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 .
DETAILED ACTION
Status of Claims
This action is a Final action on the merits in response to the application filed on 06/16/2026.
Claims 1, 6, 10, 11 and 16 have been amended. Claims 1 – 20 are currently pending and have been examined in this application.
Response to Amendment
Applicant’s amendment has been considered.
Applicant’s amendment is sufficient to overcome the 112(b) rejection set forth in the previous office action.
Response to Arguments
Applicant’s remarks have been considered.
Applicant argues, “… that the currently amended independent claims are directed to patent eligible subject matter and not directed to certain methods of organizing human activity, a mental processes, or any other abstract idea.” (pg. 13)
The limitations encompass Certain Methods of Organizing Human Activities related to managing personal behavior or interaction, but for the recitation of generic computer components (e.g. a processor). For example, receiving employee feedback, generating a prompt for obfuscation and summary generation by rewriting unstructured text and generating the obfuscated summary using LLM involve collecting and analyzing feedback data to determine behavior or interaction insights. Accordingly, the claim recites an abstract idea of Certain Methods of Organizing Human Activity.
Applicant argues, “…the claims integrate any such abstract idea into a practical application.” (pg.13)
Examiner respectively disagrees. The judicial exceptions are not integrated into a practical application. The claims recite the additional elements of a manger feedback interface on a manager client device, a non-transitory computer-readable medium, a processor and a computer system These are generic computer components recited at a high level of generality as performing generic computer functions (¶0147, general purpose computer).
For instance, in Claim 1 the steps of receiving employee feedback data is data gathering activity; the steps of generating a prompt for obfuscation and summary generation LLM to generate an obfuscated summary by rewriting the unstructured text of feedback data to obscure a source while maintaining intent; generating the summary by utilizing the LLM are considered collecting and analyzing data; and providing the obfuscated summary within an interface is displaying a result of the analysis. Accordingly, the claim recites an abstract idea of Certain Methods of Organizing Human Activity.
Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer components (e.g. a processor). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a processor). Note, the use of the obfuscation and summary generation LLM and recommendation LLM is interpreted as ‘apply it’. The additional elements do not integrate the abstract ideas into a practical application because it does not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The remainder of Applicant’s arguments are mute in view of new grounds of rejection as necessitated by amendment.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-9, 11, 12, 16-20 are rejected under 35 U.S.C. 112, (b)/second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
The following limitations are rejected under lack of antecedent basis.
Claim 1 recites, “the large language model” at lines 8-9. The claims previously recite “an obfuscation and summary generation large language model” a line 3. Claims 2-9 are rejected based on their dependency on Claim 1.
Claim 11 recites, “the [personalized] modification suggestion at line 9.
Claim 12 recites, “an [the] obfuscation and summary generation large language model” at lines 8-9.
Claim 15 recites, “the modification suggestion” at lines 3, 4 and 8. It is unclear if Applicant is referring to another modification suggestion or the personalized modification in Claim 10.
Claim 16 recites, “the [personalized] modification suggestion” at line 17. Claims 17-20 are rejected based on their dependency on Claim 16.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefore, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites:
receiving employee feedback data comprising unstructured text;
generating a prompt for an obfuscation and summary generation large language model to generate an obfuscated summary of the employee feedback data by rewriting the unstructured text of the employee feedback data to obscure a source for the employee feedback data while maintaining one or more of information, an idea, or a sentiment of the employee feedback data;
generating, the obfuscated summary of the employee feedback data by utilizing the large language model to rewrite the unstructured text of the employee feedback data based on the prompt; and
providing the obfuscated summary within a manager feedback interface on a manager client device.
The limitation under its broadest reasonable interpretation covers Certain Methods of Organizing Human Activities related to managing personal behavior or interaction, but for the recitation of generic computer components (e.g. a processor). For example, receiving employee feedback, generating a prompt for obfuscation and summary generation by rewriting unstructured text and generating the obfuscated summary using LLM involve collecting and analyzing feedback data to determine behavior or interaction insights. Accordingly, the claim recites an abstract idea of Certain Methods of Organizing Human Activity.
