DETAILED ACTION
Notice of Pre-AIA or AIA Status
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 Nonfinal Office Action is in response to the RCE filed 04/22/2026, where: Claims 28, 36 and 44 are amended. Claims 28, 29 and 31-48 are pending and considered herein.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/22/2026 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 28, 29 and 31-48 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claims 28, 36 and 44, The applicant states in the remarks at Page 11 that support for the amended independent claims (the only substantive amendments presented) is included in Paras. [0042], [0124], [0127], [0128], and [0176] and that “No new matter has been added.”
However, there does not appear to be direct support in the Specification for the amended limitations to “electronically locate, using the outputted media content, one or more websites” and “transmit, to the first client device, at least one link to the one or more websites.” Para. [0042] promotes user engagement by suggesting different types of media and the “server may suggest and/or send a link to an online invite for a new gardening club if the user typically reads about gardening.” The link is supported but there is no direct support for “one or more websites,” as claimed. Para. [0176] also describes to “scan the internet for preferred content” and “providing links thereto,” but the ”one or more websites” as claimed is not supported. Para. [0080] describes an output component and states “Web browsers enable users, such as user 204, to display and interact with media and other information typically embedded on a web page, a website, or an application hosted by ECSP computing device 102 and/or client device 104. A client application may allow user 204 to interact with, for example, any of ECSP computing device 102, client devices 104, and third party servers 108.” This is not the same as what is claimed. The dependent claims inherit the deficiencies of the independent claims. Appropriate correction is required.
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 28, 29 and 31-48 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 28 recites, wherein the abstract elements are not emboldened:
An engagement and care support platform ("ECSP") computer system for providing personalized digital data, the ECSP computer system comprising at least one processor and at least one memory device in communication therewith, the at least one processor programmed to: register a user through an application executing on a first client device, wherein the user inputs user data associated with the user including user preferences; collect sensor data relating to the user from at least one of the first client device, a wearable device associated with the user, or a smart device located proximate to the user; generate a user profile for the user including the user data and the sensor data; build a daily interactive user interface including displayable content for display on the first client device, the content displayed on the daily interactive user interface including upcoming activities scheduled for the user and at least one type of media; train at least one machine learning model using training data including data from the user profile, the at least one machine learning model trained to refine outputs specific to the user to encourage engagement of the user with the daily interactive user interface; using the at least one trained machine learning model and without receiving further user data from the user, output media content for display on the daily interactive user interface as the at least one type of media that will cause the user to engage with the application; electronically locate, using the outputted media content, one or more websites for one or more upcoming events associated with the outputted media content; transmit, to the first client device, at least one online link to the one or more websites, wherein the at least one online link enables the first client device to access information associated with the one or more upcoming events; cause the first client device to display the daily interactive user interface including the upcoming activities scheduled for the user, the outputted media content, and the at least one online link for causing the user to engage with the application; receive user-satisfaction data indicating a level of satisfaction experienced by the user; and re-train the at least one trained machine learning model using the user-satisfaction data, thereby increasing accuracy of the outputs provided by the at least one trained machine learning model.
Independent claims 36 and 44 recite similar limitations. The claimed invention is broadly directed to the abstract idea of collecting user information, analyzing an interaction with a computer device, and determining whether a user of the computer device has interacted or will interact with the display of a computer device.
The limitations to “register a user, wherein the user inputs user data associated with the user including user preferences; collect data relating to the user … located proximate to the user; generate a user profile for the user including the user data; build a daily interactive user interface including displayable content, the content displayed on the daily interactive user interface including upcoming activities scheduled for the user and at least one type of media; using the user profile and without prompting the user to input further data, identify media content as the at least one type of media, configured to identify media content that will cause the user to engage, to encourage engagement of the user with the daily interactive user interface; output media content for display on as the at least one type of media that will cause the user to engage with the application; locate, using the outputted media content, one or more websites for one or more upcoming events associated with the outputted media content; at least one online link to the one or more websites, wherein the at least one to display the upcoming activities scheduled for the user, the outputted media content, and the at least one online link for causing the user to engage with the application; and receive user-satisfaction data indicating a level of satisfaction experienced by the user,” as drafted, is a process that, under its broadest reasonable interpretation, is an abstract idea that covers performance of the limitation as organizing human activity including following rules or instructions. The claim recites, as a whole, a method of organizing human activity because the limitations include a method that allows users to access a user’s data, analyze the data and some indication, and updating engagement by the user based on the analyses. This is a method of managing interactions between people. Under its broadest reasonable interpretation, the limitations are categorized as methods of organizing human activity, specifically associated with managing personal behavior or relationships or interactions between people. Therefore, the limitation falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a). The mere nominal recitation of a generic display/interface and generic computer devices, sensors, electronically locating websites and machine learning tools does not take the claims out of the method of organizing human interactions grouping. The limitations seem to monopolize the abstract idea of senior patient analysis and general diagnostic techniques between a clinician and her patient that are well-established and conventional. Thus, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “computer systems,” a “processor,” a “memory device,” a “wearable device or smart device,” “client devices,” training or retraining a “machine learning model”, “sensors” and an “application,” “website or online link” and interface/display, which are recited at a high-level of generality (i.e., as a generic processor/server/storage/display/sensor performing a generic computer function of receiving inputs and displaying selected information) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. In addition, there is no clear improvement to the underlying technology in the claim. The additional elements are merely used as a tool to apply the abstract idea (i.e., to control sending or receiving of data, see MPEP 2106.05(f)(2)). The additional elements generally link the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Therefore, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is thus directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “computer systems,” a “processor,” a “memory device,” a “wearable device or smart device,” “client devices,” training or retraining a “machine learning model”, “sensors” and an “application,” “website or online link” and interface/display, alone or in combination, amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Furthermore, the additional element of electronically locating websites and an online link generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more than the abstract idea (judicial exception). Therefore, when considering the additional elements alone and in combination, there is no inventive concept in the claim. Thus, the claim is not patent eligible.
