DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This Office action is in response to Applicant’s communication filed on October 17, 2025. Claims 1-24 are pending and have been examined. The objections to the drawings, rejections and a statement of reasons for the indication of allowable subject matter over prior art are stated below.
Drawings
2. The drawings filed by the applicants on October 17, 2025 are objected to by the Examiner. Specifically, drawings of Figures 2-10 are not clear. Formal replacement legible drawings are required in the response to this Office action. Note: Applicant may not request that any objection to the drawing(s) be held in abeyance. See 37 CFR 1.85(a).
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-24 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims recite a system and method for insurance underwriting, which is considered a judicial exception because it falls under the category of “Certain Methods of organizing human activity” such as fundamental economic practice as well as commercial or legal interactions including agreements as discussed below. This judicial exception is not integrated into a practical application as discussed below. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below.
Analysis
Step 1: In the instant case, exemplary claim 1 is directed to a system (apparatus).
Step 2A – Prong One: The limitations of “A generative artificial intelligence (AI) system for insurance underwriting, comprising:
a processor in communication with a plurality of data sources;
an extraction engine executed by the processor, the extraction engine obtaining insurance underwriting submission data in disparate formats and generating a submission object from the insurance underwriting submission data, the submission object comprising a unified data structure for processing the disparate formats of the insurance underwriting submission data;
a confidence scoring module executed by the processor, the confidence scoring module processing output of the extraction engine and generating an initial confidence score based on data extracted by the extraction engine;
a prefill engine executed by the processor, the prefill engine automatically pre-filling the insurance underwriting submission data with insurance analytics data;
a validation engine executed by the processor, the validation engine validating the insurance underwriting submission data and identifying discrepancies between the insurance underwriting submission data and the insurance analytics data;
a completeness scoring engine executed by the processor, the completeness scoring engine calculating a similarity score between structured data and the insurance analytics data;
an accuracy scoring engine executed by the processor, the accuracy scoring engine calculating a final score indicating an overall accuracy of the insurance underwriting submission data; and
an underwriter assistant software application executed by the processor, the underwriter assistant software application allowing access to the insurance underwriting submission data, the similarity score, and the final score, the underwriting assistant software application generating a generative artificial intelligence chat panel, the generative artificial intelligence chat panel in communication with a plurality of large language models (LLMs) and allowing a user of the underwriter assistant software application to engage in a chat for guiding analysis of the insurance underwriting submission data” such as fundamental economic practice as well as commercial or legal interactions including agreements.
A system for insurance underwriting is a fundamental economic practice such as insurance.
Also, the steps of the claim considered collectively, as an ordered combination, is fulfilling agreements in the form of processing insurance underwriting data between the insurance company and the insured. (See specification [0003] – [0007]. Hence, the steps of the claim, considered collectively as an ordered combination without the italicized portions, covers the abstract category of “Certain Methods of organizing human activity”.
That is, other than, a generative artificial intelligence (AI) system, a processor in communication with a plurality of data sources, an extraction engine, a confidence scoring module, a prefill engine, a validation engine, a completeness scoring engine, an accuracy scoring engine, an underwriter assistant software application, a generative artificial intelligence chat panel and a plurality of large language models (LLMs), nothing in the claim precludes the steps from being performed as a method of organizing human activity. If the claim limitations, under the broadest reasonable interpretation, covers methods of organizing human activity but for the recitation of generic computer components, then it falls within the “Certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of a processor in communication with a plurality of data sources, an extraction engine, a confidence scoring module, a prefill engine, a validation engine, a completeness scoring engine, an accuracy scoring engine, an underwriter assistant software application, a generative artificial intelligence chat panel and a plurality of large language models (LLMs) to perform all the steps. A plain reading of Figures 1-6 and descriptions in the associated paragraphs of the Specification reveals that the processor may be a generic processor suitably programmed to execute the claimed steps. The extraction engine, the confidence scoring module, the prefill engine, the validation engine, the completeness scoring engine, the accuracy scoring engine, the underwriter assistant software application, and the generative artificial intelligence chat panel are broadly interpreted to include generic software suitably programmed to perform the corresponding functions. The plurality of data sources are broadly interpreted to include generic data sources suitably programmed to store the corresponding data. Similarly the plurality of large language models (LLMs) are broadly interpreted to include combinations of generic software/hardware suitably programmed to perform the corresponding functions. Hence, the additional elements in the claims are all generic components suitably programmed to perform their respective functions. The additional elements in all the steps are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Hence, claim 1 is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements (identified above) to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer component. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, independent claim 1 is not patent eligible. Independent claim 13 is also not patent eligible based on similar reasoning and rationale.
