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 .
Claims 1-20 are pending for examination. Claim(s) 1, 16, 18, and 20 are amended. This action is made Non-Final.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 7/22/2026 has been entered.
Response to Arguments
The Claim Objections towards claim(s) 10 and 11 are withdrawn as claim(s) 10 and 11 have been amended.
Applicant's arguments filed 7/22/2026 with respect to the 35 U.S.C. 101 rejection have been fully considered but they are not persuasive.
Applicant Argues: Claims 1-20 were rejected under 35 U.S.C.§ 101 as allegedly being directed to non- statutory subject matter Applicant respectfully submits that the Examiner's analysis is based on the unamended claims and does not account for the amendments that have been made to independent claims 1, 19, and 20. The amended claims do not recite an abstract idea at Step 2A, Prong One, and alternatively, even if any limitations could be characterized as reciting an abstract idea, the claims as a whole integrate any such recited exception into a practical application at Step 2A, Prong Two, and recite significantly more than any alleged abstract idea at Step 2B.
Examiner’s Response: The examiner respectfully disagrees for the reasons set forth below.
I. Step 2A, Prong One: The Independent Claims Do Not Recite an Abstract Idea.
Applicant Argues: Under Step 2A, Prong One, the amended independent claims do not recite a judicial exception. The Examiner identified the "Certain Methods of Organizing Human Activity" and "Mental Processes" groupings, but the amended claims are not properly characterized as either.
Examiner’s Response: The examiner respectfully disagrees. The examiner notes that a proper two-part framework from Alice Corp. and Mayo was conducted and the examiner has concluded that the claims are still directed towards an abstract idea.
Applicant Argues: First, the claims do not recite a mental process. The Final Office Action states that the claims encompass steps "that a user can manually perform in the human mind or by a human using pen and paper." But the amended claims now require computer-implemented operations that cannot practically be performed mentally, including encoding past update questions and answers into vector representations using embedding techniques and applying a trained machine-learning model to those vector representations to generate suggested update questions. Those limitations are not observations, evaluations, judgments, or opinions capable of practical mental performance.
Examiner’s Response: The examiner respectfully disagrees. As amended the examiner has concluded that the use of a trained machine learning model to encode vector representations using embedding techniques is noted to be a to a be recitation of generic computer components that is used to perform the mental process of “encoding past update questions and answers into representations using techniques and applying a model to those representations to generate suggested update questions.” The examiner respectfully notes the aforementioned feature can be practically perform by the human mind with the aid of pen and paper; therefore, is a feature is noted to be observations, evaluations, judgments, or opinions that is capable of practical mental performance. Therefore, the examiner finds this argument not persuasive.
Applicant Argues: Second, the claims do not recite a "certain method of organizing human activity." The claims do not merely manage employee interactions or reminders. They recite a particular computer- implemented architecture for configuring status-update questions and reminder schedules using trained models, interlinking update data with user profiles and goals stored in databases, and providing a forecast using the one or more machine-learning models. The rejection characterizes the claims at a high level as providing status updates, sending reminders, collecting sentiment ratings, and providing likelihood information, but that characterization omits the newly added implementation details.
Examiner’s Response: The examiner respectfully disagrees. The examiner respectfully notes that that the claims are directed towards “Certain Methods of Organizing Human Activity” grouping of abstract ideas. As argued the features of “configuring status-update questions and reminder schedules..., interlinking update data with user profiles and goals stored..., and providing a forecast...” are noted to be forms of managing personal behavior or relationships or interactions between. The “trained machine-learning models”, database, and use of “the one or more machine-learning models” as claimed are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements, even in combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the examiner finds this argument not persuasive.
Applicant Argues: Third, the Examiner expressly did not rely on the mathematical-concepts grouping. Accordingly, arguments directed to whether the claims recite mathematical formulas or calculations are unnecessary to resolve the pending rejection.
The present amendments also remove the alleged black-box character identified by the Examiner because the claims no longer merely recite applying machine learning to optimize settings; they recite the particular preprocessing and model-application steps used to generate suggested update questions and reminder timing.
For these reasons, independent claims 1, 19, and 20 do not recite a judicial exception under Step 2A, Prong One. The dependent claims are eligible at least by virtue of their dependency and their additional limitations.