Furthermore, the dependent claims do not remedy the deficiencies of the independent claims with respect to patent eligible subject matter, but merely further limit the abstract idea. Dependent claims 34 and 42 include a verbal interaction which merely adds computer components such as a microphone to the abstract idea and amount to mere instructions to apply an exception. Dependent claims 32, 40 and 47 include a touch interaction received at a client computer device. The verbal acknowledgement and the touch interaction are recited at a high level of generality such that they amount to no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the implied microphone/speaker and verbal acknowledgement and touch screen interaction do not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claims 34-35 and 42-43 recite a chatbot for audio prompts, which is recited at a high level of generality such that they amount no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the chatbot and prompts do not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claim 48 details a reward system and further limits the abstract idea. The additional limitations in the remaining dependent claims merely detail a type of data input or calculated or displayed, or sending and receiving and storing and retrieving of information. Therefore, the claims are not patent eligible.
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 28, 31, 36, 39, 44 and 46 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2017/0214758 A1 to Engel, hereinafter “Engel,” in view of U.S. 11,188,840 B1 to Rivera et al., hereinafter “Rivera,” in view of U.S. 2009/0259492 A1 to Cossman, hereinafter “Cossman,” in view of U.S. 2019/0043086 A1 to Busey et al., hereinafter “Busey,” in view of U.S. 2020/0126670 A1 to Bender et al., hereinafter “Bender” and further in view of U.S. 7,885,902 B1 to Shoemaker et al., hereinafter “Shoemaker.”
Regarding claim 28, Engel discloses An engagement and care support platform ("ECSP") computer system for providing personalized digital data, the ECSP computer system including at least one processor in communication with at least one memory device in communication therewith (See Engel at Figs. 1-8), the at least one processor is programmed to: collect sensor data relating to the user from at least one of the first client device, a wearable device associated with the user, or a smart device located proximate to the user (See id. at least at Paras. [0024], [0080], [0107], [0111]-[0112]; Figs. 1-8); generate a user profile for the user including the user data and the sensor data (See id. at least at Paras. [0027]-[0037], [0054]-[0069] (“[A] particular user interaction record 402 includes a user ID 404 (e.g., an identifier for a particular user of the server system (e.g., the server system 120 in FIG. 1), an origin value 406 (e.g., a identifier of the service through which the interaction was received), a time 408, an interaction type 410 (e.g., details of the interaction), an interaction family 412 (e.g., the general category into which the interaction falls), a device 414 (e.g., an identifier for the particular device from which the interaction record was received)”); Claims 6-10; Figs. 1-8); build a daily interactive user interface including displayable content for display on the first client device, the content displayed on the daily interactive user interface (See id. at least at Paras. [0069]-[0074]; Figs. 1-8).
Engel may not specifically describe but Rivera teaches to train at least one machine learning model using training data including data from the user profile, the at least one machine learning model trained to refine outputs specific to the user to encourage engagement of the user with the daily interactive user interface (See Rivera at least at Abstract; Col. 1, ln. 46 – Col. 2, ln. 53 (machine learning tools and user interaction); Col. 4, ln. 15-20 (“After a user provides input through a first page of the web application 132, successive pages may dynamically generate content displayed to the user based on the input from the first page. The sequence of pages shown to a user may vary depending on the information the user provides.”); Col. 7, ln. 4-51 (“At regular intervals during the interaction session, the retention module 134 may also predict the next action a user will take and customize the page(s) and/or content presented to the user based on the prediction [media content]. The retention module 134 may predict the next action via a fourth machine-learning model (as described in greater detail in FIG. 2) based on the retention-prediction value, the reason (if the retention-prediction value meets the threshold retention-prediction value), and input features based on the composite information set.”); Col. 11, ln. 50 – Col. 12, ln. 33 (An “action for increasing the probability that the user will complete the target action.”); Col. 13, ln. 39-53; Claims 1, 5; Figs. 2-4); using the at least one trained machine learning model, output media content for display on the daily interactive user interface as the at least one type of media that will cause the user to engage with the application (See id. at least at Abstract; Col. 1, ln. 46 – Col. 2, ln. 53 (machine learning tools and user interaction); Col. 4, ln. 15-20 (“After a user provides input through a first page of the web application 132, successive pages may dynamically generate content displayed to the user based on the input from the first page. The sequence of pages shown to a user may vary depending on the information the user provides.”); Col. 7, ln. 4-51 (“At regular intervals during the interaction session, the retention module 134 may also predict the next action a user will take and customize the page(s) and/or content presented to the user based on the prediction [media content]. The retention module 134 may predict the next action via a fourth machine-learning model (as described in greater detail in FIG. 2) based on the retention-prediction value, the reason (if the retention-prediction value meets the threshold retention-prediction value), and input features based on the composite information set […] the training data 210 may also comprise attributes and labels that describe any intervention actions taken during those previous interaction sessions, whether the users completed the target actions for the previous interactions sessions, and the underlying reasons (e.g., as predicted by the third machine learning model 216 or as verified by surveying the users) why any of those users did not complete the target actions during the previous interaction sessions.); Col. 11, ln. 50 – Col. 12, ln. 33 (An “action for increasing the probability that the user will complete the target action.”); Col. 13, ln. 39-53; Claims 1, 5; Figs. 2-4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Engel to incorporate the teachings of Rivera and provide machine learning tools and encouraging interaction. Rivera is directed to machine learning models to facilitate user retention. Incorporating the machine learning models and techniques of Rivera with the tracking and encouraging of user interactions as in Engel would thereby improve the applicability, efficacy, and accuracy of engagement and care support platforms.