Claim 10 recites:
provide a summary of employee feedback data within a manager feedback interface on a manager client device;
receive, [from the manager client device], a request to generate a personalized modification suggestion based on the employee feedback data;
based on comparing the employee feedback data to the request to generate the personalized modification suggestion, select one or more instances of employee feedback data;
generate, utilizing a recommendation large language model, the personalized modification suggestion based on the one or more instances of the employee feedback data corresponding to the personalized modification suggestion; and
The limitation under its broadest reasonable interpretation covers Certain Methods of Organizing Human Activities related to managing personal behavior or interaction,
but for the recitation of generic computer components (e.g. a processor). For example, providing a summary of employee feedback, receiving a request to generate a modification suggestion and generating the modification suggestion involves collecting and analyzing data related to determining behavior or interaction insights. Accordingly, the claim recites an abstract idea of Certain Methods of Organizing Human Activity.
Claim 16 recites:
generate an obfuscated summary of employee feedback data by;
generating a prompt for an obfuscation and summary generation large language model to generate an obfuscated summary of the employee feedback data by rewriting the unstructured test of the employees feedback data to obscure a source for the employee feedback data while maintaining one or more of information, an idea, or a sentiment of the employee feedback data; and
utilizing the obfuscation and summary generation large language model to generate the obfuscated summary based on the prompt;
provide the obfuscated summary of the employee feedback data within a manager feedback interface on a manager client device;
receive, from the manager client device, a request to generate a modification suggestion based on the employee feedback data; and
in response to receiving the request to generate the modification suggestion, generate utilizing a recommendation large language model, the personalized modification suggestion based on one or more instances of employee feedback data corresponding to the personalized modification suggestion.
The limitation under its broadest reasonable interpretation covers Certain Methods of Organizing Human Activities related to managing personal behavior or interaction,
but for the recitation of generic computer components (e.g. a processor). For example, generating an obfuscated summary, providing the summary and generating a modification involves collecting and analyzing data related to determining behavior or interaction insights. Accordingly, the claim recites an abstract idea of Certain Methods of Organizing Human Activity.
The dependent claims encompass the same abstract ideas. For instance, Claim 2 is directed to generating a semantic similarity and quality metric (analyzing data); Claim 3 and 11 are directed to providing an interface with a generate modification widget; Claim 4 is directed to providing and receiving survey response; Claims 5 is directed to generating the obfuscated summary based on number of devices; Claim 6 is directed to generating a prompt to generate obfuscated summary based number of employee feedback; Claim 7 is directed to generating high-level obfuscated summary; Claim 8 is directed to displaying feedback data based on topic; Claim 9 is directed to filtering employee data by topic, Claim 12 is directed to receiving feedback data and generating summary; Claim 13 is directed to determining number of instance of employee feedback data not satisfying a threshold; Claim 14 is directed to determining number of instances of employee feedback data satisfies a summary threshold; Claim 15 is directed to request to generate a modification suggestion and generating topic level summary; Claim 17 is directed to providing a modification suggestion; Claim 18 is directed to providing and receiving survey response ; Claim 18 is directed to determining number of client devices providing feedback satisfies a minimum threshold and Claim 20 is directed to receiving a request to generate topic level summary and generating topic summary.
The judicial exceptions are not integrated into a practical application. Claim 1 recites the additional elements of a manger feedback interface on a manager client device. Claim 10 recites the additional elements of a non-transitory computer-readable medium, a process an a computer system and a manager feedback interface on a manger client device. Claim 16 recites the additional elements of a processor and non-transitory computer readable storage medium. These are generic computer components recited at a high level of generality as performing generic computer functions (¶0147, general purpose computer).
For instance, in Claim 1 the steps of receiving employee feedback data is data gathering activity; the steps of generating a prompt for obfuscation and summary generation LLM to generate an obfuscated summary by rewriting the unstructured text of feedback data to obscure a source while maintaining intent; generating the summary by utilizing the LLM are considered collecting and analyzing data; and providing the obfuscated summary within an interface is displaying a result of the analysis.
