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 .
Response to Amendment
The amendments filed 04/16/2026 have been accepted and considered in this office action. Claims 1, 6, 11, and 16 have been amended. Claims 5, 7, 15, and 17 have been cancelled. Claims 1-4, 6, 8-14, 16, 18-20 are pending.
Response to Arguments
Applicant’s arguments with respects to claims 1-4, 6, 8-14, 16, 18-20 have been considered but are moot in view of new grounds of rejection necessitated by the applicant’s amendments to the claims.
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 1-4, 6, 8-14, 16, 18-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a mental process without significantly more.
Independent claims 1 and 11 regard a process that, as drafted under its broadest reasonable interpretation (BRI), covers collecting, sorting, and summarizing business data. For example, under the BRI the claims relate to:
receiving a user request for a summary from a user device associated with a user (a person can mentally receive or acknowledge a request from another person for a summary); compiling data associated with the user, the data comprising a plurality of data points (a person can mentally gather records, notes, files, business information, financial information, and/or interaction history associated with a specific user from memory); itemizing the compiled data based on a plurality of predetermined categorization buckets by assigning each data point to a categorization bucket (a person can mentally sort fathered information into predefined categories, such as business data, financial data, etc.); feeding the itemized compiled data as an input to a machine learning model (a person can provide the sorted list of information to ChatGPT or to another person via word of mouth), wherein the machine learning model is trained on business information, financial information, and historical interaction information (a person can input these information types into ChatGPT for training or mentally perform the task, and either way evaluate this information to decide what information is relevant to a user), the historical interaction data comprising links clicked by the user, previous actions executed by the user, and time spent on one or more parts of a software application by the user (a person can observe or review a user’s prior clicks/actions/ time spent on pages to input into ChatGPT); analyzing, with the machine learning model, each of the plurality of data points of the itemized compiled data to determine a relevance level for each data point, the relevance level reflecting a level of relevance to the user (a person can prompt ChatGPT or mentally review each item on a list and decide how relevant or important each item is to another person/requester); ranking, with the machine learning model, the itemized compiled data based on the determined relevance levels to the user (a human can mentally rank a list of items by importance or relevance to the requestor or prompt ChatGPT to do so); generating a large language model (LLM) prompt comprising the ranked itemized compiled data and the user request for the summary (a person can mentally draft instructions or a written prompt to input into ChatGPT involving ranked list of information and request for a summary); feeding the prompt as an input to the LLM (a person can provide written prompt to another person or to ChatGPT); generating, via the LLM, a summary for each item of the ranked itemized compiled data using the user request for the summary (person can receive/read/write down output from prompt to ChatGPT or another person requesting summary for each ranked item or mentally come up with a summary for each item based on request); augmenting the summaries (a human can augment/add to generated summaries mentally); and causing at least one augmented summary to be displayed on the user device (a person can show/present/send/or display summary to another person, user device is generic computer component).
As described above, these limitations can be carried out as a series of mental steps. The judicial exception is not integrated into a practical application because the only additional elements recited are a system comprising a computer processor, memory, and LLM/ML models, which are general-purpose hardware and software tools being used as a tool to implement the mental process. The computer-executable instructions and the user device are conventional components that utilize the basic functions of a computer to automate the abstract gathering and summarizing of data.
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the only additional elements recited are the recitation of generic processors, storage devices, and AI models used to perform the mental steps of data organization. These amount to nothing more than a "computerized" version of a standard human cognitive task.
The remaining dependent claims fail to add patent-eligible subject matter to the independent claims:
Claims 2 and 12 simply add the use of APIs to obtain profit/loss, inventory, and transaction information, which a human can do mentally or by manually checking business ledgers and recording the information.
Claims 3 and 13 simply add accessing an AI model to obtain recommended actions, which is a process a human can perform mentally by observing data and determining a logical next step or recommendation.
Claims 4 and 14 simply add that the data compiled includes business, financial, and interaction information, which is a selection of data categories a human can gather and process mentally or with pen and paper.
Claims 6 and 16 simply add determining if two entries have a related topic, which is a fundamental human cognitive process of identifying relationships between pieces of information.
Claims 8 and 18 simply add that the prompt comprises a top predefined number of entries, which a human can do mentally by choosing only the top few items from a ranked list.
Claims 9 and 19 simply add augmenting the summary with a recommended action, which a human can do mentally by writing a suggestion next to a summary.
