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 1-12 and 14-20 are pending for examination. Claims 1, 11, 19, and 20 are amended. Claim 21 is cancelled. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 7/6/2026 has been entered.
Response to Arguments
Applicant's arguments filed 7/6/2026 with respect to 35 U.S.C. 101 rejection have been fully considered but they are not persuasive.
Applicant Argues: Step 2A, Prong One The Examiner's conclusion that the amended claims recite abstract ideas is fundamentally flawed, contradicts established USPTO guidance regarding the proper scope of the abstract idea groupings, and must be revisited in light of the present amendments. The amended independent claims now specifically recite "analyzing, using machine learning techniques, the received feedback responses linked to the user profile to determine a last feedback submission date and a praise ratio for the feedback requestor, wherein the praise ratio is determined by identifying sentiment of the received feedback responses as positive, negative, or neutral using the machine learning techniques, wherein the praise ratio indicates a proportion of positive feedback." This amendment directly and expressly incorporates into the claims the machine learning and sentiment analysis functionality that the Examiner previously identified as absent from the claim language. The Examiner's prior basis for the mental processes finding and the Examiner's repeated reliance on In re Van Geuns, 988 F.2d 1181 (Fed. Cir. 1993), for the proposition that specification features cannot be read into the claims is no longer applicable. Those features are now unambiguously recited in the claims themselves.
Examiner’s Response: The examiner respectfully disagrees and notes that even with the incorporation of the “using machine learning techniques” such a claim element is noted to be a recitation of a generic computer component, and the claimed limitations of “analyzing... the received feedback responses linked to the user profile to determine a last feedback submission data and a praise ratio for the feedback requestor” is a limitation that still falls under Mental Processes grouping of abstract ideas and as it coves performance of the limitation in the mind. Further, such a limitation still falls under Certain Methods of Organizing Human Activity as it recites managing personal behavior or relationships or interactions between people. Therefore, the examiner finds this argument not persuasive.
Applicant Argues: Mental Processes. The Examiner maintained that the claimed steps, including the step of analyzing the received feedback responses to determine a last feedback submission date and a praise ratio, "are steps that can be practically performed in the human mind or by a human using pen and paper." The present amendments render this position untenable. The amended claims now require that the analysis be performed "using machine learning techniques" and that the praise ratio itself be "determined by identifying sentiment of the received feedback responses as positive, negative, or neutral using the machine learning techniques." This cannot practically be performed in the human mind. The human mind is simply not equipped to perform machine learning-based sentiment classification.
The 2024 Al SME Update is unambiguous on this point, stating that "claim limitations that only encompass Al in a way that cannot practically be performed in the human mind do not fall within this grouping." The August 2025 Memo reinforces this instruction, reminding examiners that "[c]laim limitations that encompass Al in a way that cannot practically be performed in the human mind do not fall within this grouping" and that examiners "are reminded not to expand this grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind." The amended claims recite machine learning techniques as the mechanism by which sentiment is identified and the praise ratio is determined. This is not a case of claiming automation of a mental process at a high level of generality it is a claim to a specific, computer-implemented analytical technique that cannot be performed mentally. The amended claims therefore do not fall within the mental processes grouping of abstract ideas.
Examiner’s Response: As noted above, the examiner respectfully disagrees and notes that even with the incorporation of the “using machine learning techniques” such a claim element is noted to be a recitation of a generic computer component, and the claimed limitations of “analyzing the received feedback responses to determine a last feedback submission date and a praise ratio... wherein praise ratio is determined by identifying sentiment of the received feedback responses as positive, negative, or neutral” are limitations that still falls under Mental Processes grouping of abstract ideas and as it coves performance of the limitation in the mind. Therefore, the examiner finds this argument not persuasive.