The references may not specifically describe but Bender teaches using a machine learning model and applying without receiving further the user data from the user (See Bender at least at Paras. [0107] (“Embodiments herein can provide enhanced user interface functionality so that actions of a first user can be automatically sensed by a second user and, further, so that inputs can be provided by a computer system without manual input on behalf of the user […] Various decision data structures can be used to drive artificial intelligence (AI) decision making, such as decision data structure that cognitively maps stress levels to haptic feedback selections. Decision data structures as set forth herein can be updated by machine learning so that accuracy and reliability is iteratively improved over time.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Engel and Rivera to incorporate the teachings of Bender and provide machine learning tools, registering devices and clients, and displaying pertinent results. Bender is directed to machine learning and using biometric signals of a patient. Incorporating the machine learning without further user prompts as in Bender with the machine learning models and techniques of Rivera with the tracking and encouraging of user interactions as in Engel would thereby improve the applicability, efficacy, and accuracy of engagement and care support platforms.
The references may not specifically describe but Cossman teaches to register a user through an application executing on a first client device, wherein the user inputs user data associated with the user including user preferences (See Cossman at least at Abstract; Paras. [0026]-[0030] (patient registered and doctor registered), [0033]-[0037] (Predetermined schedule. “[S]tored consultation information is accessed, viewed, and/or downloaded by a doctor from the service, and/or a report is sent by the service to the doctor or retrieved by the doctor from the service […] The same or similar information, groupings, summaries, and the like can be provided for viewing and/or downloading by the patient, such as via an interactive secure web site [application on first client device] provided by the service.”), [0045]-[0050] (“The web server can interact with a doctor and/or a patient to register them to use the remote consultation service. The web server can also authorize users to access consultation information stored in data storage device 470, and can present, group, summarize, and/or otherwise manipulate the stored information for viewing [interactive user interface with preferences] and/or downloading by authorized users.”); Claims 21, 23; Figs. 3-6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Engel, Rivera and Bender to incorporate the teachings of Cossman and provide machine learning tools, registering devices and clients, and displaying pertinent results. Cossman relates to a remote consultation system. Incorporating the consultation and preferences as in Cossman with the machine learning without further user prompts as in Bender with the machine learning models and techniques of Rivera with the tracking and encouraging of user interactions as in Engel would thereby improve the applicability, efficacy, and accuracy of engagement and care support platforms.
The references may not specifically describe but Busey teaches to cause the first client device to display the daily interactive user interface including the upcoming activities scheduled for the user, the outputted media content, and the at least one online link for causing the user to engage with the application (See Busey at least at Abstract (“[M]ethods for automatic, long-term, multi-stage user engagement that facilitate repeated user interaction from the provision of a single link to content.”); Paras. [0003]-[0012] (“[L]ong-term, multi-stage user engagement comprising: an interaction module identifying a user that interacts with a link providing access to content, wherein the identity of the user on an electronic communications platform is known, and wherein the content is associated with a product or an event; […] a re-engagement module automatically re-engaging with the user by sending the user a first message comprising additional content via an interactive chat feature of the electronic communications platform, wherein the additional content is associated with the product or the event. In some embodiments, the link is public or private. In some embodiments, the content or the additional content comprises a date associated with the product or the event.”), [0053]-[0056] (“[U]pon interacting with a link directed to brand content 101, the user is delivered content associated with a brand or product of interest 102. In some embodiments, the brand content is information about a commercial product or event.”), [0061]-[0063], [0072]-[0074] (client device to display on interface); Figs. 1-7); electronically locate, using the outputted media content, one or more websites for one or more upcoming events associated with the outputted media content (See id. at least at Abstract (“[M]ethods for automatic, long-term, multi-stage user engagement that facilitate repeated user interaction from the provision of a single link to content.”); Paras. [0003]-[0012] (“[L]ong-term, multi-stage user engagement comprising: an interaction module identifying a user that interacts with a link providing access to content, wherein the identity of the user on an electronic communications platform is known, and wherein the content is associated with a product or an event; […] a re-engagement module automatically re-engaging with the user by sending the user a first message comprising additional content via an interactive chat feature of the electronic communications platform, wherein the additional content is associated with the product or the event. In some embodiments, the link is public or private. In some embodiments, the content or the additional content comprises a date associated with the product or the event.”), [0053]-[0056] (“[U]pon interacting with a link directed to brand content 101, the user is delivered content associated with a brand or product of interest 102. In some embodiments, the brand content is information about a commercial product or event.”), [0061]-[0067] ([“A] user is presented with a Facebook messenger destination advertisement 401 in a Facebook or Instagram feed in which the user is offered additional content to a brand, product, or event […] once the movie is close to release, the user is automatically sent an additional Facebook Messenger message containing an image reminding the user of the movie of interest 308 and giving the user the option to automatically find additional relevant information about the move premiere (e.g., a “Find showtimes” link) […] the user is given the option to subscribe to future notifications surrounding “Who We Are” and automatically be sent additional message with content or information about the movie. For example, the chat bot asks the user 407, “Want to receive Who We Are release reminders and updates?” The user can then interact with the “Yes!” or “No” links 408 (or send an equivalent chat message) 408a to indicate their preference for future notifications.”), [0072]-[0074] (client device to display