DETAILED ACTION
This communication is in response to the Amendments and Arguments filed on 05/20/2026. Claims 1-20 are pending and have been examined.
Any previous rejection/objection not mentioned in this Office Action has been withdrawn by the Examiner.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/11/2026 and 08/06/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Change of Examiner
The Examiner of record has changed from Feng-Tzer Tzeng To SPE Paras Shah.
Response to Amendments and Arguments
The Applicant has amended independent claims 1 and 15 to recite various new limitations to address the 35 USC 101 and 35 USC 103 rejections. The Applicant has separately amended claim 9 to recite a different limitations than claims 1 and 15. Nevertheless, the Applicant’s arguments are moot in view of new grounds for rejections.
With respect to the 35 USC 101 abstract rejections, the Applicant asserts on page 10-11:
Under Step 2A, Prong One of the Alice/Mayo framework, the pending claims are not directed to a mental process or other abstract idea. As amended, independent claims 1, 9, and 15 recite capturing prompts, corresponding replies, and user interaction data from an interface with a large language model ("LLM") service, including interaction-specific signals such as dwell time, invocation of stop-replying features, and selection of content included in replies. The claims further recite analyzing such interaction data across a group of observed users to generate usage patterns associated with the LLM service and generating guidance for modifying subsequent prompts submitted to the LLM service. Such operations require collection and processing of interface-level interaction telemetry generated during operation of a computer-based LLM environment and cannot practically be performed in the human mind. Rather than merely observing or evaluating information, the claims recite a specific technical implementation for capturing, processing, and utilizing interaction data associated with operation of an LLM system.
The Examiner respectfully disagrees with these assertions. The Examiner notes The each of the stated recitations relate to performing analysis on what was imputed into the model (prompts) and what was outputted from the model (responses), where the model is an LLM. This is done on a per user basis This capturing per the claimed language and as correctly noted by the applicant relates to “data gathering” processes which is specifically only analyzing inputs and outputs of an already known model such as a LLM and presenting insights on a display, noted to be post solution activity by conveying these patterns and recommending adjustments to prompts. These are all steps that can be performed mentally or via pen and paper. This does not require a technical implementation as asserted by the applicant as there is no feedback to the LLM nor is the LLM ever used other than analysis of the inputs and outputs.
With respect to the 35 USC 101 abstract rejections, the Applicant asserts on page 11:
Even assuming, arguendo, that the claims recite an abstract idea, the claims are integrated into a practical application under Step 2A, Prong Two. In particular, the amended claims recite generating guidance for modifying subsequent prompts submitted to the LLM service based on the generated insights, wherein the guidance reduces repeated prompting interactions between a user and the LLM service, thereby reducing computational resource usage by the LLM service. The present application explains that improved prompting reduces "conversational churn" by eliciting improved replies from the LLM system. See Spec. 1 [0020]. The Specification further explains that reduced conversational churn decreases the computing resources required by LLM engines and reduces energy consumption associated with processing repeated queries. See Spec.1 [0021]. Accordingly, the claims are directed to a specific technological solution that improves operation and efficiency of computer-implemented LLM systems by reducing unnecessary prompting interactions and associated computational load.
The Examiner respectfully disagrees with these assertions. The Applicant notes that the claims recite “generating guidance for modifying subsequent prompts submitted to the LLM service based on the generated insights”. This limitation in itself is directed towards an abstract idea and relates to a step that can be performed mentally based on already known prompts submitted into an LLM and the responses provided by the LLM. Based on this, a human/expert can recommend modifications to prompts to better provide a response that matches the prompt. The examiner has not interpreted this as an additional limitation. Therefore, this limitation cannot be interpreted under Step 2A, Prong Two. The Applicant notes “wherein the guidance reduces repeated prompting interactions between a user and the LLM service, thereby reducing computational resource usage by the LLM service”. This is an intended use language and has not been given patentable weight. The reduction of repeated prompting is something that can be benefited through the abstract idea and therefore is not considered as an improvement to the technology. The applicant in their claim language has not shown or claimed how “reduces repeated prompting interactions between a user and the LLM service, thereby reducing computational resource usage by the LLM service” is occurring and what this LLM service is and the link is not clearly claimed other than the intended result of the prior limitation. The Applicant also asserts that the present application reduces conversational churn by improved replies form the LLM system. However, such benefit can equally be realized by a human providing suggestions to prompts based on analysis of historical prompts and responses that were provided from the model and using this to enable users to provided refined prompts to a model. Thus, this conversational churn reduction is derived from the abstract idea and not per Prong 2 of Step 2A and the additional elements thereof.
