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) 1-20 are amended. This action is made Final.
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The Claim Objections towards claim(s) 10 and 11 are withdrawn as claim(s) 10 and 11 have been amended.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 1/20/2026 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.
Applicant Argues: ...The Examiner has oversimplified the claims by evaluating them at such a high level of generality that potentially meaningful technical limitations have been dismissed without adequate explanation, in direct contravention of the guidance provided in these authorities.
Examiner’s Response: The examiner respectfully disagrees. The examiner notes that a proper two-part framework from Alice Corp. and Mayo was conducted for the claims as amended. The examiner has concluded that the claims are still directed towards an abstract idea.
Applicant Argues: At Step 2A, Prong One, the amended independent claims 1, 19, and 20 do not recite any of the three enumerated groupings of abstract ideas identified in the USPTO's guidance: mathematical concepts, certain methods of organizing human activity, or mental processes. [...] These limitations do not recite mathematical formulas, equations, or calculations using words or mathematical symbols. While the claimed machine learning optimization, sentiment score tracking, and task completion forecasting may involve or be based on mathematical concepts, they do not set forth or describe those mathematical concepts in the claims. Consistent with Example 39 and the guidance in the AI SME Update and August 2025 Memo, limitations that merely involve abstract ideas but do not recite them do not fall within the enumerated groupings of abstract ideas.
Examiner’s Response: The respectfully notes that the 35 U.S.C 101 rejection of the claimed invention being directed to abstract idea without significantly more never relied on the enumerated subgrouping of mathematical concepts. Therefore, this argument is moot.
Applicant Argues: At Step 2A, Prong One, the amended independent claims 1, 19, and 20 do not recite any of the three enumerated groupings of abstract ideas identified in the USPTO's guidance: mathematical concepts, certain methods of organizing human activity, or mental processes. [...] These limitations do not recite mathematical formulas, equations, or calculations using words or mathematical symbols. While the claimed machine learning optimization, sentiment score tracking, and task completion forecasting may involve or be based on mathematical concepts, they do not set forth or describe those mathematical concepts in the claims. Consistent with Example 39 and the guidance in the AI SME Update and August 2025 Memo, limitations that merely involve abstract ideas but do not recite them do not fall within the enumerated groupings of abstract ideas.
Examiner’s Response: The respectfully notes that the 35 U.S.C 101 rejection of the claimed invention being directed to abstract idea without significantly more never relied on the enumerated subgrouping of mathematical concepts. Therefore, this argument is moot.
Applicant Argues: The amended claims likewise do not recite the mental process grouping of abstract ideas. The 2024 AI SME Update explains that claims do not recite a mental process when they contain limitations that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. The August 2025 Memo reinforces that the mental process grouping is not without limits and that claim limitations that encompass AI in a way that cannot be practically performed in the human mind do not fall within this grouping. The amended claims require "wherein the update frequency, the set of update questions, and the schedule are optimized based on an application of a machine learning model trained on past update questions and answer and past reminder effectiveness data." Applying a machine learning model that has been trained on past update questions and answers and past reminder effectiveness data to optimize update frequency, update questions, and schedule settings cannot practically be performed in the human mind because the human mind is not equipped to train machine learning models on historical data and apply those models to optimize multiple interdependent parameters. Similarly, "providing a machine-learned forecast of a likelihood of an employee of the set of employees completing a task based past updates and productivity patterns" cannot practically be performed in the human mind because generating machine-learned forecasts based on analysis of past updates and productivity patterns requires computational capabilities beyond what the human mind can perform. The specification describes in detail how machine learning is used for managing status updates, explaining that machine learning can forecast an employee's likelihood of completing goals and tasks by analyzing past updates and productivity patterns, and that a model can be trained to determine optimal timing and frequency for status update reminders based on analysis of past reminder effectiveness data, with the system then automatically sending reminders optimized for each user.