on interface); Figs. 1-7); and transmit, to the first client device, at least one online link to the one or more websites, wherein the at least one online link enables the first client device to access information associated with the one or more upcoming events (See Busey at least at Abstract (“[M]ethods for automatic, long-term, multi-stage user engagement that facilitate repeated user interaction from the provision of a single link to content.”); Paras. [0003]-[0012] (“[L]ong-term, multi-stage user engagement comprising: an interaction module identifying a user that interacts with a link providing access to content, wherein the identity of the user on an electronic communications platform is known, and wherein the content is associated with a product or an event; […] a re-engagement module automatically re-engaging with the user by sending the user a first message comprising additional content via an interactive chat feature of the electronic communications platform, wherein the additional content is associated with the product or the event. In some embodiments, the link is public or private. In some embodiments, the content or the additional content comprises a date associated with the product or the event.”), [0061]-[0067] ([“A] user is presented with a Facebook messenger destination advertisement 401 in a Facebook or Instagram feed in which the user is offered additional content to a brand, product, or event […] once the movie is close to release, the user is automatically sent an additional Facebook Messenger message containing an image reminding the user of the movie of interest 308 and giving the user the option to automatically find additional relevant information about the move premiere (e.g., a “Find showtimes” link) […] the user is given the option to subscribe to future notifications surrounding “Who We Are” and automatically be sent additional message with content or information about the movie. For example, the chat bot asks the user 407, “Want to receive Who We Are release reminders and updates?” The user can then interact with the “Yes!” or “No” links 408 (or send an equivalent chat message) 408a to indicate their preference for future notifications.”), [0072]-[0074] (client device to display on interface); Figs. 1-7).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Engel, Rivera, Bender and Cossman to incorporate the teachings of Busey and provide preferences, engagement and links to websites and machine learning. Busey is directed to systems for long-term multi-stage user engagement. Incorporating the user engagement and techniques of Busey with the consultation and preferences as in Cossman, the machine learning without further user prompts as in Bender with the machine learning models and techniques of Rivera with the tracking and encouraging of user interactions as in Engel would thereby improve the applicability, efficacy, and accuracy of engagement and care support platforms.
The references may not specifically describe but Shoemaker teaches to receive user-satisfaction data indicating a level of satisfaction experienced by the user (See Shoemaker at least at Abstract; Col. 3, ln. 56 – Col. 6, ln. 8 (“In the example wherein the learning-based system is employed to provide recommendations for potential dates, an input sample employed to train the learning algorithm may include the user profile data, the profile data of a person that has gone out on a date with that user, and the feedback data from the user and/or the date pertaining to the satisfaction level with the dating experience.”); Col. 13, ln. 29 – Col. 14, ln. 32 (“[T]he invention may apply broadly to any situation wherein computer-generated recommendations to a human are desired to improve the likelihood of a satisfactory outcome. For example, the learning-based recommendation system incorporating collaborative filtering and feedback may be employed to make recommendations regarding the purchase of goods and/or services. In this case, the match is between attributes of a person or organization (i.e., the purchaser) and attributes of goods or services, with the satisfaction level reported in the feedback representing the label for classification.”); Figs. 1-4); and re-train the at least one trained machine learning model using the user-satisfaction data, thereby increasing accuracy of the outputs provided by the at least one trained machine learning model (See id. at least at Abstract; Col. 3, ln. 56 – Col. 6, ln. 8 (“In the example wherein the learning-based system is employed to provide recommendations for potential dates, an input sample employed to train the learning algorithm may include the user profile data, the profile data of a person that has gone out on a date with that user, and the feedback data from the user and/or the date pertaining to the satisfaction level with the dating experience.”); Col. 10, ln. 21 – Col. 11, ln. 9 (“As discussed, training set 406 may include input samples pertaining to all users of the dating service who have gone out on dates and who have provided feedback data. These input samples may be obtained from user database 408, for example. Learning algorithm 406, which may be for example SVM, produces a set of hypotheses 410, representing the hypotheses generated by LBDRS 402 regarding feature combinations which are likely to yield compatible matches […] After the interaction, the date seeker and/or his match may provide feedback 422, which indicates whether the match was compatible and may include a ranking of the degree of compatibility and/or satisfaction with the dating experience. The feedback data, along with features of the recommended date that resulted in the interaction, may then be formulated into an input sample 424 for use by learning algorithm 404 to update hypotheses 410 in order to refine future recommendations.”); Col. 13, ln. 29 – Col. 14, ln. 32 (“[T]he invention may apply broadly to any situation wherein computer-generated recommendations to a human are desired to improve the likelihood of a satisfactory outcome. For example, the learning-based recommendation system incorporating collaborative filtering and feedback may be employed to make recommendations regarding the purchase of goods and/or services. In this case, the match is between attributes of a person or organization (i.e., the purchaser) and attributes of goods or services, with the satisfaction level reported in the feedback representing the label for classification.”); Figs. 1-4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Engel, Rivera, Bender, Cossman and Busey to incorporate the teachings of Shoemaker and provide user satisfaction levels and retraining a machine learning model. Shoemaker is directed to learning-based recommendation system incorporating collaborative filtering and feedback. Incorporating the user satisfaction levels and retraining a machine learning model of Shoemaker with the user engagement and techniques of Busey, the consultation and preferences as in Cossman, the machine learning without further user prompts as in Bender with the machine learning models and techniques of Rivera with the tracking and encouraging of user interactions as in Engel would thereby improve the applicability, efficacy, and accuracy of engagement and care support platforms.