Claims 10 and 20 simply add formatting the summaries into a structured representation, which a human can do mentally or with a pen and paper (e.g., writing summaries in a table or structured list).
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.
The factual inquiries 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 6, 8-14, 16, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gardner et al. (hereinafter Gardner) (US 12008332 B1) in view of Yi et al. (hereinafter Yi) (US 20180011854 A1) further in view of Wang et al. (hereinafter Wang) (US 20120143790 A1).
Regarding claim 1, Gardner discloses:
A computing system comprising (Gardner, P(1102)):
a processor (Gardner, P(1091), Claim 16, Fig. 8); and
a non-transitory computer-readable storage device storing computer-executable instructions, the instructions operable to causing the processor to perform operations comprising (Gardner, P(1091), Claim 16, Fig. 8):
receiving a user request for a summary from a user device associated with a user (Gardner, P(0732): “Native iOS and Android apps allow power users to efficiently explore and annotate summaries anytime, anywhere.”, reads on a request initiated via a user-associated mobile device, P(428): "(428) The core API is the Summarize API which accepts a content item and desired zoom level as input parameters." (request to Gardner's summarize API is a user request for a summary. Desired zoom/abstraction level is part of summary request); ;
compiling data associated with the user, the data comprising a plurality of data points ((Gardner, P[0930]: “Doctors at an insurance company can leverage the system to summarize lengthy medical records and exam notes to compile condensed patient health histories.”, reads on gathering a specific individual's private records (data), records plural reads on plurality of data points);
itemizing the compiled data based on a plurality of predetermined categorization buckets by assigning each data point to a categorization bucket (Gardner, P(59): "Classification models can categorize the text subjects for topical filtering and abstraction. Text classifiers may be trained on labeled document corpuses to predict categories and tags." (predicted categories/tags read on predetermined categorization buckets, assigning compiled data/text subjects to those categories itemizes the data by bucket));
wherein the machine learning model is trained on business information, financial information, and historical interaction information, the historical interaction data (Gardner, P[0055]: “These models may be trained on text-summary pairs to learn to rank sentences based on importance.”; Gardner, P[0099]: “Financial Data: EDGAR filings, company profiles, macroeconomic indicators, and market data provide business context; and/or User Analytics: CRM systems, sales funnels, web analytics, and other behavioral data tailor summaries to user needs.”, reads on an ML model trained specifically using datasets of business, financial, and historical interaction data)
generating a large language model (LLM) prompt comprising the ranked itemized compiled data and the user request for the summary (Gardner, P(50): "The system may be configured to integrate with an LLM API by programmatically constructing prompts designed to produce summaries with the appropriate length, abstraction level, inferences, data integration, etc." P(51): "The prompts can include the original text to summarize along with instructions tailored to elicit the target summary characteristics from the LLM.", (discloses generating an LLM prompt using the content to summarize and request abstraction));
feeding the prompt as an input to the LLM (Gardner, P(51): "The system sends the prompts through the API and ingests the LLM-generated summaries to present to the user." (sending the prompt through the LLM API discloses feeding the prompt as input to the LLM));
generating, via the LLM, a summary for each item of the ranked itemized compiled data using the user request for the summary (Gardner, P(49): "LLMs like GPT-3 expose their generative capabilities through APIs that allow sending a text prompt and receiving back model-generated completions. ", P(51): "ingests the LLM-generated summaries to present to the user.", (discloses generating LLM summaries from prompts. In the combination, the prompt is generated from Yi's ranked content/data items, so the LLM generates summaries for the ranked items using the user's summary request));
augmenting the summaries (Gardner, P(58): "(58) Data retrieval models can find and integrate relevant external data sources. These models may leverage inverted indexes, dense retrievers like DPR, and knowledge graphs to identify contextual data. The system can be configured to used data retrieval models to provide supplementary information to augment the summaries with outside facts and context beyond the original text."); and
causing at least one augmented summary to be displayed on the user device (Gardner, P[0735]: “By providing quick access from mobile devices, native apps integrate summarization into daily workflows.”, reads on delivery and display via a mobile interface (user device)).