Regarding Claim 10 the steps of providing a summary feedback data is display functionality; receiving a request to generate a personalized modification suggestion is making a selection on a display to invoke analysis; the step of comparing employee feedback data to generate the personalized modification suggestion; generating, utilizing a recommendation LLM the personalized modification suggestion utilizing a involves analyzing data; and providing the personalized modification suggestion is display functionality.
Regarding Claim 16 the steps of generating an obfuscated summary of feedback utilizing a prompt and LLM by rewriting unstructured text is analyzing data; generating the obfuscated summary based on the prompt; the step of providing the obfuscated summary within a manager feedback interface is displaying the result of the analysis; the step of receiving a request to generate a personalized modification suggestion and generating utilizing a recommendation LLM the personalized modification suggestion involve analyzing data and producing a result.
Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component (e.g. a processor). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a processor). Note, the use of the obfuscation and summary generation LLM and recommendation LLM is interpreted as ‘apply it’. The additional elements do not integrate the abstract ideas into a practical application because it does not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3, 4, 10-12 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Duff (US 2025/0086400) further in view of Ghantasala et al. (US 12619781)
Claim 1:
Duff discloses:
A computer-implemented method comprising: receiving employee feedback data comprising unstructured text; (see at least Abstract, receiving unstructured feedback; see also ¶0045 and ¶0054, employee submitting feedback anonymously; see at least ¶0058, unstructured feedback)
generating a prompt for an obfuscation and summary generation large language model to generate an obfuscated summary of the employee feedback data [by rewriting the unstructured text of the employee feedback data to obscure a source for the employee feedback data while maintaining one or more of information, an idea, or a sentiment of the employee feedback data];(see at least Figure 8 and associated text; see also ¶0086, aggregate feedback for viewing user (811))
generating, the obfuscated summary of the employee feedback data by utilizing the large language model to rewrite the unstructured text of the employee feedback data based on the prompt; and
; and (see at least Figure 3 and associated text; see also ¶0144-¶0151, submit first content item of text to a prompt, prompt may instruct LLM on summary abstract level then summarizes content and outputs a second summarized content; see also ¶0292, anonymize sensitive data )
providing the obfuscated summary within a manager feedback interface on a manager client device. (see at least Figure 8 and associated text)
While Duff discloses the above limitations, Duff does not explicitly disclose the following limitations; however, Ghantasala does disclose:
generating a prompt for an obfuscation and summary generation large language model to generate an obfuscated summary of the employee feedback data [by rewriting the unstructured text of the employee feedback data to obscure a source for the employee feedback data while maintaining one or more of information, an idea, or a sentiment of the employee feedback data];(see at least Abstract, utilizing generative AI to rewrite text that retains the meaning, tone and sentiment and also anonymized the text; see also column 4, lines 15-25)
generating, the obfuscated summary of the employee feedback data by utilizing the large language model to rewrite the unstructured text of the employee feedback data based on the prompt; and (see at least see also column 4, lines 15-25, utilizing generative AI to rewrite text that retains the meaning, tone and sentiment and also anonymized the text; see also column 6, lines 29-45 )
providing the obfuscated summary within a manager feedback interface on a manager client device. (see at least column 7, 7-16, generate feedback analysis data)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the anonymous employee feedback of Duff with the verbatim feedback processing with AI trained to rewrite text including anonymizing of Ghantasala in order to process large amounts of feedback data while maintaining meaning, tone or sentiment of feedback (column 1, lines 27-30).
