3DETAILED 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 .
Status of Claims
This action is in reply to the application filed on 22 October 2024.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 22 October 2024 was filed on the mailing date of the initial disclosure. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Independent Claims 1, 10 and 19 recite limitations for summarizing information and generating an accurate and contextually relevant response based on data and information. These limitations, as drafted, illustrate a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind. Summarizing information and generating a response illustrate high level observation and determination/evaluation type functions that could be done the same way mentally or manually. A person could summarize information and determine or generate a response the same way in their mind or manually with paper and pan. The mere nominal recitation of a generic computer component or computer system environment does not take the claim limitations out of the mental processes grouping. Thus, the claims recite a mental process, which is an abstract idea.
This judicial exception is not integrated into a practical application. The claims recite additional elements including a computer processor for receiving, a data extractor for extracting data using deep learning, and NLP, and transmitting a response to a user device, as well as the extractor using learning and NLP t and a response generator of the system that are configured to execute the summarizing and generating steps. The receiving, extracting and transmitting are recited at a high level of generality and amount to mere data gathering and transmission, which are forms of insignificant extra solution activity. The extractor using deep learning and NLP and response generator that perform the summarizing and generating are also recited at a high level of generality and merely automate those steps. Each of the additional components is no more than mere instructions to apply the exception using a generic computer component. The combination of these additional elements is no more than mere instructions to apply the exception in a generic computer environment with generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to step 2A Prong 2, the additional elements in the claims amount to no more than mere instructions to apply the exception using a generic computer component or linking the steps to a generic computer environment. The same analysis applies here in 2B and does not provide an inventive concept.
For the receiving, extracting and transmitting steps that were considered extra solution activity in step 2A above, these have been re-evaluated in step 2B and determined to be well-understood, routine and conventional activity in the field. The specification does not provide any indication that the system components are anything other than generic, off the shelf computer components, and the Symantec, TLI and OIP Techs. court decisions in MPEP 2106.05 indicate that the mere collection, receipt or transmission of data over a network is a well-understood, routine and conventional function when it is claimed in a merely generic manner, as it is here.
Dependent claims 2-9, 11-18 and 20 include all of the limitations of claims 1, 10 and 19 and therefore recite the same abstract idea. The claims merely narrow the recited abstract idea by describing additional observation and evaluation steps including describing generated responses as consisting of guidance, open questions and summaries, describing evaluation strategies as including preferences involving feedback and comparing outputs to similar trust texts, that outputs indicate a preference, and describing roles in underwriting. The additional elements recited describe further capturing, retrieval, storage, extraction, program aided LLMs (insignificant extra solution activity for data gathering and transmission) and engines, generators, programs that merely apply the exceptions. These elements fail to transform the claims into a patent eligible invention. When reconsidered the elements do not amount to significantly more for the same reasons and rationale set forth above. The elements do not integrate the abstract idea into a practical application nor do they amount to significantly more.
Accordingly, claims 1-20 are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Vasylyev (US 2024/0412720) in view of Whitehead (US 2024/0330817).
As per Claim 1 Vasylyev teaches:
computer server of an enterprise, comprising:
(a) a data store that contains electronic records associated with a plurality of relationships between the enterprise and parties, and, for each relationship, a relationship identifier, a party identifier, and at least one relationship parameter (Vasylyev [0009, 0011-0015, 0034, 0050, 0062, 0071, 0088-0090, 0095, 0191, 0201, 0258-0269] describe data storage containing records associated with entities, relationships, identifiers, and parameters) ; and
(b) the back-end application computer server, coupled to the risk relationship data store, including: a computer processor, and a computer memory coupled to the computer processor and storing instructions that, when executed by the computer processor, cause the back-end application computer server to (Vasylyev in at least [0091-0095] describes processors having various architectures including server computers and coupled memory storing executable instructions):
extract and summarize information, by a data extractor using deep learning and natural language processing from multiple data sources, including knowledge graphs, websites, and historical loss reports (Vasylyev in at least [0012, 0146, 0212, 0268-0269 0643, 0695] describes extracting and gathering data from knowledge bases and graphs using different learning models and natural language processing and describes using web scraping techniques to extract information from websites, [0125-0126, 0144, 0242, 0307] describes how the assistant system can employ summarization and compression techniques history data and other),
generate, by a response generator, an accurate and contextually relevant response based on the extracted data and other information in the data store, and transmit the relevant response to the user device (Vasylyev in at least the Abstract and [0005, 0007, 0009, 0011-0014, 0022, 0033, 0034-0035, 0037, 0046, 0060-0062, 0102-0103] describes generating accurate and contextual responses based on extracted data and other stored information and transmitting or outputting the generated response to a user device).