Applicant Argues: Certain Methods of Organizing Human Activity. The Examiner maintained that the claims fall within the "certain methods of organizing human activity" grouping because they recite managing personal behavior or relationships or interactions between people. This characterization misapprehends the technological nature of the amended claims. The amended claims are not directed to the concept of encouraging managers to give feedback they are directed to a specific computer-implemented system that employs machine learning techniques to analyze feedback data, classify that data by sentiment, derive a quantitative praise ratio therefrom, and apply rule-based logic to determine whether and what type of automated notification to generate and transmit. The 2024 AI SME Update notes that "the term 'certain' qualifies the 'certain methods of organizing human activity' grouping, and as a result, not all methods of organizing human activity are abstract ideas," and that "except in rare circumstances, this grouping should not be expanded beyond the activity within the enumerated sub-groupings." The amended claims do not recite fundamental economic principles, commercial or legal interactions, or managing personal behavior - they recite a specific technological implementation involving machine learning-based analysis of text data to drive automated system behavior. The amended claims do not recite a judicial exception under the "certain methods of organizing human activity" grouping and are therefore eligible at Step 2A, Prong One.
In conclusion, the amended claims do not recite any of the enumerated categories of abstract ideas under either the mental processes or certain methods of organizing human activity groupings, and therefore should not be subjected to further eligibility analysis under Step 2A, Prong Two or Step 2B. The claims are eligible as not reciting judicial exceptions at Step 2A, Prong One.
Examiner’s Response: The examiner respectfully disagrees. The claims focus on receiving, sending, and storing feedback responses in order to generate a nudge, which further includes features the argued features of “analyze feedback data, classify that data by sentiment, derive a quantitative praise ratio therefrom, and apply rule-based logic to determine whether and what type of ... notification to generate and transmit.” The examiner notes this clearly falls under “Certain Methods of Organizing Human Activity” as they recite managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). Thus, the claim recites an abstract idea enumerated under “Certain Methods of Organizing Human Activity” for Step 2A-Prong 1
With respect to Step 2A-Prong 2 ,it is noted that the features upon which applicant relies (i.e., automation and/or using machine learning techniques) 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. Further, with respect to Step 2B (i.e., automation and/or using machine learning techniques) 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 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: Step 2A, Prong Two Should the analysis proceed beyond Step 2A, Prong One, the amended claims integrate any recited judicial exception into a practical application by improving computer technology and the technical field of feedback management systems. The recently issued Desjardins Memo provides directly applicable guidance that compels a finding of practical application.
The Amended Claims Reflect the Technological Improvement Disclosed in the Specification. The Desjardins Memo, which revises MPEP §§ 2106.04(d) and 2106.05(a) and applies to the examination of this application, instructs that eligibility under the improvements consideration requires a two-step inquiry: "first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement in the functioning of a computer, or an improvement to other technology or a technical field," and "second, if the specification sets forth an improvement in technology or a technical field, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement, i.e., that the claim includes the components or steps of the invention that provide the improvement described in the specification."
Both prongs of this inquiry are satisfied here. First, the specification at paragraphs [0294] and [0300] discloses in detail a technological improvement to feedback management systems through the use of machine learning. Paragraph [0294] explains that "[t]he automation module 210 works with the feedback module 206 to provide automated nudges, reminders, and moderation to streamline the collection and dissemination of impactful group feedback." Paragraph [0300] provides substantial technical detail: "[m]achine learning techniques can be used to analyze feedback comments to identify sentiment (positive, negative, neutral) and highlight portions that are especially constructive or unconstructive," and that "a convolutional neural network model can be trained to classify feedback comments as constructive, unconstructive, off-topic, etc." with "[t]ext embedding and other natural language processing ... to extract semantic features from the comments during preprocessing." Paragraphs [0333] and [0334] further confirm that "[a]utomated feedback moderation by the automation module 210 to detect inappropriate comments using natural language processing and sentiment analysis" and that "[f]eedback nudges [are] sent by the automation module 210 when managers have not submitted feedback in a certain timeframe." The specification thus clearly discloses, with the technical detail required by the Desjardins Memo, a technological improvement in the form of machine learning-based sentiment analysis applied to automated feedback nudge generation.