Claim 3:
Duff and Ghantasala disclose claim 1. Duff further discloses:
further comprising: providing the obfuscated summary within the manager feedback interface on the manager client device by providing the obfuscated summary in a feedback widget associated with the manager feedback interface; (see at least Figure 8 and associated text; see also ¶0086, manager or hr. interface)
receiving, within the feedback widget, user input from the manager client device to generate a modification suggestion based on the employee feedback data; (see at least Figure 8 and associated text; see also ¶0086, manager or hr interface displays AI insights which provide suggested actions for negative feedback)
and providing the modification suggestion within the feedback widget on the manager client device. (see at least Figure 8 and associated text; see also ¶0086, manager or hr interface displays AI insights which provide suggested actions for negative feedback)
Claim 4:
Duff and Ghantasala disclose claim 1. Duff further discloses:
wherein receiving the employee feedback data comprising unstructured text further comprises: (see at least ¶0005, feedback survey; see also ¶0010, unstructured data )
providing, to an employee client device, a digital feedback survey comprising an option to provide unstructured text; and (see at least ¶0005, feedback survey; see also ¶0010, unstructured data; see also ¶0030 )
receiving, from the employee client device, a response to the digital feedback survey comprising the employee feedback data. (see also ¶0039)
Claim 10:
Duff discloses:
A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computer system to: provide a summary of employee feedback data within a manager feedback interface on a manager client device; (see at least Figure 8 and ¶0086, manager or hr interface; see also Figure 1; see also Claim 9, processor and memory)
receive, from the manager client device, a request to generate a modification suggestion based on the employee feedback data; (see at least Figure 8 and associated text; see also ¶0086, manager or hr interface displays AI insights which provide suggested actions for negative feedback)
based on comparing the employee feedback data to the request to generate the personalized modification suggestion, based on employee feedback data; (see at least Figure 8 and associated text; see also ¶0086, manager or hr interface displays AI insights which provide suggested actions for negative feedback)
generate, utilizing a recommendation large language model, the personalized modification suggestion based on the employee feedback data based on the one or more instances of employee feedback data corresponding to the personalized modification suggestion; (see at least Figure 8 and associated text; see also ¶0086, manager or hr interface displays AI insights which provide suggested actions for negative feedback)
provide the personalized modification suggestion within the manager feedback interface on the manager client device. (see at least Figure 8 and associated text; see also ¶0086, manager or hr interface displays AI insights which provide suggested actions for negative feedback)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the feedback system interface of Duff with the AI trained to rewrite text including anonymizing of Ghantasala to provide an analytic manager dashboard to review and analyze feedback (see ¶0086).
Claim 11:
Duff and Ghantasala disclose claim 10. Duff further discloses:
further comprising instructions that, when executed by the at least one processor, cause the computer system to: provide the summary within the manager feedback interface on the manager client device by providing the summary in a feedback widget associated with the manager feedback interface; (see at least Figure 8 and ¶0086, manager or hr interface; see also Figure 1)
receive the request to generate a modification suggestion within the feedback widget; and (see at least Figure 8 and associated text; see also ¶0086, manager or hr interface displays AI insights which provide suggested actions for negative feedback)
provide the personalized modification suggestion with the manager feedback interface by providing the modification suggestion within the feedback widget. (see at least Figure 8 and associated text; see also ¶0086, manager or hr interface displays AI insights which provide suggested actions for negative feedback)
Claim 12:
While Duff and Ghantasala disclose claim 10, Duff further discloses receiving employee feedback data comprising unstructured text; (see at least Abstract, receiving unstructured feedback; see also ¶0045 and ¶0054, employee submitting feedback anonymously; see at least ¶0058, unstructured feedback),Duff does not explicitly disclose the following limitations; however, Ghantasala does disclose:
further comprising instructions that, when executed by the at least one processor, cause the computer system to: receive the employee feedback data, wherein the employee feedback data comprising unstructured text; (see at least Claim 1, feedback comprising unstructured text)
generate a prompt for an obfuscation and summary generation large language model to generate an obfuscated summary of the employee feedback data; and (see at least Abstract, utilizing generative AI to rewrite text that retains the meaning, tone and sentiment and also anonymized the text; see also column 4, lines 15-25)
generate the summary of the employee feedback data by providing the prompt to an obfuscation and summary generation large language model to generate the obfuscated summary of the employee feedback data. (see at least see also column 4, lines 15-25, utilizing generative AI to rewrite text that retains the meaning, tone and sentiment and also anonymized the text; see also column 6, lines 29-45)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the feedback system interface of Duff with the AI trained to rewrite text including anonymizing of Ghantasala to provide an analytic manager dashboard to review and analyze feedback (see ¶0086).