Vasylyev does not explicitly recite that the stored, identifier and parameter data are for risk relationships, requesting a risk relationship analysis or using the risk relationship to help generate a response. However, Whitehead teaches providing risk relationship tools for an enterprise. Whitehead further describes
A risk relationship analysis system implemented via a back-end application computer server of an enterprise (Whitehead [0005] a back end application computer server directed to a tracking tool for risk relationships), comprising:
(a) a risk relationship data store that contains electronic records associated with a plurality of risk relationships between the enterprise and parties, and, for each risk relationship, a risk relationship identifier, a party identifier, and at least one risk relationship parameter (Whitehead in at least the Abstract and [0005-0006, 0036, 0039, 0041, 0043, 0053, 0057-0058] describe data stores storing electronic records associated with risk relationships associated with interactions, accounts, entities, and issue categories that can all be identified with designated identifiers and parameters, see also Figs. 2, 21, 23-24); and
receive a risk relationship analysis request from a user device, generate, by a response generator, an accurate and contextually relevant response based on the extracted data and information in the risk relationship data store (Whitehead in at least [0041-0043] describes the ability to make selections and in response retrieve interaction parameters rom an entity interaction data store and risk relationship parameters from a risk relationship data store that are provided to an enterprise predictive model that outputs an interaction insight result, e.g. a request for analysis is received and a response is generated based on extracted and risk relationship data, see also [0046, 0049-0050, 0052-0053, 0057-0058, 0062] and Figs. 3, 19 and 21-24)
Therefore, it would be obvious to one of ordinary skill in the art to modify the analysis system that generates accurate and contextually relevant responses to include techniques for utilizing stored risk relationship data in response to a request to generate a response because each of the elements were known, but not necessarily combined as claimed. The technical ability existed to combine the elements as claimed and the result of the combination is predictable because each of the elements performs the same function as they did individually. By utilizing stored risk relationship data, upon receiving a request for analysis, the system can generate a response improving the speed security and accuracy of interaction tracking tools and electronic record analysis for an enterprise while customizing and executing interaction insights, aggregating data from multiple sources and automatically optimizing interaction information to reduce unnecessary communications (Whitehead [0034]).
As per Claim 2 Vasylyev further teaches:
further comprising one or more data extractors that include:
mechanisms for capturing enterprise data from data sources, translating it into embeddings, and storing it in a vector database (Vasylyev in at least [0037, 0069, 0143-0146, 0206, 0254-0257, 0622, 0657] describe the ability to gather and interpret data from various sources as well as the ability to convert data into high dimensional vectors using the model’s learned embedding layer),
a semantic cache reasoning service for retrieval of repetitive information (Vasylyev [0057, 0063, 0071, 0074, 0089, 0103, 0146, 0172-0173, 0205-0206, 0209-0211, 0241, 0266-0269, 0275-0277, 0385, 0604, 0644] describe utilizing cached memory, reasoning capabilities to and the assistant system to automate repetitive and rule based tasks as well as to retrieve redundant and duplicate information) , and
integration of various databases and data storage solutions for optimized performance (Vasylyev in at least [0067, 0168, 0210] describes the architecture of the LLM that may employe mechanisms to connect data storage including memory through networks as well as training LLMs and AI models using optimization algorithms, see also [0012, 0034, 0050, 0067, 0082, 0095, 0102, 0342, 0428-0430]).
As per Claim 3 Vasylyev further teaches:
further comprising: a plurality of response generators, including:
a centralized orchestration component with an intent classifier and a centralized flow controller to manage information flow, KGQuest for transforming knowledge graph queries using large language models, TreeBERT for navigating hierarchical document structures to extract precise information, DocuProbe for generating synthetic questions and extracting document-centric content, DimenRAG for multi-dimensional enhanced data retrieval, an abstractive summarizer for creating summaries based on specific templates, a multi-stage LLM preference for verifying the accuracy of generative responses, a stage-level human preference for continuous feedback to the intent classifier (Vasylyev [0174, 0177, 0460] describe classification models and intent recognition models to classify queries into predefined intent categories, [0654, 0631, 0592, 0543, 0429, 0396, 0318, 0144, 0137, 0073, 0049] describe control signals that initiate and control processes and coordinate the flow of data and results, [0296] describes the ability to integrate external knowledge graphs and ontologies to enrich understanding of conversational context using language modeling, [0083, 0572-0573] describe the ability to utilize an indexing mechanism to map keys and their storage locations in memory hierarchy, the multi-level tree structure has corresponding levels and storage and layers of granularity of access, [0298, 0401, 0448, 0650] describe the ability to recognize questions and based on the context and its content provide responses, [0125-0126] describes summarization and compression techniques, [0012, 0043, 0054-0057, 0064, 0067, 00086, 0091, 0092, 0101-0103, 0107-0108, 0157, 0169-0170, 0175, 0178-0179, 0206, 0212-0213] describe user preferences and model preference and the ability to score and evaluate interactions with feedback to the generated responses and other results).