Examiner’s Response: The examiner respectfully disagrees. The “application of a machine learning model trained” is noted to a be recitation of generic computer components that is used to perform the mental process of providing... a plurality of update settings including an update frequency, set of update questions, and a schedule for update reminders... [wherein these are] optimized based on past update questions and answer and past remainder effectiveness data are features that the human mind is can practically perform with the aid of pen and paper. Further, the “machine-learned forecast” is noted to a be recitation of generic computer components that is used to perform the mental process of providing a forecast of a likelihood of an employee of the set of employees completing a task based [sic] past updates and productivity patterns are features that the human mind is can practically perform with the aid of pen and paper. The “machine learning” as claimed are generic computer components recited at a high level of generality. Therefore, the examiner finds this argument not persuasive.
Applicant Argues: The amended claims also do not recite certain methods of organizing human activity. The 2024 AI SME Update explains that not all methods of organizing human activity are abstract ideas, as indicated by the term "certain" that qualifies this grouping, and that except in rare circumstances, this grouping should not be expanded beyond the activity within the enumerated sub-groupings of fundamental economic principles or practices, commercial or legal interactions, and managing personal behavior or relationships or interactions between people. The amended claims are directed to specific computer-implemented techniques for optimizing status update management using machine learning trained on historical effectiveness data, collecting and displaying employee sentiment scores with conditional toggle-based display logic, and generating machine-learned forecasts of task completion likelihood. These claims are directed to technological improvements in computer system functionality for employee performance platforms, not to managing personal behavior or relationships or interactions between people in an abstract sense. The specification describes the difficult technical problem of determining how to interconnect data items related to performance of employees such that an organization can optimally celebrate and reward employees for their efforts, and explains that organizations may be unable to use existing HR systems to effectively link and surface data items pertaining to reviews, feedback, goals, and compensation such that each user is provided with insights specific to the user and suggestions of actions specific to the user for improving with respect to one or more user metrics and helping the organization improve with respect to organizational metrics. The claims at issue provide a technological solution to this technological problem through the specific computer-implemented techniques recited in the amended claims.
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 more specifically the claims recite features that are noted to be forms of managing personal behavior or relationships or interactions between people as the claims recite 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, and wherein the update frequency, the set of update questions, and the schedule are optimized based on past update questions and answer and past reminder effectiveness data; sending reminders to the set of employees based on the plurality of update settings, wherein, the employee sentiment score being on, 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 toggle for the employee sentiment score being on, an average sentiment score for each employee of the set of employees; and providing likelihood of an employee of the set of employees completing a task based past updates and productivity patterns. The features as recited clearly recite forms of managing personal behavior or relationships or interactions between people. The “machine learning” as claimed are generic computer components recited at a high level of generality. Therefore, the examiner finds this argument not persuasive.
Applicant Argues: At Step 2A, Prong Two, even if the Examiner were to maintain that some limitation recites an abstract idea, the claims as a whole integrate any such exception into a practical application of that exception because, for example, the claimed invention improves the functioning of a computer and improves the technology or technical field of employee performance platforms.
Examiner’s Response: The examiner respectfully disagrees. The claims recite elements 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. Therefore, the examiner finds this argument not persuasive.
Applicant Argues: The specification describes specific technological improves proved by the claimed invention [...] The amended claims reflect these disclosed improvements in the specification. The limitation "wherein the update frequency, the set of update questions, and the schedule are optimized based on an application of a machine learning model trained on past update questions and answer and past reminder effectiveness data" reflects the specific disclosed improvement of using machine learning trained on historical effectiveness data to optimize update settings, rather than requiring manual configuration or using generic default settings. This provides a particular way to achieve the desired outcome of optimizing status update effectiveness, not merely the idea of optimization. The limitation "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 on, each of the set of employees is asked to provide a sentiment rating" reflects the disclosed improvement of conditionally collecting sentiment data based on toggle configuration, allowing administrators to selectively enable sentiment tracking. The limitation "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 on, an average sentiment score for each employee of the set of employees" reflects the disclosed improvement of providing a specialized interface that conditionally displays average sentiment scores based on the toggle setting, giving administrators granular control over what data is tracked and displayed for each employee. The limitation "providing a machine-learned forecast of a likelihood of an employee of the set of employees completing a task based past updates and productivity patterns" reflects the disclosed improvement of using machine learning to generate predictive forecasts that allow managers to proactively identify issues, transforming the system from merely tracking historical data to providing predictive insights.