With respect to the 35 USC 101 abstract rejections, the Applicant asserts on page 11:
The claims also recite significantly more than any alleged abstract idea under Step 2B of the Alice/Mayo framework. The amended claims recite a specific arrangement of technological components and operations, including capturing user interaction data from an interface with an LLM service, analyzing the interaction data across multiple users to generate usage patterns, and generating prompt-modification guidance that reduces repeated prompting interactions and computational resource consumption. These limitations amount to significantly more than merely collecting and analyzing information. Instead, the claims recite a particularized implementation directed to improving operation of LLM-based computing systems through reduced conversational churn and improved prompting efficiency. The Office Action does not identify, and Applicant is unaware of, any evidence demonstrating that the claimed combination of operations was well- understood, routine, or conventional.
The Examiner respectfully disagrees with these assertions. The Examiner notes that usage of a “interface” without providing further details on what the improvement and how the interface is laid out would not provide to significantly more under Step 2B. In fact, the claims use the wor “interface” for the “Capturing”, and “display”. The claims do not go into detail with respect to this interface. Thus, amounting to merely pre/post solution activities. Further, each of the limitations mentioned above relate to steps that can be mentally performed and are directed towards an abstract idea. Therefore, these cannot provide significantly more than the abstract idea. The OA has identified those aspects that were considered well understood, routine or conventional. This evidence only has to be provided to “additional elements”. These have been clearly noted in the prior action to be “computing device,” “processor” and “computer readable media”. The “large language model service” has not been interpreted as an additional element since the examiner has interpreted the inputs/prompts and outputs/responses are something that can be known from past usage and the claims make no use of the LLM or provide further improvements.
Hence, Applicant’s arguments are 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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claims 1, 9, 15 recite a method, an apparatus and a computer readable media, thus relating to a statutory category.
Claims 1, 9, 15 recite “observing, by an insights service, prompting associated with a large language model service, wherein: the observation is performed on a per-user basis with respect to each user in a group of observed users; and capturing, by the insights service from an [interface] with a large language model service, prompts, corresponding replies, and user interaction data associated with the replies, including at least one of dwell time, invocation of a stop-replying feature, or selection of content included in the replies; identifying, by the insights service, insights into the prompting on the per-user basis with respect to each of the group of observed users, including analyzing the prompts, the replies, and the user interaction data across the group of observed users to generate usage patterns associated with the large language model service; causing, by the insights service, display of the insights in a [user interface] associated with a reviewing user, wherein the insights are configured to enable evaluation of usage patterns of the large language model service across the group of observed users; and generating, by the insights service, guidance for modifying subsequent prompts submitted to the large language model service based on the insights, wherein the guidance reduces repeated prompting interactions between a respective user and the large language model service, thereby reducing computational resource usage by the large language model service” The limitations as drafted cover mental processes, where a human can observe any prompts that have been used with a LLM as well as responses LLM have provided and make a determination and this analysis can be done for each user who has interacted with the LLM. Then, the human making a determination of trends as to which prompts yielded better responses from the LLM vs which prompts did not. Then, the human providing recommendations and these trends on a sheet of paper to inform the user. Claim 9 is broader than claim 1 and 15 and therefore is rejected for similar reasons as in claims 1 and 15.
This judicial exception is not integrated into a practical application. In particular, independent claims 9, 15 recite additional elements of “computing device,” “processor” and “computer readable media” which amount to general purpose computing devices. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea (SPEC [0075] – Examples of processing system 602 include general purpose central processing units, graphical processing units, application specific processors, and logic devices, as well as any other type of processing device, combinations, or variations thereof). Further, the claims recite an “interface”. However, this interface is mainly being used for pre/post solution activities for capturing the data associated with the model and the output of the insights and prompt recommendations.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a processor is noted as a general computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, the additional limitations in the claims noted above are directed towards insignificant solution activity. The claims are not patent eligible.
With respect to claims 2, 11, 16, the claims recite “wherein the user interaction data is logged by an application executing on a computing device of a respective user and transmitted to the insights service for generation of the usage pattern ”. This limitation includes additional elements of “the user interaction data is logged by an application executing on a computing device of a respective user”. The Examiner notes that this is merely a data gathering step to retrieval and capture all of the interactions between the user and the LLM. This additional limitation are not sufficient to amount to significantly more than the judicial exception.