Examiner’s Response: The examiner respectfully disagrees. It is noted that the features upon which applicant relies (i.e., automations and/or using machine learning techniques) 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 (i.e., wherein ...generating of the feedback nudges comprises accessing a nudges settings database to identify rules for generating nudges, analyzing, ..., the received feedback responses linked to the user profile to determine a last feedback submission date and a praise ratio for the feedback requestor, wherein the praise ratio is determined by identifying sentiment of the received feedback responses as positive, negative, or neutral ..., wherein the praise ratio indicates a proportion of positive feedback, applying the rules for generating nudges to the last feedback submission date and the praise ratio to determine whether a nudge should be generated, in response to determining a nudge should be generated based on the rules, selecting a nudge type and generating a nudge notification based on the nudge type, and sending the nudge notification to a manager of the feedback requestor). Therefore, the examiner finds this argument not persuasive.
Applicant Argues: Second, the amended claims reflect this disclosed improvement. The Desjardins Memo expressly states that "the claim itself does not need to explicitly recite the improvement described in the specification" and cautions that "[e]xaminers and panels should not evaluate claims at such a high level of generality that potentially meaningful technical limitations are dismissed without adequate explanation." The amended claims recite "analyzing, using machine learning techniques, the received feedback responses linked to the user profile to determine a last feedback submission date and a praise ratio for the feedback requestor, wherein the praise ratio is determined by identifying sentiment of the received feedback responses as positive, negative, or neutral using the machine learning techniques." This directly reflects the machine learning-based sentiment analysis improvement disclosed in the specification. The Examiner's prior basis for finding that the specification's technological features were absent from the claims is squarely eliminated by the present amendments.
Examiner’s Response: The examiner respectfully disagrees. The examiner respectfully disagrees. It is noted that the features upon which applicant relies (i.e., using machine learning techniques) 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 (i.e., analyzing, ... the received feedback responses linked to the user profile to determine a last feedback submission date and a praise ratio for the feedback requestor, wherein the praise ratio is determined by identifying sentiment of the received feedback responses as positive, negative, or neutral ...). Therefore, the examiner finds this argument not persuasive.
Applicant Argues: The Claims Provide a Particular Technological Solution to a Technological Problem. The August 2025 Memo instructs that "[a]n important consideration in determining whether a claim improves technology or a technical field is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome," and further provides that examiners should consider "whether the claim invokes computers or other machinery merely as a tool to perform an existing process, or whether the claim purports to improve computer capabilities or to improve an existing technology." The amended claims fall squarely on the eligible side of this distinction. The claimed invention addresses a technological problem through a specific automated solution: machine learning-based sentiment analysis of stored feedback responses to derive a quantitative praise ratio, combined with rule-based logic that accesses a nudges settings database, evaluates the praise ratio and last feedback submission date against those rules, selects an appropriate nudge type, generates a nudge notification, and transmits it to the relevant manager - all without human intervention. This is not a claim to the idea of encouraging feedback; it is a claim to a specific computer-implemented technological mechanism for doing so through machine learning-driven automation.
Examiner’s Response: The examiner respectfully disagrees. The examiner respectfully notes that (i.e., automation or using machine learning) is noted to be a tool to perform the abstract idea of “...analysis of stored feedback responses to derive a quantitative praise ratio, combined with rule-based logic that accesses a nudges settings ...., evaluates the praise ratio and last feedback submission date against those rules, selects an appropriate nudge type, generates a nudge notification, and transmits it to the relevant manage....” Therefore, the examiner finds this argument not persuasive.