Examiner’s Response: The examiner respectfully disagrees. The examiner notes the use of a “machine learning model trained” to “optimize” the update frequency, the set of update questions, and the schedule ... based ... on past update questions and answer and past reminder effectiveness data is noted an element that is 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. Further the use of toggles/windows (i.e., interactive interface) to aid in sending reminders or enabling status updates are noted to be elements that 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. Further, use of “machine-learned forecast” to provide forecast of a likelihood of an employee of the set of employees completing a task based past updates and productivity patterns is noted to be an element that is 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. Therefore, the examiner finds this argument not persuasive.
Applicant Argues: The August 2025 Memo instructs examiners to consider whether the claim recites only the idea of a solution or outcome versus whether the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, and whether the claim invokes computers or other machinery merely as a tool to perform an existing process versus whether the claim purports to improve computer capabilities or to improve an existing technology. The amended claims recite particular solutions: specific use of a machine learning model trained on past update questions and answers and past reminder effectiveness data to optimize specific update settings, conditional collection and display of sentiment scores based on toggle configuration, and machine-learned forecasting based on past updates and productivity patterns. These are not generic invocations of machine learning as a tool but rather specific applications of machine learning to solve the identified technical problems in employee performance management platforms. The specification explains that specialized interfaces for each administrative service, in conjunction with supporting modules, focus on surfacing the most relevant information and actions for users, improving efficiency and reducing complexity compared to conventional interfaces for such administrative tasks.
Examiner’s Response: The examiner respectfully disagrees. 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 and use of application of machine learning/machine learning forecast. 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.
Applicant Argues: The December 2025 Memo explains that in Ex Parte Desjardins, enumerated improvements identified in the specification included disclosures of effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks, allowing the system to reduce use of storage capacity, and enablement of reduced complexity in the system, and that such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation. Similarly here, the improvements to the employee performance platform system relate to how the system itself functions: using machine learning to automatically optimize update settings based on historical effectiveness rather than requiring manual configuration, conditionally tracking and displaying sentiment data based on administrator-configured toggles, and generating predictive forecasts to enable proactive management intervention. These improvements are to the computer system's functionality in managing employee performance data, not merely improvements to any underlying mathematical calculations that may be involved in the machine learning operations.
Examiner’s Response: The examiner respectfully disagrees. The “machine learning” as claimed for optimizing update settings based on historical effectiveness and predicting forecasts to enable proactive management intervention are found to be 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. The examiner respectfully notes the machine learning is claimed at a high level (i.e., black-box – input/output). Therefore, the examiner finds this argument not persuasive.
Applicant Argues: The Examiner's conclusory assertion that the additional elements are described at a high level without meaningful detail about their structure or configuration and amount to no more than mere instructions to apply the exception using generic computer components is contradicted by the specification. The specification provides extensive detail regarding the machine learning techniques used, including that transformer neural network models can be trained to generate suggested update questions, that training data consists of past update questions and answers, and that models can be trained to determine optimal timing and frequency for status update reminders based on analysis of past reminder effectiveness data. The specification further describes that the automation module is configured to automate various tasks and that machine-learning models may be trained to output values that optimize the performance of the organization with respect to one or more objectives, with inputs to the machine-learning model including relevant data items that are then applied to generate values based on novel input data. This level of detail demonstrates that the claimed machine learning optimization, sentiment tracking, and forecasting are not generic computer functions but rather specific technological implementations that solve specific technical problems.