Examiner’s Response: The examiner respectfully disagrees. The examiner notes the argued “pre-processing and model-application steps” involving machine learning (i.e., wherein one or morequestions for inclusion in the set of update questions; and applying a are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements, even in combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the examiner finds this argument not persuasive.
II. Step 2A, Prong Two: The Claims Integrate Any Alleged Abstract Idea Into a Practical Application
Applicant Argues: Even if any limitation were deemed to recite an abstract idea, the independent claims as a whole integrate any such exception into a practical application. The claim limitations do not operate independently or merely append generic computer implementation to an abstract result. Rather, they interact as a specific computer-implemented sequence: the status update configuration window receives update settings; one or more machine-learning models configure the update questions and reminder schedule using the recited vector-representation and training-data operations; update data is interlinked with database-stored user profiles and goals; reminders and sentiment-rating prompts are sent based on the configured settings; a log window displays employee-specific enablement and sentiment information; and the system provides a model-based task-completion forecast.
This ordered combination addresses the technical problem described in the specification: existing systems may have difficulty linking and surfacing employee-performance data across HR tools so that users receive user-specific insights and suggested actions. The specification states that organizations may be unable to use HR systems "to link and/or surface data items pertaining to reviews, one-on-ones feedback, praise, goals, and compensation" such that each user receives "insights specific to the user and/or suggestions of one or more actions specific to the user." The specification also identifies the technical problem of interconnecting employee-performance data items. The amended claims recite the particular computer-implemented mechanism for that solution, rather than merely claiming the goal of better status updates or better reminders. The claims address that problem at the data-processing level by transforming historical update question-and-answer data into vector representations, applying trained models to those representations, and interlinking update data with database-stored profile and goal data for downstream configuration and forecasting.
The Final Office Action characterizes the additional elements as "processor, memory/(medium), and instructions involving and use of windows/toggles on a graphical user interface and use of application of machine learning/machine learning forecast." That characterization no longer addresses the amended claims. The claims now recite particular data preprocessing, trained-model application, database interlinking, and model-based forecasting. Accordingly, even if an abstract idea were identified, the claims as a whole integrate it into a practical application.
Examiner’s Response: The examiner respectfully disagrees. The argued features and sequence i.e., “the status update configuration ... receives update settings; one or more ... models configure the update questions and reminder schedule using the recited ...representation and ... operations; update data is interlinked with ...stored user profiles and goals; reminders and sentiment-rating prompts are sent based on the configured settings; a log ... displays employee-specific enablement and sentiment information; and ... provides a model-based task-completion forecast” are noted to steps that are part of the abstract idea (i.e., Certain Methods of Organizing Human Activity and/or Mental Processes). Further, the argued features of “data preprocessing, ...model application, ... interlinking, and model-based forecasting...” are to be features that are also part of the abstract idea (i.e., Certain Methods of Organizing Human Activity and/or Mental Processes). The examiner respectfully notes limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)), The claims recite i.e., processor, memory/(medium), and instructions involving and use of windows/toggles on a graphical user interface, use of machine learning including a trained machine learning model to encode vector representations using embedding techniques and a database. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. These elements in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements, even in combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Therefore, the examiner finds this argument not persuasive.
II. Step 2B: The Claims Recite More Than Any Alleged Abstract Idea
Applicant Argues: Even if the claims were deemed to recite an abstract idea and not integrate that idea into a practical application, the claims recite significantly more than any alleged abstract idea.
The amended independent claims recite a specific ordered combination: vector-encoding past update questions and answers; applying a trained model to those vector representations to generate suggested update questions; applying a model trained on past reminder effectiveness data to determine optimal reminder timing; interlinking update data with database-stored user profiles and goals; conditionally prompting for sentiment ratings and displaying average sentiment scores; and providing a task-completion forecast using the one or more machine-learning models. These limitations are not merely a generic instruction to "apply" an abstract idea on a computer.
The rejection does not provide factual support that this ordered combination was well- understood, routine, and conventional. The prosecution record also does not support such a conclusion. The Final Office Action withdrew the § 103 rejection of claims 1-20 and stated that the prior art of record did not anticipate, reasonably teach, or render obvious the quoted independent claim features then of record. Although withdrawal of a prior-art rejection is not dispositive of eligibility, it confirms that the Office has not identified evidence that the ordered combination is conventional in the prior art. At Step 2B, any assertion that the combination is well-understood, routine, and conventional still requires factual support.