With respect to claims 3, 12, 17, the claims recite “organizing the prompting into conversations; classifying each of the conversations as belonging to one or more of a set of categories based at least on characteristics of the prompts, characteristics of the replies, and characteristics of the user actions; and identifying trends with respect to the set of categories ..” where a human can perform all of the above recited limitations. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 4, 18, the claims recite “wherein the categories comprise a subset of categories associated with prompting types .. comprising a creative category, a productivity category, a learning category, and a research category ..” where a human can categorize prompt types into different categories. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 5, 19, the claims recite “wherein the categories comprise a subset of categories associated with prompting topics .. comprising an off-task category, an on-task category, and an inappropriate content category ..” where a human can categorize prompt topics into different categories. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 6, 20, the claims recite “wherein the categories comprise a subset of categories associated with prompting quality .. comprising a high-quality category and a low-quality category ..” where a human can categorize prompt quality into different categories. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 7, 13, the claims recite “wherein the categories comprise: a first subset of categories associated with prompting types, wherein the first subset of categories comprises a creative category, a productivity category, a learning category, and a research category; a second subset of categories associated with prompting topics, wherein the second subset of categories comprises an off-task category, an on-task category, and an inappropriate content category; and a third subset of categories associated with prompting quality, wherein the third subset of categories comprises a high-quality category and a low-quality category ..” where a human can recognize all different categories of prompting types, topics and quality. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 8, 14, the claims recite “wherein: the characteristics of the prompts comprises content of the prompts; the characteristics of the replies comprises content of the replies; and the characteristics of the user actions comprises dwell time over the replies, a frequency of using a stop-replying feature with respect to the replies, and a frequency of click-throughs with respect to the content in the replies ..” where a human can recognize all different characteristics of the contents of the prompts and the replies, and various characteristics such as related to clicks associated with the user action. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 10, the claim recites “wherein the insights comprise trends identified in the prompting ..” where a human can observe the prompt/question to identify a trend. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 9-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pereira et al. (“Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts”, April 2023).
As to claim 9, Pereira teaches a computing apparatus comprising:
one or more computer readable storage media (see Figure 1-2, where an inherent storage can be evident as a result of the series of display screens present);
one or more processors operatively coupled with the one or more computer readable storage media (see Figure 1-2, where an inherent processor is evident ); and
an application comprising program instructions stored on the one or more computer readable storage media that, when executed by the one or more processors, direct the computing apparatus to at least (see Figure 1-2, where the usage of the different user interfaces indicates the usage of an application program);
communicate with an insights service to obtain, on a per-user basis with respect to each user of a group of observed users, insights into prompting associated with a large language model service, wherein the insights are generated based on prompts submitted to the large language model service, replies generated by the large language model service, and user interaction data associated with the replies (see page 6, right column, “assessing what the baseline bot is capable of” allows the logging of prompts that is provided to the bot, and see page 7, right column, “identifying errors” where documentation of errors is determined as well as successes and as a result is labelled, this assessment and determination of insights continues into “debugging”, thereby testing prompts with the chatbot and see page 8, left column, “Interview Protocol”, where different users interacted with the system in an attempt to improve the chatbot and each participant in Table 3 goes through the steps outlined in page 7);
display a view of the insights in a user interface to the application (see Figure 1-2, which shows the different states of the chatbot prompts, responses, and issues found, where aggregate stats provided); and
generate guidance for modifying subsequent prompts submitted to the large language model service based on the insights, wherein the guidance reduces repeated prompting interactions between a respective user and the large language model service, thereby reducing computational resource usage by the large language model service (see page 7, left column, “evaluating the new prompt locally”, “evaluating the new prompt globally”, and “iteration” sections describe using new prompts and testing whether the chatbot fixes prior errors and how the new prompt behaves).
As to claim 10, Pereira teaches wherein the insights comprise trends identified in the prompting based on observations by the insights service of the prompting (see page 17, where A2, notes that template change affects any identified problematic chatbot responses replaying errors and displays modified responses and see A3, where pilot users detected most of a set of 5 error categories).
As to claim 11, Pereira teaches wherein the observations comprise:
observations of prompts submitted to the large language model service see page 6, right column, “assessing what the baseline bot is capable of” allows the logging of prompts that is provided to the bot, where the bot is implemented via a LLM (see page 5, right column -page 6 left column, “BotDesigner Design”));
observations of replies to the prompts from the large language model service (see page 7, right column, “identifying errors” where documentation of errors is determined as well as successes from the responses); and
observations of user actions with respect to the replies (see page 7, right column, where labels are noted for errors as well as new prompts provided to see if it corrects the error and how the chatbot responds).