Gardner does not explicitly disclose:
feeding the itemized compiled data as an input to a machine learning model
analyzing, with the machine learning model, each of the plurality of data points of the itemized compiled data to determine a relevance level for each data point, the relevance level reflecting a level of relevance to the user
ranking, with the machine learning model, the itemized compiled data based on the determined relevance levels to the user
comprising links clicked by the user, previous actions executed by the user, and time spent on one or more parts of a software application by the user
However, Yi discloses:
feeding the itemized compiled data as an input to a machine learning model (Yi, P[0043]: "The user engagement based card ranking system 140 can extract different types of user engagement signals from the user activity log database 150 and combine these signals to train a ranking model" (discloses feeding user-activity/user-engagement information derived from itemized/logged data into a machine-learning ranking model)),
analyzing, with the machine learning model, each of the plurality of data points of the itemized compiled data to determine a relevance level for each data point, the relevance level reflecting a level of relevance to the user (Yi, P[0066]: "a normalized user engagement score for each type of user engagement signals.", P[0069]: "compute a final relevance score or aggregated score for each (card, query)" (discloses determining normalized engagement/relevance scores for item /query pairs which correspond to relevance levels for data points/items reflecting relevance to the user.));
ranking, with the machine learning model, the itemized compiled data based on the determined relevance levels to the user (Yi, P[0044]: "The user engagement based card ranking system 140 may use the ranking model to rank a list of content items to be presented by the card-based information guide system 130 to a user.", P[0077]: "can rank the set of cards to generate a ranked list of cards, based on the user engagement information and the context information" (discloses ranking content items/cards using the trained ranking model and user-engagement relevance information));
It would have been obvious before the effective filing date of the claimed invention to have modified Gardner in view of Yi. Doing so would combine the trained engagement-based ranking model of Yi (Yi, Abstract, P[0043], P[0066]) with the LLM summarization workflow to generate summaries through automatically engineered LLM prompts and user/business/financial context of Gardner (Gardner, Abstract, P(50), P(99)). Doing so would prioritize user-relevant data before Gardner’s prompt generation, improving personalization and reducing irrelevant LLM input.
The combination of Gardner and Yi does not explicitly disclose:
comprising links clicked by the user, previous actions executed by the user, and time spent on one or more parts of a software application by the user
However, Wang discloses:
comprising links clicked by the user, previous actions executed by the user, and time spent on one or more parts of a software application by the user (Wang, P[0022]: "The click log 150 may comprise the query 111 posed, the time at which it was posed, a number of pages shown to the user (e.g., ten pages, twenty pages, etc.) as the result set 112, and the page of the result set 112 that was clicked by the user.", "Clicks may be combined into sessions and may be used to deduce the sequence of pages clicked by a user", Abstract: "user post-click behavior that may be examined includes the amount of time that a user remains on a target page" (discloses clicked links/pages, previous post-click actions, and time spent/'dwell' time));
It would have been obvious before the effective filing date of the claimed invention to have modified Gardner in view of Yi and in further view of Wang. Doing so would combine the clicked pages, post-click behavior, and dwell time analysis for determining relevance of Wang (Wang, Abstract) with the trained engagement-based ranking model of Yi (Yi, Abstract, P[0043], P[0066]) and the LLM summarization workflow to generate summaries through automatically engineered LLM prompts and user/business/financial context of Gardner (Gardner, Abstract, P(50), P(99)). Doing so would greatly improve the relevance ranking of user-associated data before Gardner’s LLM prompt is generated.
Regarding claim 2, The combination of Gardner, Yi, and Wang discloses the computer system according to claim 1:
Gardner further discloses: The computing system of claim 1, wherein compiling the data associated with the user comprises accessing one or more application programming interfaces (APIs) to obtain one or more of profit/loss information, inventory information, and transaction information (Gardner, P[0053]: “The prompts and APIs enable seamlessly integrating LLMs into the summarization workflow.”; Gardner, P[0747]: “They reviewed the client's pain points around managing a complex multi-channel sales process and inventory with high order volumes. The rep proposed Acme OMS and CRM as solutions, highlighting capabilities like order routing, inventory linking, reporting, and customer profiles.”, reads on using APIs to access and obtain inventory and transaction data for the summarization process).
Regarding claim 3, The combination of Gardner, Yi, and Wang discloses the computer system according to claim 1:
Gardner further discloses:
The computing system of claim 1, wherein compiling the data associated with the user comprises accessing one or more artificial intelligence models to obtain one or more recommended actions for the user (Gardner, P[0734]: “For instance, a consultant could review summarized client reports and research papers during commutes to prepare recommendations.”; Gardner, P[0932]: “Negative zoom can infer likely missing details from doctor notes and incorporate medical research on conditions as supplemental context.”, reads on using AI to identify gaps and generate recommended actions for the user based on compiled data).