Claim 16:
Duff discloses
A system comprising: at least one processor; and at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to: (see at least Claim 9, one or more processors coupled to a memory)
generate an obfuscated summary of employee feedback data by: generating a prompt for an obfuscation and summary generation large language model to generate an obfuscated summary of the employee feedback data [by rewriting the unstructured text of the employee feedback data to obscure a source for the employee feedback data while maintaining one or more of information, an idea, or a sentiment of the employee feedback data];(see at least Figure 8 and associated text; see also ¶0086, aggregate feedback for viewing user (811); ¶0045 and ¶0054, employee submitting feedback anonymously; see at least ¶0058, unstructured feedback )
utilizing the obfuscation and summary generation large language model to generate the obfuscated summary based on the prompt:
provide the obfuscated summary of the employee feedback data within a manager feedback interface on a manager client device; (see at least (see at least Figure 8 and associated text))
receive, from the manager client device, a request to generate a modification suggestion based on the employee feedback data; and (see at least Figure 8 and associated text; see also ¶0086, manager or hr interface displays AI insights which provide suggested actions for negative feedback)
in response to receiving the request to generate the modification suggestion, generate utilizing a recommendation large language model, the personalized modification suggestion based on one or more instances of employee feedback data corresponding to the personalized modification suggestion (see at least Figure 8 and associated text; see also ¶0086, manager or hr interface displays AI insights which provide suggested actions for negative feedback)
While Duff discloses the above limitations, Duff does not explicitly disclose the following limitations, however, Ghantasala does disclose:
generating a prompt for an obfuscation and summary generation large language model to generate an obfuscated summary of the employee feedback data [by rewriting the unstructured text of the employee feedback data to obscure a source for the employee feedback data while maintaining one or more of information, an idea, or a sentiment of the employee feedback data];(see at least Abstract, utilizing generative AI to rewrite text that retains the meaning, tone and sentiment and also anonymized the text; see also column 4, lines 15-25)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the anonymous employee feedback of Duff with the verbatim feedback processing with AI trained to rewrite text including anonymizing of Ghantasala in order to process large amounts of feedback data while maintaining meaning, tone or sentiment of feedback (column 1, lines 27-30).
Claim 17:
Duff and Ghantasala disclose claim 16, Duff further discloses:
further comprising instructions that, when executed by at least one processor, cause the system to provide the modification suggestion in the manager feedback interface on the manager client device. (see at least Figure 8 and ¶0086, manager or hr interface; see also Figure 1)
Claim 18:
While Duff and Ghantasala disclose claim 16 and Duff further discloses surveys (see ¶0035), Duff does not explicitly disclose the following limitation; however, Ghantasala does disclose:
provide, to an employee client device, a digital feedback survey comprising an option to provide unstructured text; and (see at least column 6, lines 14-20, surveys; see also Claim 1, unstructured text)
receive the employee feedback data by receiving, from the employee client device, a response to the digital feedback survey comprising unstructured text corresponding to the option to provide unstructured text about a manager performance. (see at least column 6, lines 14-20, surveys; see also Claim 1, unstructured text)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the anonymous employee feedback of Duff with the verbatim feedback processing with AI trained to rewrite text including anonymizing of Ghantasala in order to process large amounts of feedback data while maintaining meaning, tone or sentiment of feedback (column 1, lines 27-30).
Claims 2, 7, 8, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Duff (US 2025/0086400) further in view of Ghantasala et al. (US 12619781) further in view of Gardner et al. (US 2025/0061291).
Claim 2:
While Duff and Ghantasala disclose claim 1, neither explicitly disclose the following limitations; however, Gardner further discloses:
generating a semantic similarity metric and a summary quality metric for the obfuscated summary; and (see at least ¶0096, fidelity semantic similarity metrics compare reference summaries to system outputs by n-gramp overlap; see at least ¶0136)
utilizing the semantic similarity metric and the summary quality metric to analyze the obfuscated summary from the obfuscation and summary generation large language model. (see at least ¶1165, evaluation module analyzes summarization outputs using metrics like semantic equivalence, redundancy, and human validation studies to quantify quality; see also ¶01188-¶1191, calculating automated metrics like semantic similarity, conciseness, and grammar correctness to quantify quality; see also ¶0123, summary ratings measure quality and satisfaction)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the anonymous employee feedback of Duff and the verbatim feedback processing with AI trained to rewrite text including anonymizing of Ghantasala with the semantic analysis of Gardner to analyze summarization outputs (see ¶1188).