Dependent claims 2-12, and 14-24, when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations only refine the abstract idea further.
For instance, in claims 2-3 and 14-15, the steps
“wherein the insurance underwriting submission data is obtained by the system from the plurality of data sources or from a user in communication with the system” and
“wherein the insurance underwriting submission data comprises at least one of unstructured text, comma-separated value (CSV) data, or portable document format (PDF) data” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the insurance underwriting submission data used in the intermediate steps of the underlying process.
In claims 4-5 and 16-17, the steps
“wherein the extraction engine identifies missing data or gaps in required data from the insurance underwriting submission data” and
“wherein the extraction engine scores accuracy of the insurance underwriting submission data” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the functions of the extraction engine used in the intermediate steps of the underlying process.
In claims 6 and 18, the steps “wherein the confidence scoring module identifies file type and document types from the insurance underwriting submission data and assigns each field of the insurance underwriting submission data a pred-determined confidence score” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the functions of the confidence scoring module used in the intermediate steps of the underlying process.
In claims 7 and 19, the steps “wherein the completeness scoring engine accesses a scoring factors database” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the functions of the completeness scoring engine used in the intermediate steps of the underlying process.
In claims 8, and 20, the steps “wherein the submission object further comprises a plurality of fields including a submission document source field identifying a source of a document, a group field indicating a component to which a field belongs, a field name, a Boolean field indicating whether a question is required for the insurance underwriting submission data, and a comments field” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the submission object used in the intermediate steps of the underlying process.
In claims 9 and 21, the steps “wherein the extraction engine compares the submission object to a plurality of data stores to determine accuracy and completeness of the submission object” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In claims 10 and 22, the steps “wherein the underwriter assistant software application displays a main analytics screen allowing the user to generate a submission for analysis, monitor a status of a submission already submitted to the system, and view current analytics relating to a submission” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process. The additional element of a main analytics screen is broadly interpreted to correspond to generic software suitably programmed to perform the associated function. The additional element of the main analytics screen, performs a traditional function recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components.
In claims 11 and 23, the steps “wherein the main analytics screen displays average loss ratios, sources of losses, and commercial statistical plan percentages” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe contents of the main analytics screen displays used in the intermediate steps of the underlying process.
In claims 12 and 24, the steps “wherein the underwriter assistant software application displays a submission analytics screen summarizing information about an insurance submission, missing data fields identified by the system in the submission, total completed data fields, and total number of data fields” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the submission analytics screen used in the intermediate steps of the underlying process. The additional element of a submission analytics screen is broadly interpreted to correspond to generic software suitably programmed to perform the associated function. The additional element of the submission analytics screen, performs a traditional function recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components.
In all the dependent claims, the judicial exception is not integrated into a practical application because the limitations are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. Also, the claims do not affect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer system itself; the claims do not affect a transformation or reduction of a particular article to a different state or thing; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment. In addition, the dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. The claims as a whole, do not amount to significantly more than the abstract idea itself. For these reasons, the dependent claims also are not patent eligible.
Claim Rejections - 35 USC § 103
5. 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.
6. 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.
7. 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 of this title, 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.
8. Claims 1-24 are rejected under 35 U.S.C. 103 as being unpatentable over Jonathan J Thompson et al. (GB 2628918-A) in view of Fields Brian Mark et al. (US Pub. 2024/0281888 A1) and further in view of Molakantalla Amaranatha Reddy et al. (US Pub. 2022/0179835 A1).