As to claim 12, Pereira teaches wherein the insights service: organizes the prompting into conversations (See Figure 1-2, where the prompting is organized as a conversation with respect to user prompts and chat bot responses); classifies each of the conversations as belonging to one or more of a set of categories based at least on: characteristics of the prompts; characteristics of the replies; and characteristics of the user actions; and identifies the trends with respect to the set of categories (see page 7, left column, “identifying errors”, where critical errors is tagged based on the combination of prompt provided to the bot, the response, and user action of labelling and further debugs and evaluates new prompts to see if the error is fixed (see “debugging” and “evaluating the new prompt locally/globally)).
As to claim 13, Pereira teaches wherein the set of categories comprises: a first subset of categories associated with prompting types, the first subset of categories comprising a creative category, a productivity category, a learning category, and a research category; a second subset of categories associated with prompting topics, the second subset of categories comprising an off-task category, an on-task category, and an inappropriate content category; and a third subset of categories associated with prompting quality, the third subset of categories comprising a high-quality category and a low-quality category (see page 7, left column, “identifying errors”, where critical errors is tagged, where critical errors is deemed to be synonymous to “low quality category” and see Figure 2, various labels assigned) . (Note: The Examiner notes that per claim 12, only one category is required as a result of the classifying).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s)1-7 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pereira (as cited above) in view of Gore (US 2024/0320424).
As to claims 1 and 15, Pereira teaches a method of operating an insights service, the method comprising:
observing, by an insights service, prompting associated with a large language model service, wherein: the observation is performed on a per-user basis with respect to each user in a group of observed users; and capturing, by the insights service from an interface with a large language model service, prompts, corresponding replies, and user interaction data associated with the replies, [[including at least one of dwell time, invocation of a stop-replying feature, or selection of content included in the replies]] (see page 6, right column, “assessing what the baseline bot is capable of” allows the logging of prompts that is provided to the bot (which uses a LLM), and see page 7, right column, “identifying errors” where documentation of errors is determined as well as successes and as a result is labelled, this assessment and determination of insights continues into “debugging”, thereby testing prompts with the chatbot and see page 8, left column, “Interview Protocol”, where different users interacted with the system in an attempt to improve the chatbot and each participant in Table 3 goes through the steps outlined in page 7);
identifying, by the insights service, insights into the prompting on the per-user basis with respect to each of the group of observed users, including analyzing the prompts, the replies, and the user interaction data across the group of observed users to generate usage patterns associated with the large language model service (see page 8, left column-page 9, sect 4, where observations made based on participants interactions and evaluation of prompt effectiveness, system behavior and responses and changes of prompts);
causing, by the insights service, display of the insights in a user interface associated with a reviewing user (see Figure 1-2, which shows the different states of the chatbot prompts, responses, and issues found, where aggregate stats provided), wherein the insights are configured to enable evaluation of usage patterns of the large language model service across the group of observed users (see page 8, left column, “Interview Protocol”, where different users interacted with the system in an attempt to improve the chatbot and each participant in Table 3 goes through the steps outlined in page 7); and
generating, by the insights service, guidance for modifying subsequent prompts submitted to the large language model service based on the insights, wherein the guidance reduces repeated prompting interactions between a respective user and the large language model service, thereby reducing computational resource usage by the large language model service (see page 7, left column, “evaluating the new prompt locally”, “evaluating the new prompt globally”, and “iteration” sections describe using new prompts and testing whether the chatbot fixes prior errors and how the new prompt behaves).
However, Pereira does not specifically teach including at least one of dwell time, invocation of a stop-replying feature, or selection of content included in the replies.
Gore teaches at least one of dwell time, invocation of a stop-replying feature, or selection of content included in the replies (see [0108], where click rate is compared to an engagement metric and the language model is aligned based on the content that generated high engagement and used as training along with the original prompt).
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed inventions to have modified the insights as taught by Pereira with the selection of content as taught by Gore in order to align a language model to content that generated high engagement (see Gore [0108]).