Accordingly, the independent claims recite significantly more than any alleged abstract idea. The dependent claims add further technical detail. For example, claim 16 specifies the reminder- effectiveness model's operation by analyzing update completion times, update frequencies, reminder times, and user time zone information; determining patterns and correlations across user segments and time zones; and generating predicted optimal reminder delivery times. Claim 17 further recites displaying the predicted optimal reminder delivery time for user selection. These limitations further confirm that the claims do not merely recite generic status-update management.
Therefore, Applicant respectfully requests withdrawal of the rejection of claims 1-20 under 35 U.S.C. § 101.
Examiner’s Response: The examiner respectfully disagrees. The examiner notes that features of “: and providing a task-completion forecast using are noted to steps that are part of the abstract idea (i.e., Certain Methods of Organizing Human Activity and/or Mental Processes). As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of i.e., processor, memory/(medium), and instructions involving and use of windows/toggles on a graphical user interface and use of application of machine learning/machine learning forecast; amounts to no more than mere instructions to apply the exception using a generic computer component and do not add anything that is not already present when they are considered individually or in combination. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, under Step 2B, there are no meaningful limitations in claim(s) that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
Further, the rejection does not cite any elements as well-understood, routine, and conventional therefore, there is no evidentiary requirement (i.e., MPEP 2106.07(a)(III)). Further, a claim may still be abstract even though there is no outstanding prior art rejection under 35 U.S.C. 102 or 103.
The examiner notes that features of (i.e., Claim 16 - analyze status update data comprising update completion times, update frequencies, reminder times, and user time zone information; determine patterns and correlations between reminder timing and update completion rates across different user segments and time zones based on the analysis; and generate a predicted optimal reminder delivery time for each user that maximizes update completion rates based on the determined patterns and correlations and Claim 17 - displays the predicted optimal reminder delivery time) are noted to steps that are part of the abstract idea (i.e., Certain Methods of Organizing Human Activity and/or Mental Processes) similar to that of claim 1.. Claims 16 and 17, these contain similar additional elements to that of claim 1, i.e., and instructions involving and use of windows/toggles on a graphical user interface, and use of machine learning including a trained machine learning model to encode vector representations using embedding techniques. These elements amount to no more than mere instructions to apply the exception using a generic computer component and do not add anything that is not already present when they are considered individually or in combination and do not add anything that is not already present when they are considered individually or in combination. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, under Step 2B, there are no meaningful limitations in claim(s) that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. Therefore, the examiner finds this argument not persuasive.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1: claim(s) 1-20 are directed to a machine, process, and/or manufacture. Therefore, the claims are directed to statutory subject matter under Step 1 (Step 1: YES). See MPEP 2106.03.
Prong 1, Step 2A: claim 1, and similar claim(s) 19 and 20, taken as representative, recites at least the following limitations that recite an abstract idea:
providing a status update configuration update questions and the schedule for update reminders, wherein applying
interlinking update data associated with status updates received from the set of employees with user profiles and goals stored
sending reminders to the set of employees based on the plurality of update settings, wherein,
providing a log
providing using the one or more
The above limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(II), in that they recite managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). The broadest reasonable interpretation of these limitations includes for claim 1, and for similar claim(s) 19 and 20 includes providing a status update configuration, the status update configuration comprising a plurality of update settings for configuring status updates for a set of employees, plurality of update settings comprising an update frequency, a set of update questions, and a schedule for update reminders, wherein the plurality of update settings includes an employee sentiment score that is settable to an on state or an off state, and wherein models are applied to configure the set of update questions and the schedule for update reminders, wherein applying models comprises: encoding past update questions and answers into representations using techniques; applying, to the representations, a model trained on the past update questions and answers to generate one or more suggested update questions for inclusion in the set of update questions; and applying a model trained on past reminder effectiveness data to determine an optimal timing for the schedule for update reminders; interlinking update data associated with status updates received from the set of employees with user profiles and goals; sending reminders to the set of employees based on the plurality of update settings, wherein, the employee sentiment score being in the on state, each of the set of employees is asked to provide a sentiment rating; providing a log, for each employee of the set of employees, for enabling the status updates and, based on \ for the employee sentiment score being in the on state, an average sentiment score for each employee of the set of employees; and providing using the one or more