Claims 1, and 13, Jonathan J Thompson et al. teaches a generative artificial intelligence (AI) system and method for insurance underwriting (See para. 0044, 0315-0318 and figure 1, system 100, comprising a generative Al component in the modeler of the response system 130; para. 0167, system is used for insurance underwriting), comprising:
a processor in communication with a plurality of data sources (See para. 0068 and fig. 1, processor of the response system 100 is in communication with the data sources 160);
an extraction engine executed by the processor, the extraction engine obtaining insurance underwriting submission data in disparate formats and generating a submission object from the insurance underwriting submission data (See para. 0174-0175, extraction of data from accepted files in various formats, such as docx, CSV, pdf; para. 0173, extracted data is used to populate fields of a data object), the submission object comprising a unified data structure for processing the disparate formats of the insurance underwriting submission data (See para. 0173, mapping of data in various formats to a data object implies that the data object comprises a unified data structure);
a prefill engine executed by the processor, the prefill engine automatically pre-filling the insurance underwriting submission data with insurance analytics data (See para. 0260-0262, fields of the data object may be filled with insurance analytics data, such as a calculated insurance readiness score);
a validation engine executed by the processor, the validation engine validating
the insurance underwriting submission data and identifying discrepancies between the insurance underwriting submission data and the insurance analytics data (See para. 0262, cross-checking of the submission data with other sources applying validation rules); and
an accuracy scoring engine executed by the processor, the accuracy scoring
engine calculating a final score indicating an overall accuracy of the insurance
underwriting submission data (See para. 0181, verification of the accuracy of the uploaded information).
Thompson et al. does not explicitly teach the features of
a confidence scoring module executed by the processor, the confidence scoring module processing output of the extraction engine and generating an initial confidence score based on data extracted by the extraction engine;
a completeness scoring engine executed by the processor, the completeness scoring engine calculating a similarity score between structured data and the insurance analytics data; and
an underwriter assistant software application executed by the processor, the underwriter assistant software application allowing access to the insurance underwriting submission data, the similarity score, and the final score, the underwriting assistant software application generating a generative artificial intelligence chat panel, the generative artificial intelligence chat panel in communication with a plurality of large language models (LLMs) and allowing a user of the underwriter assistant software application to engage in a chat for guiding analysis of the insurance underwriting submission data.
However, Fields Brian Mark et al. teaches a confidence scoring module executed by the processor, the confidence scoring module processing output of the extraction engine and generating an initial confidence score based on data extracted by the extraction engine (See para. 0181); and
an underwriter assistant software application executed by the processor, the underwriter assistant software application allowing access to the insurance underwriting submission data, the similarity score, and the final score, the underwriting assistant software application generating a generative artificial intelligence chat panel, the generative artificial intelligence chat panel in communication with a plurality of large language models (LLMs) and allowing a user of the underwriter assistant software application to engage in a chat for guiding analysis of the insurance underwriting submission data (See para. 0017, 0266-0269 and fig. 13).
One of ordinary skill in the art would have included the features taught by Fields Brian Mark et al. to the invention of Thompson et al. The motivation to combine is that these features would have provided additional assistance to the insurance underwriting personnel.
Thompson et al. does not explicitly teach the features of a confidence scoring module executed by the processor, the confidence scoring module processing output of the extraction engine and generating an initial confidence score based on data extracted by the extraction engine.
However, Molakantalla Amaranatha Reddy et al. teaches the feature of a confidence scoring module processing output of the extraction engine and generating an initial confidence score based on data extracted by the extraction engine (See para. 0004 and 0026).
One of ordinary skill in the art would have included the features taught by Reddy et al. to the invention of Thompson et al. The motivation to combine is that these features would have provided improved data quality control.