Regarding claim 31, Engel as modified by Rivera, Bender, Cossman, Busey and Shoemaker teaches the limitations of claim 28 and Rivera further teaches wherein the at least one processor is further programmed to identify an article of interest to the user as the outputted media content for display on the daily interactive user interface, and wherein the article is displayed along with a visual prompt requesting that the user interact with the article (See Rivera at least at Abstract; Col. 1, ln. 46 – Col. 2, ln. 53 (machine learning tools and user interaction); Co. 3, ln. 15-20 (“Specifically, embodiments disclosed herein describe a machine-learning system for predicting behavior of application users and selecting intervention actions for the application to perform to facilitate user retention.”); Col. 4, ln. 15-20 (“After a user provides input through a first page of the web application 132, successive pages may dynamically generate content displayed to the user based on the input from the first page. The sequence of pages shown to a user may vary depending on the information the user provides.”); Col. 7, ln. 4-51 (“At regular intervals during the interaction session, the retention module 134 may also predict the next action a user will take and customize the page(s) and/or content presented to the user based on the prediction [media content]. The retention module 134 may predict the next action via a fourth machine-learning model (as described in greater detail in FIG. 2) based on the retention-prediction value, the reason (if the retention-prediction value meets the threshold retention-prediction value), and input features based on the composite information set.”); Col. 11, ln. 50 – Col. 12, ln. 33 (An “action for increasing the probability that the user will complete the target action.”); Col. 12, ln. 40-50 (“[A]ltering at least one aspect of the one or more web pages to facilitate user performance of the next action. For example, if the next action is that the user will enter text in a field, the application may dynamically increase the display size of a label of the field and/or the field itself, highlight the field, or display a pop-up balloon with instructions near the field to explain a format in which text should be entered into the field.”); Col. 13, ln. 39-53; Claims 1, 5; Figs. 2-4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Engel, Shoemaker, Bender, Cossman and Busey to incorporate the teachings of Rivera and provide identifying an article of interest for a user. Rivera is directed to machine learning models to facilitate user retention for applications. Incorporating the machine learning models and techniques of Rivera with the user satisfaction levels and retraining a machine learning model of Shoemaker, the user engagement and techniques of Busey, the consultation and preferences as in Cossman, the machine learning without further user prompts as in Bender and the tracking and encouraging of user interactions as in Engel would thereby improve the applicability, efficacy, and accuracy of engagement and care support platforms.
Regarding claims 36, 39, 44 and 46, (claims 36 and 44) and (claims 39 and 46) recite substantially the same limitations as included in claims 28 and 31, respectively. Thus, the claims are rejected under the same grounds of rejection and same reasoning as applied to claims 28 and 31, above.
Claims 29, 37-38 and 45 are rejected under 35 U.S.C. 103 as being unpatentable over Engel, in view of Rivera, in view of Bender, in view of Cossman, in view of Busey, in view of Shoemaker and further in view of U.S. 2016/0342767 A1 to Narasimhan et al., hereinafter “Narasimhan.”
Regarding claim 29, Engel as modified by Rivera, Bender, Cossman, Busey and Shoemaker teaches the limitations of claim 28. The references may not specifically describe but Narasimhan teaches wherein the at least one processor is further programmed to identify a digital photograph associated with the user as the media content for display on the daily interactive user interface, and wherein the digital photograph is displayed along with a visual prompt requesting that the user acknowledges the digital photograph (See Narasimhan at least at Paras. [0009] (identify image associated with user medication), [0012] (acknowledgement), [0026], [0062]-[0064], [0080] (acknowledgement from patient and prompt providing further instructions), [0083], [0087], [0145]; Claims 4-6; Figs. 3-6, 9).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Engel, Rivera, Bender, Cossman, Busey and Shoemaker to incorporate the teachings of to incorporate the teachings of Narasimhan and provide image and prompting/acknowledgement. Narasimhan is directed to medication adherence and care platform. Incorporating the adherence and care platform of Narasimhan with the machine learning models and techniques of Rivera with the user satisfaction levels and retraining a machine learning model of Shoemaker, the user engagement and techniques of Busey, the consultation and preferences as in Cossman, the machine learning without further user prompts as in Bender and the tracking and encouraging of user interactions as in Engel would thereby improve the applicability, efficacy, and accuracy of engagement and care support platforms.