Claim 7:
While Duff and Ghantasala disclose claim 1, neither explicitly disclose the following limitation; Gardner further discloses:
wherein generating the obfuscated summary of the employee feedback data comprising generating a high-level obfuscated summary for the employee feedback data and one or more topic-level obfuscated summaries of a portion of the employee feedback data. (see at least ¶0016-¶0017, abstraction level control parameters; see also ¶0027, the prompt includes first content item and level of abstraction; see also ¶00576, key topics; see also ¶00592)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the anonymous employee feedback of Duff and the verbatim feedback processing with AI trained to rewrite text including anonymizing of Ghantasala with the abstraction level for summaries of Gardner to analyze summarization outputs (see ¶0016).
Claim 8:
While Duff, Ghantasala and Gardiner disclose claim 7, neither Duff nor Ghantasala explicitly discloses the following limitations; however, Gardner does disclose:
further comprising: receiving, within the manager feedback interface on the manager client device, a user input requesting a display of employee feedback data based on a topic; and (see at least ¶0761, prompt request identify key parties, topics and outcome)
generating the one or more topic-level obfuscated summaries in response to receiving the user input requesting the display of employee feedback data based on the topic. (see at least ¶0762, response output parties, topic and outcome; see also ¶1171, to enhance the summarization of classifiers tag topics then retrieves relevant facts for those topics; see also ¶0062)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the anonymous employee feedback of Duff and the verbatim feedback processing with AI trained to rewrite text including anonymizing of Ghantasala with the abstraction level for summaries of Gardner to analyze summarization outputs (see ¶0016).
Claim 9:
While Duff and Ghantasala disclose claim 1, neither explicitly disclose the following limitations; however, Gardner further discloses:
further comprising: receiving, from the manager client device and within the manager feedback interface, a user selection of an option to filter the employee feedback data by topic; (see at least ¶0066, single prompt could provide the text to LLM for overall summarization and have classifiers to tag topics (filtering); (see at least ¶0761, prompt request identify key parties, topics and outcome )
in response to receiving the user selection of the option to filter the employee feedback data by topic, provide the employee feedback data to the obfuscation and summary generation large language model to generate a topic-level obfuscated summary; and (see at least ¶0762, response output parties, topic and outcome; see also ¶1171, to enhance the summarization classifiers tag topics then retrieves relevant facts for those topics; see also ¶0062)
providing the topic-level obfuscated summary within the manager feedback interface on the manager client device. (see at least ¶0762, response output parties, topic and outcome; see also ¶1171, to enhance the summarization classifiers tag topics then retrieves relevant facts for those topics; see also ¶0062)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the anonymous employee feedback of Duff and the verbatim feedback processing with AI trained to rewrite text including anonymizing of Ghantasala with the abstraction level for summaries of Gardner to analyze summarization outputs (see ¶0016).