Claims 2 and 14, Thompson et al. teaches the features wherein the insurance underwriting submission data is obtained by the system from the plurality of data sources or from a user in communication with the system (See Paragraphs 0068 and 0174)
Claims 3 and 15, Thompson et al. teaches the features wherein the insurance underwriting submission data comprises at least one of unstructured text, comma-separated value (CSV) data, or portable document format (PDF) data (See Paragraph 0174)
Claims 4, and 16, Thompson et al. teaches the features wherein the extraction engine identifies missing data or gaps in required data from the insurance underwriting submission data (See Paragraphs 0168 and 0175)
Claims 8 and 20, Thompson et al. teaches the features wherein the submission object further comprises a plurality of fields including a submission document source field identifying a source of a document, a group field indicating a component to which a field belongs, a field name, a Boolean field indicating whether a question is required for the insurance underwriting submission data, and a comments field (See Paragraph 0262).
Claims 9 and 21, Thompson et al. teaches the features wherein the extraction engine compares the submission object to a plurality of data stores to determine accuracy and completeness of the submission object (See Paragraph 0262; cross checking other sources).
Claims 5, and 17, Fields Brian Mark et al. teaches the features wherein the extraction engine scores accuracy of the insurance underwriting submission data (See Paragraph 0223).
Claims 6, and 18, Fields Brian Mark et al. teaches the features wherein the confidence scoring module identifies file type and document types from the insurance underwriting submission data and assigns each field of the insurance underwriting submission data a pred-determined confidence score (See Paragraph 0223).
Claims 7 and 19, Reddy et al. teaches the features wherein the completeness scoring engine accesses a scoring factors database (See Paragraphs 0004 and 0026).
Claims 10 and 22. Fields Brian Mark et al. teaches the features wherein the underwriter assistant software application displays a main analytics screen allowing the user to generate a submission for analysis, monitor a status of a submission already submitted to the system, and view current analytics relating to a submission (See Paragraphs 0110 and 0132).
Claims 11 and 23, Fields Brian Mark et al. teaches the features wherein the main analytics screen displays average loss ratios, sources of losses, and commercial statistical plan percentages (Implied in Paragraphs 0110 and 0132 - the display of non-technical, business-related data such as average loss ratios or commercial statistical plan percentages).
Claims 12 and 24, Fields Brian Mark et al. teaches the features wherein the underwriter assistant software application displays a submission analytics screen summarizing information about an insurance submission, missing data fields identified by the system in the submission, total completed data fields, and total number of data fields (See Paragraph 0167).
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(a) Isert; Carsten et al. (US Pub. 2026/0161677 A1) discloses implementations for selectively using retrieval augmented generation (RAG) for generative model prompting. In various implementations, a generative model query of a user may be analyzed to determine whether retrieval augmented generation (RAG) should be used to generate a response. If RAG should be used, a generative model input prompt may be formed with data indicative of the generative model query and data indicative of: (i) user-specific conditioning data (USCD) associated with the user, or (ii) personal RAG data of the user. The user-specific conditioning data may have been built over time based at least in part on the personal RAG data of the user. The prompt may be processed using generative model(s) to generate generative model output, conditioned on one or both of the USCD or the personal RAG data of the user, that includes a response to the generative model query.
(b) Mobasseri; Ramin et al. (US Pub. 2026/0154299 A1) discloses techniques that provide self-service functionality for data analytics with built-in support guidance. The techniques include a collective intelligence system coupled with a centralized analytics support and enablement (CASE) system. The collective intelligence system is configured to build an indexed database based on documentation from disparate internal and external data sources. The CASE system provides a self-service function that includes a user interface as a search function and a chatbot, each configured to receive user queries regarding at least one of data or data analytics and return answers or results in response to the user queries based on the indexed database. Based on an inability of the chatbot and/or the search function to return an answer or result in response to a user query, the CASE system is configured to route the user query to a selected agent for either asynchronous or synchronous support for the user query.
10. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Narayanswamy Subramanian whose telephone number is (571) 272-6751. The examiner can normally be reached Monday-Friday from 9:00 AM to 5:00 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Abhishek Vyas can be reached at (571) 270-1836. The fax number for Formal or Official faxes and Draft to the Patent Office is (571) 273-8300.
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/Narayanswamy Subramanian/
Primary Examiner
Art Unit 3691
August 6, 2026
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