Regarding claims 37 and 45, claims 37 and 45 recite substantially the same limitations as included in claim 29. Thus, the claims are rejected under the same grounds of rejection and same reasoning as applied to claim 29, above.
Regarding claim 38, Engel as modified by Rivera, Bender, Cossman, Busey, Shoemaker and Narasimhan teaches the limitations of claim 37 and Narasimhan further teaches wherein the digital photograph is received from a second client device associated with a registered caregiver of a circle of caregivers for the user (See id. at least at Paras. [0009]-[0015], [0021]-[0030], [0047], [0071]-[0072], [0084]-[0092]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Engel, Rivera, Bender, Cossman, Busey and Shoemaker to incorporate the teachings of to incorporate the teachings of Narasimhan and provide caregiver sending and receiving media and information. Narasimhan is directed to medication adherence and care platform. Incorporating the adherence and care platform of Narasimhan with the machine learning models and techniques of Rivera with the user satisfaction levels and retraining a machine learning model of Shoemaker, the user engagement and techniques of Busey, the consultation and preferences as in Cossman, the machine learning without further user prompts as in Bender and the tracking and encouraging of user interactions as in Engel would thereby improve the applicability, efficacy, and accuracy of engagement and care support platforms.
Claims 32-33, 40-41 and 47 are rejected under 35 U.S.C. 103 as being unpatentable over Engel, in view of Rivera, in view of Bender, in view of Cossman, in view of Busey, in view of Shoemaker and further in view of U.S. 2013/0147899 A1 to Labhard, hereinafter “Labhard.”
Regarding claim 32, Engel as modified by Rivera, Bender, Cossman, Busey and Shoemaker teaches the limitations of claim 28. The references may not specifically describe but Labhard teaches wherein the at least one processor is further programmed to determine whether a user interaction was received via the daily interactive user interface at the first client device by determining (i) whether any touch interaction was received at the first client device via the daily interactive user interface, and (ii) whether any audible interaction was received via the first client device (See Labhard at least at Abstract; Paras. [0006]-[0007], [0020]-[0022]; Claims 5, 6, 7).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Engel, Rivera, Bender, Cossman, Busey and Shoemaker to incorporate the teachings of to incorporate the teachings of Labhard and provide interactions. Labhard is directed to an Alzheimer’s support system. Incorporating the support system as in Labhard with the machine learning models and techniques of Rivera with the user satisfaction levels and retraining a machine learning model of Shoemaker, the user engagement and techniques of Busey, the consultation and preferences as in Cossman, the machine learning without further user prompts as in Bender and the tracking and encouraging of user interactions as in Engel would thereby improve the applicability, efficacy, and accuracy of engagement and care support platforms.
Regarding claim 33, Engel as modified by Rivera, Bender, Cossman, Busey, Shoemaker and Labhard teaches the limitations of claim 32 and Labhard further teaches wherein the at least one processor is further programmed to: receive an indication of touch interaction from the first client device; generate a daily update message including data representing the indication of the touch interaction; and transmit the daily update message to at least one second client device each being associated with at least one caregiver of a circle of caregivers for the user (See id.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Engel, Rivera, Bender, Cossman, Busey and Shoemaker to incorporate the teachings of to incorporate the teachings of Labhard and provide interactions. Labhard is directed to an Alzheimer’s support system. Incorporating the support system as in Labhard with the machine learning models and techniques of Rivera with the user satisfaction levels and retraining a machine learning model of Shoemaker, the user engagement and techniques of Busey, the consultation and preferences as in Cossman, the machine learning without further user prompts as in Bender and the tracking and encouraging of user interactions as in Engel would thereby improve the applicability, efficacy, and accuracy of engagement and care support platforms.
Regarding claims 40-41 and 47, (claims 40 and 47) and (claim 41) recite substantially the same limitations as included in claims 32 and 33, respectively. Thus, the claims are rejected under the same grounds of rejection and same reasoning as applied to claims 32 and 33, above.
Claims 34 and 42 are rejected under 35 U.S.C. 103 as being unpatentable over Engel, in view of Rivera, in view of Bender, in view of Cossman, in view of Busey, in view of Shoemaker, in view of Narasimhan, in view of U.S. 2022/0031239 A1 to Curtis, hereinafter “Curtis” and further in view of U.S. 2019/0362858 A1 to Valentino et al., hereinafter “Valentino.”