Regarding claim 4, The combination of Gardner, Yi, and Wang discloses the computer system according to claim 1:
Gardner further discloses:
wherein compiling the data associated with the user comprises compiling business information, financial information, and historical interaction information (Gardner, P[0099]: “Financial Data: EDGAR filings, company profiles, macroeconomic indicators, and market data provide business context; and/or User Analytics: CRM systems, sales funnels, web analytics, and other behavioral data tailor summaries to user needs.”, reads on the compilation of business, financial, and historical interaction/behavioral information).
Regarding claim 6, The combination of Gardner, Yi, and Wang discloses the computer system according to claim [[5]] 1:
Gardner further discloses:
wherein itemizing the compiled data based on the plurality of predetermined buckets comprises determining that two or more entries have a related topic (Gardner, P[0055]: “Extractive summarization models can identify and extract the most salient sentences or phrases from the original text to produce abridged summaries. These models may use encoder-decoder architectures, converting sentences to vector representations and scoring for relevance.”, reads on using vector proximity to determine that entries have a related topic or theme).
Regarding claim 8, The combination of Gardner, Yi, and Wang discloses the computer system according to claim 1:
Gardner further discloses:
wherein generating the LLM prompt comprises a top predefined number of entries from the ranked itemized compiled data (Gardner, P[0055]: “The system can be configured to use extractive summarization models to, for example, concatenate extracted snippets into summaries meeting length constraints.”, reads on selecting the "top" or most salient entries from the ranked set to fit within a specific length-limited prompt).
Regarding claim 9, The combination of Gardner, Yi, and Wang discloses the computer system according to claim 1:
Gardner further discloses:
wherein augmenting the summaries comprises adding one or more recommended actions associated with the item (Gardner, P[0734]: “For instance, a consultant could review summarized client reports and research papers during commutes to prepare recommendations.”; Gardner, P[0932]: “Negative zoom can infer likely missing details from doctor notes and incorporate medical research on conditions as supplemental context.”, reads on the system adding specific recommendations or inferences to the summary as an augmentation step).
Regarding claim 10, The combination of Gardner, Yi, and Wang discloses the computer system according to claim 1:
Gardner further discloses:
wherein generating, via the LLM, the summary for each item of the ranked itemized compiled data comprises formatting the generated summaries into a structured representation (Gardner, P[0053]: “The system may be configured to leverage multiple LLMs with different capabilities and may use orchestration mechanisms to combine outputs.”; Gardner, P[0931]: “Positive zoom extracts critical information like past conditions, surgeries, medications, test results, and diagnoses.”; Gardner, P[0933]: “This helps streamline compiling health backgrounds to facilitate claims processing and auditing.”, reads on the LLM generating and orchestrating content into a "structured representation" by organizing extracted data into a standardized patient health background/history format).
Regarding claim 11, claim 11 recites the method corresponding to the system described in claim 1 and is rejected under the same grounds as stated above.
Additionally, Gardner further discloses: A computer-implemented method, performed by at least one processor, comprising: a processor (Gardner, Claim 16, Fig 8.);
Regarding claim 12, claim 12 recites the method corresponding to the system described in claim 2 and is rejected under the same grounds as stated above.
Regarding claim 13, claim 13 recites the method corresponding to the system described in claim 3 and is rejected under the same grounds as stated above.
Regarding claim 14, claim 14 recites the method corresponding to the system described in claim 4 and is rejected under the same grounds as stated above.
Regarding claim 16, claim 16 recites the method corresponding to the system described in claim 6 and is rejected under the same grounds as stated above.
Regarding claim 18, claim 18 recites the method corresponding to the system described in claim 8 and is rejected under the same grounds as stated above.
Regarding claim 19, claim 19 recites the method corresponding to the system described in claim 9 and is rejected under the same grounds as stated above.
Regarding claim 20, claim 20 recites the method corresponding to the system described in claim 10 and is rejected under the same grounds as stated above.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHASHIDHAR S MANOHARAN whose telephone number is (571)272-6772. The examiner can normally be reached M-F 8:00-4:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Flanders can be reached at 571-272-7516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHASHIDHAR SHANKAR MANOHARAN/Examiner, Art Unit 2655
/ANDREW C FLANDERS/Supervisory Patent Examiner, Art Unit 2655
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