Each of independent claims 1, 19, and 20 as a whole recites a specific configuration in which "the plurality of update settings includes a toggle for an employee sentiment score," "based on the toggle for the employee sentiment score being on, each of the set of employees is asked to provide a sentiment rating," and "based on the toggle for the employee sentiment score being on, an average sentiment score for each employee of the set of employees" is displayed in the log window. This conditional logic for selectively enabling sentiment collection and conditionally displaying average sentiment scores based on toggle configuration represents a specific way to achieve the desired outcome of providing administrators with granular control over sentiment tracking, not merely the idea of collecting sentiment data. Similarly, the combination of machine learning optimization of "the update frequency, the set of update questions, and the schedule" based on training on "past update questions and answer and past reminder effectiveness data," together with "providing a machine-learned forecast of a likelihood of an employee of the set of employees completing a task based past updates and productivity patterns," represents a specific application of machine learning to the technical field of employee performance management platforms that provides particular solutions to the technical problems described in the specification.
The Examiner's Step 2A, Prong Two analysis fails to consider the claims as a whole and instead improperly evaluates individual elements in isolation. The August 2025 Memo instructs that the analysis in Step 2A Prong Two considers the claim as a whole, and that the additional limitations should not be evaluated in a vacuum completely separate from the recited judicial exception, but rather the analysis should take into consideration all the claim limitations and how these limitations interact and impact each other when evaluating whether the exception is integrated into a practical application. Example 40 in the published USPTO Examples illustrates this principle, where even though individual collecting steps analyzed separately may be viewed as mere pre- or post-solution activity, the claim as a whole was directed to a particular improvement in collecting traffic data that provided a specific improvement over prior systems resulting in improved network monitoring, and thus the claim as a whole integrated the mental process into a practical application. Similarly here, even if individual limitations such as providing windows or receiving selections might be characterized as routine in isolation, the claims as a whole recite a specific combination in which machine learning optimization of update settings based on historical effectiveness data is combined with conditional sentiment score collection and display based on toggle configuration, and machine- learned forecasting of task completion likelihood based on past updates and productivity patterns, all integrated into specialized graphical user interfaces for employee performance management. This combination as a whole provides the disclosed technological improvements and constitutes a particular way to achieve the desired outcome of improving employee performance management systems.
Examiner’s Response: The examiner respectfully disagrees. The claim was analyzed in whole. The abstract idea was identified in Step 2A-Prong 2. Regarding Step 2B, 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 and use of application of machine learning/machine learning forecast. 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.
Applicant Argues: At Step 2B, the amended claims recite additional elements that amount to significantly more than any alleged judicial exception. The December 2025 Memo explains that improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams is an example that may show an improvement in computer functionality, and that claims reflecting a specific improvement that addressed a technical problem in continual learning systems while allowing artificial intelligence systems to variously optimize system performance, use less storage capacity, and reduce system complexity constituted significantly more than the alleged abstract idea. The amended claims recite "wherein the update frequency, the set of update questions, and the schedule are optimized based on an application of a machine learning model trained on past update questions and answer and past reminder effectiveness data," which represents adjustments to parameters of status update management based on machine learning model outputs that optimize system performance. This is directly analogous to the improvements recognized in Ex Parte Desjardins. The specification explains that the automation module is configured to provide various suggestions and that machine- learning models may be trained to output values that optimize the performance of the organization with respect to one or more objectives, and specifically describes how a model can be trained to determine optimal timing and frequency for status update reminders based on analysis of past reminder effectiveness data, with the system then automatically sending reminders optimized for each user.
The additional element of "providing a machine-learned forecast of a likelihood of an employee of the set of employees completing a task based past updates and productivity patterns" similarly amounts to significantly more than any alleged abstract idea because it transforms the claimed system from a retrospective tracking system into a predictive system that enables proactive intervention. The specification explains that machine learning can forecast an employee's likelihood of completing goals and tasks by analyzing past updates and productivity patterns, and that managers can use this to proactively identify issues. This predictive functionality represents a significant technological advancement over conventional status update systems that merely collect and display historical information. The combination of conditional sentiment score collection based on "wherein, based on the toggle for the employee sentiment score being on, each of the set of employees is asked to provide a sentiment rating," conditional display logic in the log window where "based on the toggle for the employee sentiment score being on, an average sentiment score for each employee of the set of employees" is displayed, machine learning optimization of settings based on historical effectiveness data, and machine-learned forecasting of task completion likelihood, represents a specific ordered combination of elements that provides significantly more than any alleged abstract idea by integrating multiple AI-based capabilities into a cohesive employee performance management platform.