Regarding claim 34, Engel as modified by Rivera, Bender, Cossman, Busey and Shoemaker teaches the limitations of claim 28. The references may not specifically describe but Narasimhan teaches wherein the at least one processor is further programmed to use the machine learning tools to identify a digital photograph associated with the user as the outputted media content for display on the daily interactive user interface, and wherein the digital photograph is displayed (See Narasimhan at least at Paras. [0009] (identify image associated with user medication), [0012] (acknowledgement), [0026], [0062]-[0064], [0080] (acknowledgement from patient and prompt providing further instructions), [0083], [0087], [0145]; Claims 4-6; Figs. 3-6, 9). The references may not specifically describe but Curtis teaches a chatbot, wherein the at least one processor is in further communication with the chatbot (See Curtis at least at Paras. [0011], [0021] (chatbot and audio interactions), [0059] (prompt), [0069]-[0072] (“The tracking module 218 is integrated with one or more messaging platforms and one or more voice platforms of the computing device corresponding to the users to monitor textual interactions and audio interactions of the users.) and an audio prompt generated by the chatbot (See id.). The references may not specifically describe but Valentino teaches the audio prompt requesting that the user verbally acknowledges the digital photograph (See Valentino at least at Paras. [0059]-[0063]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Engel, Rivera, Bender, Cossman, Busey, Shoemaker and Narasimhan to incorporate the teachings of to incorporate the teachings of Curtis and Valentino and provide chatbots and verbal acknowledgement. Curtis relates to analyzing and sharing information over a communication network including AI based agent modules (chatbots). Valentino is directed to monitoring remotely located individuals. Incorporating the chatbots of Curtis with the remote monitoring of Valentino, the adherence and care platform of Narasimhan, the machine learning models and techniques of Rivera with the user satisfaction levels and retraining a machine learning model of Shoemaker, the user engagement and techniques of Busey, the consultation and preferences as in Cossman, the machine learning without further user prompts as in Bender and the tracking and encouraging of user interactions as in Engel would thereby improve the applicability, efficacy, and accuracy of engagement and care support platforms.
Regarding claim 42, claim 42 recites substantially the same limitations as included in claim 34. Thus, claim 42 is rejected under the same grounds of rejection and same reasoning as applied to claim 34, above.
Claims 35 and 43 are rejected under 35 U.S.C. 103 as being unpatentable over Engel, in view of Rivera, in view of Bender, in view of Cossman, in view of Busey, in view of Shoemaker, in view of Narasimhan and further in view of Curtis.
Regarding claim 35, Engel as modified by Rivera, Bender, Cossman, Busey and Shoemaker teaches the limitations of claim 28. The references may not specifically describe but Curtis teaches a chatbot, wherein the at least one processor is in further communication with the chatbot, and wherein the at least one processor is further programmed to: cause the chatbot to generate an audio prompt relating to one of a previously scheduled activity (See Curtis at least at Paras. [0011], [0021] (chatbot and audio interactions), [0059] (prompt), [0069]-[0072] (“The tracking module 218 is integrated with one or more messaging platforms and one or more voice platforms of the computing device corresponding to the users to monitor textual interactions and audio interactions of the users.); cause the first client device to audibly present the audio prompt to the user requesting an audio response to the audio prompt (See id.). Rivera further teaches to transmit an indication that the audio response was received to at least one second client device (See Rivera at least at Col. 13, ln. 26-53 (“The application 416 receives the response data from the user (e.g., via I/O device interface 404 or network interface 406) in response to the sending. The application 416 also collects additional data that characterizes user behavior during the interaction session.”); Col. 5, ln. 55-65 (data include “linguistic or paralinguistic features of audio input received through a microphone associated with the computing device 120 (which may indicate whether the user is frustrated).”); Col. 6, ln. 57-65 (“Once the retention module 134 has determined the intervention action, the retention module 134 signals the web application 132 to perform the intervention action. For example, if the intervention action is to connect the user with a live support agent, the web application 132 can open a messaging interface (e.g., a chat window or a two-way audio connection) via the browser”)). While Narasimhan further teaches being associated with at least one caregiver of a circle of caregivers for the user (See Narasimhan at least at Paras. [0009]-[0015], [0021]-[0030], [0047], [0071]-[0072], [0084]-[0092]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Engel, Rivera, Bender, Cossman, Busey and Shoemaker to incorporate the teachings of Narasimhan and Curtis and provide audio prompts and interactions with caregivers. Narasimhan is directed to medication adherence and care platform. Curtis relates to analyzing and sharing information over a communication network including AI based agent modules (chatbots). Incorporating the adherence and care platform of Narasimhan with the chatbots of Curtis, the machine learning retention models and techniques of Rivera, the user satisfaction levels and retraining a machine learning model of Shoemaker, the user engagement and techniques of Busey, the consultation and preferences as in Cossman, the machine learning without further user prompts as in Bender and the tracking and encouraging of user interactions as in Engel would thereby improve the applicability, efficacy, and accuracy of engagement and care support platforms.
Regarding claim 43, claim 43 recites substantially the same limitations as included in claim 35. Thus, claim 43 is rejected under the same grounds of rejection and same reasoning as applied to claim 35, above.
Claim 48 is rejected under 35 U.S.C. 103 as being unpatentable over Engel, in view of Rivera, in view of Bender, in view of Cossman, in view of Busey, in view of Shoemaker, in view of U.S. 2016/0106627 A1 to Geman et al., hereinafter “Geman” and further in view of U.S. 11,436,549 B1 to Hull et al., hereinafter “Hull.”
Regarding claim 48, Engel as modified by Rivera, Bender, Cossman, Busey and Shoemaker teaches the limitations of claim 28 and Rivera further teaches to re-train the at least one machine learning model using the reward signal such that subsequent generated assignment data more accurately predicts the level of satisfaction (See Rivera at least at Abstract; Col. 1, ln. 46 – Col. 2, ln. 53 (machine learning tools and user interaction); Col. 4, ln. 15-20 (“After a user provides input through a first page of the web application 132, successive pages may dynamically generate content displayed to the user based on the input from the first page. The sequence of pages shown to a user may vary depending on the information the user provides.”); Col. 7, ln. 4-51 (“At regular intervals during the interaction session, the retention module 134 may also predict the next action a user will take and customize the page(s) and/or content presented to the user based on the prediction [media content]. The retention module 134 may predict the next action via a fourth machine-learning model (as described in greater detail in FIG. 2) based on the retention-prediction value, the reason (if the retention-prediction value meets the threshold retention-prediction value), and input features based on the composite information set.”); Col. 11, ln. 50 – Col. 12, ln. 33 (An “action for increasing the probability that the user will complete the target action.”); Col. 13, ln. 39-53; Claims 1, 5; Figs. 2-4).