Claim 15:
While Duff and Ghantasala disclose claim 10, and Duff does disclose:
further comprising instructions that, when executed by the at least one processor, cause the computer system to provide the modification suggestion by: receiving, the request to generate the modification suggestion by receiving a request to generate a topic-level modification suggestion based on the employee feedback data; (see at least Figure 8 and associated text; see also ¶0086, manager or hr interface displays AI insights which provide suggested actions for negative feedback)
generating, utilizing an obfuscation and summary generation large language model, a topic-level summary of the employee feedback data; and (see at least Figure 8 and associated text; see also ¶0086, manager or hr interface displays AI insights which provide suggested actions for negative feedback)
providing the modification suggestion by providing the topic-level summary of the employee feedback data within the manager feedback interface on the manager client device. (see at least Figure 8 and associated text; see also ¶0086, manager or hr interface displays AI insights which provide suggested actions for negative feedback)
While Duff discloses the above limitations, neither Duff nor Ghantasala discloses the following limitation; however, Gardiner does disclose:
[receiving, the request to generate the modification suggestion by] receiving a request to generate a topic-level [modification suggestion based on the employee feedback data]; (see at least ¶0762, response output parties, topic and outcome; see also ¶1171, to enhance the summarization classifiers tag topics then retrieves relevant facts for those topics; see also ¶0062), Gardner does not explicitly disclose the following limitations; however,
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the anonymous employee feedback of Duff and the verbatim feedback processing with AI trained to rewrite text including anonymizing of Ghantasala with the abstraction level for summaries of Gardner to analyze summarization outputs (see ¶0016).
Claim 20:
While Duff and Ghantasala disclose claim 16, neither explicitly disclose the following limitations; however, Gardner further discloses:
receive, from the manager client device, a request to generate a topic-level obfuscated summary of the employee feedback data; and (see at least ¶0761, prompt request identify key parties, topics and outcome)
in response to receiving the request to generate the topic-level obfuscated summary of the employee feedback data, generate the topic-level obfuscated summary utilizing the obfuscation and summary generation large language model. (see at least ¶0762, response output parties, topic and outcome; see also ¶1171, to enhance the summarization classifiers tag topics then retrieves relevant facts for those topics; see also ¶0062)
Claims 5, 13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Duff (US 2025/0086400) further in view of Ghantasala et al. (US 12619781) further in view of Durairaj et al. (US 11989395).
Claim 5:
While Duff and Ghantasala disclose claim 1 and Ghantasala further discloses s generating the prompt for the obfuscation and summary generation large language model to generate the obfuscated summary of the employee feedback data (see at least see also column 4, lines 15-25, utilizing generative AI to rewrite text that retains the meaning, tone and sentiment and also anonymized the text; see also column 6, lines 29-45 ), neither Duff nor Ghantasala explicitly disclose the following limitations; however Durairaj does disclose:
[wherein generating the prompt for the obfuscation and summary generation large language model to generate the obfuscated summary of the employee feedback data is] based on determining that a number of employee client devices providing employee feedback data satisfies an obfuscated summary threshold but does not satisfy a minimum employee threshold. (see at least column 8, lines 8-32, determine feedback has been received from a threshold number of client devices and aggregating the feedback received from client devices)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the anonymous employee feedback of Duff with the verbatim feedback processing with AI trained to rewrite text including anonymizing of Ghantasala with receiving feedback from client devices vs users of Durairaj to determine similarity or significance of feedback from multiple devices to determine a similar issue (see column 8, lines 29-32).
Claim 13:
While Duff and Ghantasala disclose claim 1 and Duff further discloses cause the computer system to provide the summary of the employee feedback data within the manager feedback interface by (see at least Figure 8 and associated text; see also ¶0086, manager or hr interface), neither Duff nor Ghantasala explicitly disclose the following limitations; however, Durairaj does disclose:
[further comprising instructions that, when executed by the at least one processor, cause the computer system to provide the summary of the employee feedback data within the manager feedback interface by]: determining that a number of instances of employee feedback data does not satisfy an obfuscated summary threshold; and based on determining that the number of instances of employee feedback data does not satisfy the obfuscated summary threshold, generating the summary of the employee feedback data. (see at least column 8, lines 8-32, determine feedback has been received from a threshold number of client devices and aggregating the feedback received from client devices)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the anonymous employee feedback of Duff with the verbatim feedback processing with AI trained to rewrite text including anonymizing of Ghantasala with receiving feedback from client devices vs users of Durairaj to determine similarity or significance of feedback from multiple devices to determine a similar issue (see column 8, lines 29-32).