As to claim 15, apparatus claim 15 and method claim 1 are related as apparatus and the method of using same, with each claimed element's function corresponding to the claimed method step. Accordingly claim 15 is similarly rejected under the same rationale as applied above with respect to method claim. Furthermore, Gore teaches a non-transitory computer readable media having program instructions stored thereon for operating an insights service that when executed by one or more processors of one or more computing devices (see [0116], where medium storing instructions and executed by a processor).
As to claim 2 and 16, Pereira in view of Gore teach all of the limitations as in claim 1, above.
Furthermore, Pereira teaches wherein the user interaction data is logged by an application executing on a computing device of a respective user and transmitted to the insights service for generation of the usage pattern (see entire section 4, where results are evaluated based on interactions of the participants with the bot for analysis).
As to claim 3 and 17, Pereira in view of Gore teach all of the limitations as in claim 1, above.
Furthermore, Pereira teaches organizing, by the insights service, the prompting into conversations (See Figure 1-2, where the prompting is organized as a conversation with respect to user prompts and chat bot responses); classifying, by the insights service, each of the conversations as belonging to one or more of a set of categories based at least on characteristics of the prompts, characteristics of the replies, and characteristics of the user actions; and identifying, by the insights service, trends with respect to the set of categories (see page 7, left column, “identifying errors”, where critical errors is tagged based on the combination of prompt provided to the bot, the response, and user action of labelling and further debugs and evaluates new prompts to see if the error is fixed (see “debugging” and “evaluating the new prompt locally/globally)).
As to claims 4 and 18, Pereira in view of Gore teach all of the limitations as in claim 3, above.
Furthermore, Gore teaches wherein the categories comprise a subset of categories associated with prompting types, the subset of categories comprising a creative category, a productivity category, a learning category (see [0108], where training sample of the original language model prompt is constructed and determined based on engagement metric), and a research category. (Note: The Examiner notes that per claim 3, only one category is required as a result of the classifying).
As to claim 5 and 19, Pereira in view of Gore teach all of the limitations as in claim 3, above.
Furthermore, Pereira teaches wherein the categories comprise a subset of categories associated with prompting topics, the subset of categories comprising an off-task category, an on-task category, and an inappropriate content category (see page 17, sect. A3, 2nd para where categorization tags applied to the conversations and 3rd paragraph, where categories of skipped steps, ignored user expressions, ignored requests, and unhelpful responses and page 20 sect B1) . (Note: The Examiner notes that per claim 3, only one category is required as a result of the classifying)
As to claim 6 and 20, Pereira in view of Gore teach all of the limitations as in claim 1, above.
Furthermore, Pereira teaches wherein the categories comprise a subset of categories associated with prompting quality, the subset of categories comprising a high- quality category and a low-quality category (see page 7, left column, “identifying errors”, where critical errors is tagged, where critical errors is deemed to be synonymous to “low quality category” and see Figure 2, various labels assigned).
As to claim 7, Pereira in view of Gore teach all of the limitations as in claim 1, above.
Furthermore, Pereira teaches wherein the categories comprise: a first subset of categories associated with prompting types, wherein the first subset of categories comprises a creative category, a productivity category, a learning category, and a research category (see [0108], where training sample of the original language model prompt is constructed and determined based on engagement metric), and a research category. (Note: The Examiner notes that per claim 3, only one category is required as a result of the classifying).; a second subset of categories associated with prompting topics, wherein the second subset of categories comprises an off-task category, an on-task category, and an inappropriate content category (see page 17, sect. A3, 2nd para where categorization tags applied to the conversations and 3rd paragraph, where categories of skipped steps, ignored user expressions, ignored requests, and unhelpful responses and page 20 sect B1); and a third subset of categories associated with prompting quality, wherein the third subset of categories comprises a high-quality category and a low-quality category (see page 7, left column, “identifying errors”, where critical errors is tagged, where critical errors is deemed to be synonymous to “low quality category” and see Figure 2, various labels assigned) . (Note: The Examiner notes that per claim 12, only one category is required as a result of the classifying).
Allowable Subject Matter
Claim 8 and 14 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
None of the cited prior art of record teach the limitations as recited in claim 14. More specifically, the inclusion of “wherein: the characteristics of the prompts comprises content of the prompts; the characteristics of the replies comprises content of the replies; and the characteristics of the user actions comprises dwell time over the replies, a frequency of using a stop-replying feature with respect to the replies, and a frequency of click- throughs with respect to the content in the replies” as recited in claim 14.
Conclusion
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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/Paras D Shah/ Supervisory Patent Examiner, Art Unit 2653
08/20/2026
Prosecution Insights