The Examiner's conclusory statement that the additional elements are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components fails to account for the specific requirements of the amended claims. The claims require not merely generic application of machine learning, but rather specific application of "a machine learning model trained on past update questions and answer and past reminder effectiveness data" to optimize specific parameters including "the update frequency, the set of update questions, and the schedule." The claims require specific conditional logic where "based on the toggle for the employee sentiment score being on," employees are asked to provide sentiment ratings and average sentiment scores are displayed in the log window. The claims require "providing a machine-learned forecast of a likelihood of an employee of the set of employees completing a task based past updates and productivity patterns." These are not generic computer functions but rather specific technological implementations described in the specification that provide the disclosed improvements to employee performance management platforms.
Examiner’s Response: 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 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 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. Therefore, the examiner finds this argument not persuasive.
Applicant Argues: Applicant respectfully submits that the amended independent claims 1, 19, and 20 do not recite an abstract idea at Step 2A, Prong One. Alternatively, even if the Examiner were to maintain that some limitation recites an abstract idea, the claims as a whole integrate any such exception into a practical application at Step 2A, Prong Two because they recite technological solutions to technological problems described in the specification, and the claims recite additional elements that amount to significantly more than any alleged abstract idea at Step 2B. The dependent claims are likewise eligible because they depend from eligible independent claims and add further specific technical features. Applicant respectfully requests that the Examiner withdraw the rejection of claims 1-20 under 35 U.S.C. § 101 and allow these claims as eligible subject matter.
Examiner’s Response: The examiner disagrees for the reasons set forth above.
Applicant’s arguments filed 1/20/2026 with respect to the 35 U.S.C. 103rejection have been fully considered and are persuasive. The 35 U.S.C. 103 rejection of claim(s) 1-20 has been withdrawn.
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 comprising an update frequency, a set of update questions, and a schedule for update reminders, wherein the plurality of update settings includes
sending reminders to the set of employees based on the plurality of update settings, wherein,
providing a log
providing
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, and wherein the update frequency, the set of update questions, and the schedule are optimized based on past update questions and answer and past reminder effectiveness data; sending reminders to the set of employees based on the plurality of update settings, wherein, the employee sentiment score being on, 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 toggle for the employee sentiment score being on, an average sentiment score for each employee of the set of employees; and providing likelihood of an employee of the set of employees completing a task based past updates and productivity patterns, thus, the claim 1, and similar claim(s) 19 and 20 falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they recite managing personal behavior or relationships or interactions between people.
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 and use of application of machine learning/machine learning forecast; 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, and wherein the update frequency, the set of update questions, and the schedule are optimized based on past update questions and answer and past reminder effectiveness data; sending reminders to the set of employees based on the plurality of update settings, wherein, the employee sentiment score being on, 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 toggle for the employee sentiment score being on, an average sentiment score for each employee of the set of employees; and providing likelihood of an employee of the set of employees completing a task based past updates and productivity patterns, thus, encompasses steps that a user can manually 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 and use of application of machine learning/machine learning forecast. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration (see Applicant’s Specification, ⁋[0283]). 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 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 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 to predicted); 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.
Regarding Claim 1, and 19, the prior art of record as cited within this Office Action, nor those cited, in the additional references cited , alone or in combination, neither anticipates, reasonably teaches, nor renders obvious “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, and wherein the update frequency, the set of update questions, and the schedule are optimized based on an application of a machine learning model trained on past update questions and answer and past reminder effectiveness data; 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 on, 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 on, an average sentiment score for each employee of the set of employees; and providing a machine-learned forecast of a 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
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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).
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/ASFAND M SHEIKH/Primary Examiner, Art Unit 3626
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