Claim 19:
While Duff and Ghantasala disclose claim 16, and Duff further discloses generate the obfuscated summary of the employee feedback (see at least Figure 3 and associated text; see also ¶0144-¶0151, submit first content item of text to a prompt, prompt may instruct LLM on summary abstract level then summarizes content and outputs a second summarized content; see also ¶0292, anonymize sensitive data, neither Duff nor Ghantasala explicitly disclose the following limitations; however, Durairaj does disclose:
further comprising instructions that, when executed by the at least one processor, cause the system to: determine that a number of employee client devices providing employee feedback data satisfies a minimum employee threshold; and generate the obfuscated summary of the employee feedback data based on determining that the number of employee client devices providing employee feedback data satisfies the minimum employee threshold. (see at least column 8, lines 8-32, determine feedback has been received from a threshold number of client devices and aggregating the feedback received from client devices)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the anonymous employee feedback of Duff with the verbatim feedback processing with AI trained to rewrite text including anonymizing of Ghantasala with receiving feedback from client devices vs users of Durairaj to determine similarity or significance of feedback from multiple devices to determine a similar issue (see column 8, lines 29-32).
Claim 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Duff (US 2025/0086400) further in view of Ghantasala et al. (US 12619781) further in view of Ouyang et al. (US 2025/0094728).
Claim 6:
While Duff and Ghantasala disclose claim 1, Duff further discloses wherein generating the prompt for an obfuscation and summary generation large language model to generate an obfuscated summary further comprises (see at least Figure 8 and associated text; see also ¶0086, aggregate feedback for viewing user (811)), neither Duff nor Ghantasala explicitly disclose the following limitations; however, Ouyang does disclose:
wherein generating the prompt for an obfuscation and summary generation large language model to generate an [obfuscated] summary further comprises: (see at least LLM generates summary of reviews)
determining that a number of instances of employee feedback data satisfies a minimum feedback threshold; and (see at least Figures 6-7 and associated text; see also ¶0115-¶0116, defined number of reviews are received)
generating the prompt to generate the obfuscated summary of the employee feedback data in response to determining that the number of instances of employee feedback data satisfies the minimum feedback threshold. (see at least Figures 6-7 and associated text; see also ¶0115-¶0116, defined number of reviews are received and LLM generates summary reviews
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the anonymous employee feedback of Duff with the verbatim feedback processing with AI trained to rewrite text including anonymizing of Ghantasala with the predefined number of reviews for LLM summaries of Ouyang to provide valuable insights on user experience and recommendations (see ¶0002).
Claim 14:
While Duff and Ghantasala disclose claim 10, Duff further discloses wherein generating the prompt for an obfuscation and summary generation large language model to generate an obfuscated summary further comprises (see at least Figure 8 and associated text; see also ¶0086, aggregate feedback for viewing user (811)), neither Duff nor Ghantasala explicitly disclose the following limitations; however, Ouyang does disclose:
further comprising instructions that, when executed by the at least one processor, cause the computer system to provide the summary of the employee feedback data within the manager feedback interface by: determining that a number of instances of employee feedback data satisfies an obfuscated summary threshold; (see at least Figures 6-7 and associated text; see also ¶0115-¶0116, defined number of reviews are received)
based on determining that the number of instances of employee feedback data satisfies the obfuscated summary threshold, generating the summary of the employee feedback data by generating an obfuscated summary of the employee feedback data; and (see at least Figures 6-7 and associated text; see also ¶0115-¶0116, defined number of reviews are received and LLM generates summary reviews)
providing the summary of employee feedback data within the manager feedback interface by providing the obfuscated summary of the employee feedback data. (see at least Figures 6-7 and associated text; see also ¶0115-¶0116, defined number of reviews are received and LLM generates summary reviews)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the anonymous employee feedback of Duff with the verbatim feedback processing with AI trained to rewrite text including anonymizing of Ghantasala with the predefined number of reviews for LLM summaries of Ouyang to provide valuable insights on user experience and recommendations (see ¶0002).
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Zohar et al. (US 2026/0079732) discloses For example, LLM infrastructure, such as the LLM engine of the copilot, may be configured to provide feedback to a user’s input, suggest revisions to a user’s input, assist users with analyzing text, and assist users with generating text.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Renae Feacher/
Primary Examiner, Art Unit 3625
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