The above limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(III), in that they recite as concepts performed in the human mind, including observations, evaluations, judgments, and opinions. That is, other than reciting for claim 1, and similar claim(s) 19 and 20, i.e., processor, memory/(medium), and instructions involving and use of windows/toggles on a graphical user interface, use of machine learning including a trained machine learning model to encode vector representations using embedding techniques and a database; nothing in these claim element(s) precludes the step(s) from practically being performed in the mind. For example, the broadest reasonable interpretation of these limitations for claim 1, and similar claim(s) 19 and 20, includes providing a status update configuration, the status update configuration comprising a plurality of update settings for configuring status updates for a set of employees, plurality of update settings comprising an update frequency, a set of update questions, and a schedule for update reminders, wherein the plurality of update settings includes an employee sentiment score that is settable to an on state or an off state, and wherein models are applied to configure the set of update questions and the schedule for update reminders, wherein applying models comprises: encoding past update questions and answers into representations using techniques; applying, to the representations, a model trained on the past update questions and answers to generate one or more suggested update questions for inclusion in the set of update questions; and applying a model trained on past reminder effectiveness data to determine an optimal timing for the schedule for update reminders; interlinking update data associated with status updates received from the set of employees with user profiles and goals; sending reminders to the set of employees based on the plurality of update settings, wherein, the employee sentiment score being in the on state, each of the set of employees is asked to provide a sentiment rating; providing a log, for each employee of the set of employees, for enabling the status updates and, based on the employee sentiment score being in the on state, an average sentiment score for each employee of the set of employees; and providing using the one or more perform in the human mind or by a human using pen and paper. For example, a human using pen and paper can perform steps with respect to status updates. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes” grouping of abstract ideas.
Accordingly, these claims recite an abstract idea. (Prong 1, Step 2A: YES). The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Prong 2, Step 2A: Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)). Claim 1, and for similar claim(s) 19 and 20, recite i.e., processor, memory/(medium), and instructions involving and use of windows/toggles on a graphical user interface, use of machine learning including a trained machine learning model to encode vector representations using embedding techniques and a database. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. These elements in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements, even in combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
As such, under Prong 2 of Step 2A, when considered both individually and as a whole, the limitations of claim 1, and for similar claim(s) 19 and 20 are not indicative of integration into a practical application (Prong 2, Step 2A: NO). See MPEP 2106.04(d).
Since claim 1, and similar claim(s) 19 and 20 recites an abstract idea and fails to integrate the abstract idea into a practical application, claim 1, and similar claim(s) 19 and 20 is “directed to” an abstract idea under Step 2A (Step 2A: YES). See MPEP 2106.04(d).
Step 2B: The recitation of the additional elements is acknowledged, as identified above with respect to Prong 2 of Step 2A. These additional elements do not add significantly more to the abstract idea for the same reasons as addressed above with respect to Prong 2 of Step 2A.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of for claim 1, and similar claim(s) 19 and 20, i.e., processor, memory/(medium), and instructions involving and use of windows/toggles on a graphical user interface, use of machine learning including a trained machine learning model to encode vector representations using embedding techniques and a database; amounts to no more than mere instructions to apply the exception using a generic computer component and do not add anything that is not already present when they are considered individually or in combination. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, under Step 2B, there are no meaningful limitations in claim 1, and similar claim(s) 19 and 20 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO). See MPEP 2106.05.
Accordingly, under the Subject Matter Eligibility test, claim 1, and similar claim(s) 19 and 20 is ineligible.
Regarding Claims 2-18; these claims further define the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above w/ respect to “Certain Methods of Organizing Human Activity” as the claims recite further concepts of managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions) i.e., further features related to providing a status update and/or further recite “Mental Processes” as the claims recite further concepts that can be performed in the human mind, including observations, evaluations, judgments, and opinions. These dependent claim does not include any additional elements that integrate the abstract idea into a practical application (i.e., claims 2-5, 8-15, 17-18 – discuss further implementations windows and claims 16 – use of a trained machine learning model trained on past data to predict); as such elements are recited at a high level of generality such that it amounts not more than mere instructions to apply the exception using a generic computer component. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do no not amount to significantly more than the abstract idea itself. Thus, the aforementioned claims are not patent-eligible.
