or technical field. Therefore, the amendments further describe how the recited information is analyzed and used to facilitate the healthcare interaction, and do not demonstrate that the judicial exceptions are integrated into a practical application.
The Examiner has also considered the additional elements both individually and as an ordered combination, consistent with the Applicant's statement that the claims must be considered as a whole. When considered in combination, the recited DCN and computer components merely provide the tools through which the abstract processes are performed. The claims do not recite a particular technological mechanism for analyzing the communications, determining the adverse health condition, predicting the expected interaction or its type, or generating the prepared questions and record. The claims do not also recite an improvement to the operation of the DCN, computer processor, communication analyzer, data repository, or server controller. Instead, the computer components are used according to their ordinary functions to implement the recited information analysis, organization, and communication activities. Accordingly, considering these elements together with the judicial exceptions does not alter the conclusion that the claims merely use computer technology as a tool to carry out the abstract ideas.
Lastly, the Applicant's statement on the amended sequence of determining an adverse health condition, predicting the type of expected healthcare interaction, and generating questions for that interaction does not establish a practical application because these limitations themselves form part of the judicial exceptions. Evaluating communications to determine a health condition and predict an anticipated interaction constitutes observations, evaluations, and judgments, while preparing questions for an individual to ask a healthcare provider manages an interaction between people. Accordingly, even when the amendments and claims are considered as a whole, the additional elements do not impose a meaningful limitation that integrates the judicial exceptions into a practical application. Therefore, the Applicant's arguments and amendments do not overcome the rejection under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
Applicant’s arguments traversing the prior art rejection in the previous Office Action have been fully considered. However, those arguments are rendered moot because the present rejection under 35 U.S.C. §103 relies on a different set of prior art references (Rathod, Bitran, and Heckerman), which teach or suggest the limitations of the claims. Accordingly, Applicant’s prior arguments are not responsive to the current grounds of rejection. The rejection of claims 1 and 4-20 under 35 U.S.C. 103 is therefore maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
Devarakonda et al. (U.S. Publication 2019/0206517 A1) teaches a system that monitors a medical professional’s interactions with a patient’s electronic medical record (EMR), predicts the questions the professional is seeking to answer, analyzes the EMR to generate corresponding answers, and provides a report correlating the predicted question with the answers.
Carty et al. (U.S. Publication 2014/0207486 A1) teaches a decentralized care coordination system comprising a server and multiple remote devices (member, caregiver, and support devices) that communicate over a network to form a community of care network, enabling management of member information and facilitating the receipt and distribution of behavior communications in response to health events.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K.R.L./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
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