The references may not specifically describe but Geman teaches wherein the at least one processor is further programmed to: generate assignment data based upon task data associated with a plurality of caregivers; (See Geman at least at Abstract; Paras. [0005]-[0013], [0047], [0061]; Claims 16, 19).
While Hull teaches to generate a reward signal by comparing the assignment data to user-satisfaction data indicative of a level of satisfaction experienced by the user and at least one of the plurality of caregivers (See Hull at least at Abstract; Col. 1, ln 55-67; Col. 9, ln. 29 – Col. 10, ln 20; Fig. 1-6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Engel, Rivera, Bender, Cossman, Busey, Shoemaker and incorporate the teachings of Geman and Hull and provide reward system and caregiver satisfaction. Geman is directed to methods for medication adherence. Hull relates to machine learning and caregivers. Incorporating the rewards of Geman with the caregiver satisfaction of Hull,, the machine learning retention models and techniques of Rivera, the user satisfaction levels and retraining a machine learning model of Shoemaker, the user engagement and techniques of Busey, the consultation and preferences as in Cossman, the machine learning without further user prompts as in Bender and the tracking and encouraging of user interactions as in Engel would thereby improve the applicability, efficacy, and accuracy of engagement and care support platforms.
Response to Arguments
Applicant’s remarks filed March 30, 2026 have been fully considered, but they are not persuasive. The following explains why:
Applicant’s arguments pertaining to prior art rejections are not persuasive/moot in light of at least new references Busey and Shoemaker. See rejection above.
Applicant’s arguments pertaining to subject matter eligibility are not persuasive. The basis for the previous rejection under 35 U.S.C. §101 is still operative. The claims have been addressed with regard to the 35+ U.S.C. §101 rejection discussed above, and considered under the relevant sections of the MPEP. The arguments at pages 10-14 of Applicant’s Response are not persuasive. At pages 10-11 the Examiner disagrees that there is not a judicial exception. Here the machine learning is just a computer tool used to employ the abstract idea. Specifically, without the machine learning, the claims appear to be targeted advertising of finding content with which a user is more likely to interact. The “machine learning,” “training and retraining” and “locating and retrieving websites and links” in the claims are recited at a high level, and amount to applying the exception using a generic computer (see e.g. Updated PEG Example 47, claim 2, where the “detecting” and “analyzing” were mental processes, and “using the trained ANN” amounted to generic computer implementation). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
At pages 12-13 it is argued that there is a technological improvement overall that overcomes the abstract idea and there is a practical application. The Examiner disagrees, as stated above as the limitations do not appear to impose meaningful limits on monopolizing an abstract idea of determining if a user interacts with a particular catered prompt. In the pending application, there doesn’t appear to be any demonstrable improvement in technology per se. Furthermore, MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicates that a practical application may be present where the claimed invention provides a technical solution to a technical problem. See, e.g., DDR Holdings, LLC. v. Hotels.com, L.P., 773 F.3d 1245, 1259 (Fed. Cir. 2014) (finding that claiming a website that retained the “look and feel” of a host webpage provided a technological solution to the problem of retention of website visitors by utilizing a website descriptor that emulated the “look and feel” of the host webpage, where the problem arose out of the internet and was thus a technical problem). Here, the Examiner cannot find, nor has the Applicant identified in the claims, any technological problem that was caused by the technological environment to which the claims are confined. There is no clear improvement to the existing computer technology when looking at the claims as a whole.
The Examiner disagrees with the argument at pages 13-14 that there is a practical application that is integrated in the claims. The locating and retrieving of URLs websites and links based on individual preferences is generally linking the abstract idea to a technological environment or field of use, and does not provide a practical application that is integrated in the claims or significantly more than the abstract idea.
At pages 13-14 the Examiner disagrees there is not significantly more than the abstract idea. The additional limitations are only limiting the abstract idea and acting as mere instructions to apply the judicial exception using computer components. The claims do not amount to “significantly more” than the abstract idea since the additional non-abstract limitations amount to no more than mere instructions to apply the judicial exception using a computer component and cannot provide an inventive concept. It appears in the limitations general-purpose technology is merely being leveraged as a tool to link the process to a technological environment to communicate results or filtered data from multiple user data sources (photos, schedule and profile). For example, there is no explanation or any claimed details on how the machine learning tools are “applied” to calculate a daily interactive user interface based on the various images and data that are obtained, or how there will be any greater likelihood of user interaction.
For at least these reasons and those stated in the rejection above, the claims are not patent eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: U.S. 2018/0216946 A1 to Gueye.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM T. MONTICELLO whose telephone number is (313)446-4871. The examiner can normally be reached M-Th; 08:30-18:30 EST.
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/WILLIAM T. MONTICELLO/ Examiner, Art Unit 3682
/FONYA M LONG/ Supervisory Patent Examiner, Art Unit 3682