Reasons For No Prior Art Rejection
Upon review of the evidence at hand, it is hereby concluded that the evidence obtained and made of record, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of applicant’s invention as the noted features amount to more than a predictable use of elements in the prior art.
The closest prior art of record noted below:
Bank et al. (US 2008/0294999 A1) discusses Meeting originators grant permission to update (i.e., add, change, and/or delete) a field or fields of a meeting invitation that corresponds to a calendar entry on an electronic calendar, enabling a meeting invitee to update a meeting invitation and to thereby communicate updates that can be reflected in the corresponding electronic calendar entries of other people who are invited to the meeting. Update permission may be granted to one meeting invitee, to all meeting invitees, or to a selected subset of the meeting invitees. Update permissions are associated with the particular meeting invitation, and preferably expire once the meeting time and date have passed. For recurring meetings, a particular update permission may be granted for a single instance of the meeting, or to all instances, and this permission preferably expires after the last instance of the recurring meeting has ended. (Abstract).
Lee et al. (US 8,423,577 B1) discusses A system, method, and computer-readable media are described for suggesting an action based on multiple descriptors within a textual communication (e.g. email, text message). In one embodiment, event descriptors within an email are identified and displayed to the email recipient with an indication that the descriptors are selectable. Upon receiving the selection of at least two descriptors, an action is suggested to the recipient for acceptance. Upon receiving the acceptance, the proposed action is performed. (Abstract).
York (US 2008/0301296 A1) discusses A system and method for creating, negotiating, tracking, and analyzing tasks, wherein the present invention provides for automated negotiation of tasks between task assignor and task assignee, and wherein the present invention provides for automated tracking and trending of task completion, performing statistical analysis of the task status and task completion, tracking and trending of tasks assigned to an individuals or group of individuals, sets of individuals belonging to a department or organization, tracking and trending groups of tasks making up a project, and tracking and trending of tasks across an entire organization. (Abstract).
However, regarding Claim 1, and 19, the prior art of record, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the claim features of:
“providing a status update configuration window on a graphical user interface, the status update configuration window comprising a plurality of update settings for configuring status updates for a set of employees, plurality of update settings comprising an update frequency, a set of update questions, and a schedule for update reminders, wherein the plurality of update settings includes a toggle for an employee sentiment score that is settable to an on state or an off state, and wherein one or more machine-learning models are applied to configure the set of update questions and the schedule for update reminders, wherein applying the one or more machine-learning models comprises: encoding past update questions and answers into vector representations using embedding techniques; applying, to the vector representations, a machine-learning model trained on the past update questions and answers to generate one or more suggested update questions for inclusion in the set of update questions; and applying a machine-learning model trained on past reminder effectiveness data to determine an optimal timing for the schedule for update reminders;
interlinking update data associated with status updates received from the set of employees with user profiles and goals stored in one or more databases.
sending reminders to the set of employees based on the plurality of update settings, wherein, based on the toggle for the employee sentiment score being in the on state, each of the set of employees is asked to provide a sentiment rating;
providing a log window on the graphical user interface, the log window including, for each employee of the set of employees, a toggle for enabling the status updates and, based on the toggle for the employee sentiment score being in the on state, an average sentiment score for each employee of the set of employees; and
providing using the one or more machine-learning models likelihood of an employee of the set of employees completing a task based past updates and productivity patterns.”
Claim(s) 2-18 and 20 inherit the features as found in independent claim(s) 1-9.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
HERNANDEZ RIVERA et al. (US 2023/0334514 A1) discusses aspects of the present disclosure relate to generating an engagement model to predict actions that may have a high probability of maintaining user engagement in-application or causing a user to reengage with the application. To generate the engagement model, an approach has been developed which incorporates features analysis of the application and application users. Users may be grouped based on similar features that are used to generate machine learning engagement models. The output of an engagement model may be a prediction on whether a user will continue to engage with an application. The prediction may be provided to a reengagement model which may output prompts to help increase user engagement with the application. The prompts may be based on an understanding of application users and their preferences (Abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASFAND M SHEIKH whose telephone number is (571)272-1466. The examiner can normally be reached Mon-Fri: 7a-3p (MDT).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JESSICA LEMIEUX can be reached at (571)270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ASFAND M SHEIKH/Primary Examiner, Art Unit 3626
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