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 .
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 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 for a summary); compiling data associated with the user (a person can mentally or manually gather records associated with a specific person); itemizing the compiled data (a person can mentally sort gathered records into a list); feeding the itemized compiled data to a machine learning model and ranking it based on relevance (a person can mentally evaluate a list of items and rank them by importance or relevance to the requester); generating a large language model (LLM) prompt and generating a summary for each item (a person can mentally draft a prompt or instruction to themselves and write a brief summary for each item on a list); augmenting the summaries (a person can mentally add extra details or recommended actions to a summary); and causing the summary to be displayed (a person can show or present a written summary to another).
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 5 and 15 simply add itemizing data based on a plurality of predetermined buckets, which is a classification task a human can perform mentally (e.g., sorting papers into different folders).
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 7 and 17 simply add feeding data to a model trained on business and interaction information, which merely limits the mental process to a specific field 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 § 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Gardner et al. (US 12008332 B1) (hereinafter Gardner) (Note: Text version attached contains paragraph numbers for applicant’s convenience)
Regarding claim 1, Gardner teaches: A computing system comprising:
a processor; 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[1182]-P[1183], Claim 16, Fig. 8):
receiving a user request (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);
compiling data associated with the user (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);
itemizing the compiled data (Gardner, P[0931]: “Positive zoom extracts critical information like past conditions, surgeries, medications, test results, and diagnoses.”, reads on the structural breakdown into discrete items);
feeding the itemized data to a machine learning model (Gardner, P[0055]: “These models may use encoder-decoder architectures, converting sentences to vector representations and scoring for relevance.”, reads on inputting text items into an ML architecture);
ranking the data based on relevance (Gardner, P[0055]: “These models may be trained on text-summary pairs to learn to rank sentences based on importance.”, reads on a model assigning a rank based on importance);
generating an LLM prompt (Gardner, P[0053]: “The prompts and APIs enable seamlessly integrating LLMs into the summarization workflow.”, reads on constructing a set of instructions that feed the prioritized data into an LLM);
generating a summary for each item and augmenting it (Gardner, P[0931]: “Positive zoom extracts critical information like past conditions, surgeries, medications, test results, and diagnoses.”; Gardner, P[0932]: “Negative zoom can infer likely missing details from doctor notes and incorporate medical research on conditions as supplemental context.”; Gardner, P[0017]: “In example embodiments, negative zoom scales cause the system to make inferences and augment the summary with additional content.”, reads on the system first generating condensed representations/summaries of individual medical items and subsequently enhancing those specific items with inferred details and external scientific research).
and causing the summary to be displayed (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).
Regarding claim 2, Gardner discloses the computer system according to claim 1:
Gardner also teaches: 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, Gardner discloses the computer system according to claim 1:
Gardner also teaches:
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, Gardner discloses the computer system according to claim 1:
Gardner also teaches:
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 5, Gardner discloses the computer system according to claim 1:
Gardner also teaches:
wherein itemizing the compiled data comprises itemizing the compiled data based on a plurality of predetermined buckets (Gardner, P[0931]: “Positive zoom extracts critical information like past conditions, surgeries, medications, test results, and diagnoses.”, reads on using specific, predefined categories or "buckets" to itemize the user's data).
Regarding claim 6, Gardner discloses the computer system according to claim 5:
Gardner also teaches:
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 7, Gardner discloses the computer system according to claim 1:
Gardner also teaches:
wherein feeding the itemized compiled data to the machine learning model comprises feeding the itemized compiled data to a machine learning model trained on business information, financial information, and historical interaction information (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).
Regarding claim 8, Gardner discloses the computer system according to claim 1:
Gardner also teaches:
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, Gardner discloses the computer system according to claim 1:
Gardner also teaches:
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, Gardner discloses the computer system according to claim 1:
Gardner also teaches:
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 teaches: A computer-implemented method, performed by at least one processor, comprising: a processor (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 15, claim 15 recites the method corresponding to the system described in claim 5 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 17, claim 17 recites the method corresponding to the system described in claim 7 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
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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/SHASHIDHAR SHANKAR MANOHARAN/Examiner, Art Unit 2655
/ANDREW C FLANDERS/Supervisory Patent Examiner, Art Unit 2655