Applicant Argues: The Examiner's "Apply It" Analysis Cannot Stand. The Examiner found that the additional elements processor, memory, and instructions involving automation amount to "mere instructions to apply the exception using a generic computer component." The Desjardins Memo revises MPEP § 2106.05(f) to clarify that "other cases have found that additional elements are more than 'apply it' or are not 'mere instructions' when the claim recites a technological solution to a technological problem." The amended claims recite far more than generic computer components performing a generic function. They recite a specific machine learning analytical process sentiment identification using ML techniques integrated with database access, rule application, conditional logic, nudge type selection, and targeted notification generation. The August 2025 Memo cautions examiners "not to oversimplify claim limitations and expand the application of the 'apply it' consideration" and reminds them that "the additional limitations should not be evaluated in a vacuum, completely separate from the recited judicial exception." When considered as a whole, the ordered combination of machine learning-based sentiment analysis, rule-based nudge determination, and automated notification generation constitutes a specific technological solution that is meaningfully more than the mere application of an abstract idea on a generic computer.
Examiner’s Response: : The examiner respectfully disagrees. It is noted that the features upon which applicant relies (i.e., automations and/or using machine learning techniques) 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 (i.e., sentiment analysis, rule-based nudge determination ,and notification generation ). Therefore, the examiner finds this argument not persuasive.
Applicant Argues: The Federal Circuit's Analysis in McRO Supports Eligibility. As explained in the MPEP and the Desjardins Memo, McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299 (Fed. Cir. 2016), illustrates the eligibility of claims that describe a specific way to solve a technological problem through incorporation of particular claimed rules that improved an existing technological process. Here, similarly, the amended claims describe a specific way machine learning-based sentiment identification applied to feedback data to derive a praise ratio, evaluated against rules stored in a nudges settings database to solve the technological problem of automated, proactive feedback culture management. The incorporation of the particular machine learning analysis into the rule-based nudge generation system improves the existing feedback management process, just as the court in McRO found that the incorporation of particular animation rules improved the existing animation process.
In conclusion, the amended claims integrate any recited judicial exception into a practical application through specific technological improvements in computer-based feedback management systems, including machine learning-based sentiment analysis, intelligent rule-based nudge generation, and automated notification generation that improve both computer functionality and the technical field of organizational feedback management technology. The claims are not directed to any judicial exception and are eligible under Step 2A, Prong Two.
Examiner’s Response: The examiner disagrees. The examiner respectfully disagrees. It is noted that the features upon which applicant relies (i.e., using machine learning techniques) 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. Therefore, the examiner finds this argument not persuasive.
Applicant Argues: Step 2B Should the analysis proceed to Step 2B, the amended independent claims 1, 19, and 20 provide significantly more than any alleged abstract idea through their specific technological implementations that amount to an inventive concept sufficient to render the claims patent-eligible.
The 2024 AI SME Update emphasizes that AI-related inventions can demonstrate patent eligibility through specific technological implementations that go beyond well-understood, routine, conventional activity. The Examiner's position that the additional elements processor, memory, and instructions amount to "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" fails to account for the amended claim language. The amended claims now recite machine learning techniques for performing sentiment identification and praise ratio determination. The use of machine learning techniques to analyze feedback responses and classify sentiment is not well-understood, routine, or conventional activity, and the Examiner has provided no evidence as required by MPEP § 2106.05(d) -that it is. As the Desjardins Memo confirms, the MPEP has been revised to add as an example of an improvement in computer functionality "[a]n improved way of training a machine learning model that protected the model's knowledge about previous tasks while allowing it to effectively learn new tasks" recognizing that machine learning implementations can provide the inventive concept required by Step 2B.
The August 2025 Memo further instructs that "[c]laims that are determined to improve computer capabilities or improve technology or a technical field support a finding that the claim integrates the judicial exception into a practical application or amounts to significantly more than the judicial exception itself." The specific combination recited in the amended claims machine learning-based sentiment classification of feedback text, derivation of a quantitative praise ratio therefrom, access to a rules database, rule-based conditional nudge determination, nudge type selection, and automated manager notification represents a specific integration of technologies that goes well beyond any alleged abstract idea and provides an inventive concept under Step 2B.
For all of the foregoing reasons, Applicant respectfully submits that the amended claims 1- 12 and 14-20 are directed to patent-eligible subject matter under 35 U.S.C. § 101, and respectfully requests that the Examiner withdraw the § 101 rejection in its entirety and pass the claims to issue.
Examiner’s Response: The examiner respectfully disagrees. As similarly noted above, 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 i.e., processor, memory/(medium), and instructions involving automation and using machine learning; 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, 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) 1-12 and 14-21 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:
receiving a selection of a feedback visibility setting for a feedback message from a plurality of predefined feedback visibility settings;
receiving the feedback message and a selection of one or more recipients;
sending the feedback message to the selection of the one or more recipients based on the selection of the feedback visibility setting;
receiving a request for feedback and a selection of one or more feedback providers;
sending the request for feedback to the selection of the one or more feedback providers; and
storing received feedback responses, wherein the storing of the received feedback responses comprises linking the received feedback responses to a user profile of the feedback requestor, wherein the linking enables
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 receiving a selection of a feedback visibility setting for a feedback message from a plurality of predefined feedback visibility settings; receiving the feedback message and a selection of one or more recipients sending the feedback message to the selection of the one or more recipients based on the selection of the feedback visibility setting; receiving a request for feedback and a selection of one or more feedback providers; sending the request for feedback to the selection of the one or more feedback providers; and storing received feedback responses, wherein the storing of the received feedback responses comprises linking the received feedback responses to a user profile of the feedback requestor, wherein the linking enables generating feedback nudges, wherein the generating of the feedback nudges comprises accessing a nudges settings database to identify rules for generating nudges, analyzing, the received feedback responses linked to the user profile to determine a last feedback submission date and a praise ratio for the feedback requestor, wherein the praise ratio is determined by identifying sentiment of the revived feedback responses as positive, negative, or neutral, wherein the praise ratio indicates a proportion of positive feedback, applying the rules for generating nudges to the last feedback submission date and the praise ratio to determine whether a nudge should be generated, in response to determining a nudge should be generated based on the rules, selecting a nudge type and generating a nudge notification based on the nudge type, and sending the nudge notification to a manager of the feedback requestor, 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 for similar claim(s) 19 and 20, i.e., processor, memory/(medium), and instructions involving automation; 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 receiving a selection of a feedback visibility setting for a feedback message from a plurality of predefined feedback visibility settings; receiving the feedback message and a selection of one or more recipients sending the feedback message to the selection of the one or more recipients based on the selection of the feedback visibility setting; receiving a request for feedback and a selection of one or more feedback providers; sending the request for feedback to the selection of the one or more feedback providers; and storing received feedback responses, wherein the storing of the received feedback responses comprises linking the received feedback responses to a user profile of the feedback requestor, wherein the linking enables generating feedback nudges, wherein the generating of the feedback nudges comprises accessing a nudges settings database to identify rules for generating nudges, analyzing the received feedback responses linked to the user profile to determine a last feedback submission date and a praise ratio for the feedback requestor, wherein the praise ratio is determined by identifying sentiment of the revived feedback responses as positive, negative, or neutral, wherein the praise ratio indicates a proportion of positive feedback, applying the rules for generating nudges to the last feedback submission date and the praise ratio to determine whether a nudge should be generated, in response to determining a nudge should be generated based on the rules, selecting a nudge type and generating a nudge notification based on the nudge type, and sending the nudge notification to a manager of the feedback requestor, 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 the receiving, sending steps with respect to visibility and recipients/requestors and further manually store such feedback linked to user profiles (i.e., user records) in order to generate a nudge notification. 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 automation and/or using the machine learning techniques. 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 for similar claim(s) 19 and 20, i.e., processor, memory/(medium), and instructions involving automation and/or using the machine learning techniques; 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 2-12, 14-18, and 21; 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 “managing” feedback 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 3, 6, 8, 9, 10, 13 and 17-18 – implementations of “interface”/“a user interface” and claims 15-16 – implement an application interface); 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
Applicant has incorporated the allowable subject matter as noted in the Non-Final rejection dated on 8/21/2025
Conclusion
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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