As per Claim 4 Vasylyev further teaches:
a system for a specialized Neuro-Symbolic Large Language Model (“NS-LLM”) agents that: take input from the data extractor, including key questions and information from each data source, and interact with a neuro-symbolic reasoning engine to generate responses consisting of guidance, open questions, and summaries (Vasylyev in at least [0011-0013, 0015, 0033, 0072, 0102, 0106, 0134, 0173, 0179, 0199, 0206, 0209, 0233, 0254, 0260, 0262, 0264-0267, 0282, 0287, 0298, 0388, 0673, 0728, 0733] describe a system for large language modelling that takes extract data as inputs from a plurality of sources and interacts with tool to generate responses that provide guidance, answer questions, provide summaries and/or recommendations).
As per Claim 5 Vasylyev further teaches:
wherein evaluation strategies for a Conversational Generative Artificial Intelligence Driven Underwriting Assistant (“CG-AIUA”) include:
a stage-level human preference involving continuous feedback from human experts at every stage of AN output generation process to ensure fluidity, coherence, and domain appropriateness, and a multi-stage Large Language Model (“LLM”) preference employing advanced large language models to compare the output with similar ground truth texts in real-time for accuracy and relevance (Vasylyev [0012, 0040, 0043, 0054-0057, 0064, 0067, 00086, 0091, 0092, 0101-0103, 0107-0108, 0157, 0169-0170, 0175, 0178-0179, 0206, 0212-0213] describe user preferences involving feedback and model preferences including evaluating data for accuracy and contextual relevance).
Vasylyev describes an assistant used for evaluation strategies but does not explicitly recite that the AI is utilized for underwriting. However, Whitehead further teaches in at least [0052] the ability to utilize the system in combination with underwriting platforms. Whitehead is combined based on the reasons and rationale set forth in the rejection of Claim 1 above.
As per Claim 6 Vasylyev further teaches:
wherein an evaluation of the system’s output indicates a preference by both human evaluators and LLM agents over human-written references (Vasylyev [0012, 0040, 0043, 0054-0057, 0064, 0067, 00086, 0091, 0092, 0101-0103, 0107-0108, 0157, 0169-0170, 0175, 0178-0179, 0206, 0212-0213] describe the system’s ability to output an indication of preferences of users involving feedback and model preferences including evaluating data for accuracy and contextual relevance).
As per Claim 7 Vasylyev describes how the AI assistant system’s actions and outputs can be obtained through simulated environments. Vasylyev does not explicitly recite Whitehead further teaches:
a diverse array of roles within the CG-AIUA to simulate an underwriting process (Whitehead in at least [0073] describes the system as providing output that is indicative of, as determined by a trained predictive model, particular custom interaction scores, rules or decisions, the output may be generate din response to applying data for a current simulation to the trained predictive model component), including:
senior underwriters overseeing content production process and ensuring alignment with company objectives, analysts managing editorial workflow, editing content, and assisting in content planning, translators converting material from one language to another while maintaining an original text’s tone, style, and context, vertical industry specialists adapting content for specific verticals, regions, or markets to ensure relevance, proofreaders performing final checks for grammar, spelling, punctuation, and formatting errors, and evaluators assessing a quality of the underwriting and determining a need for further revisions based on risk formulas and other criteria (Whitehead in at least [0052] describes the ability of the system to communicate with underwriting platforms, we-based tools, administrators, insurance agents and/or other communication devices, [0041, 0043, 0045, 0048, 0066] describe an analyst role, [0044] describes capabilities to allow users to select an edit icon, [0048] shows Fig. 13 and the ability to designate resolution specialists, and other roles including analyst, customer support, etc.).
Whitehead is combined based on the reasons and rationale set forth in the rejection of Claim 1 above.
As per Claim 8 Vasylyev further teaches:
one or more program-aided LLMs to integrate code with text to capture a required reasoning and process (Vasylyev in at least [0062, 0144, 0267, 0534] describe using LLMs to capture and understanding sequences of data like sentences in text and to capture relationships between words, phrases, grammar and other language aspects, the models can also be trained allowing them to capture rich semantic and syntactic patterns and helps to develop a deep understanding of the contextual nuances and relationships within user’s input).
As per Claim 9 Vasylyev further teaches:
further comprising at least one corresponding engine that provides reasoning results (Vasylyev in at least [0062, 0144, 0267, 0534] describe using LLMs to capture and understanding sequences of data like sentences in text and to capture relationships between words, phrases, grammar and other language aspects, the models can also be trained allowing them to capture rich semantic and syntactic patterns and helps to develop a deep understanding of the contextual nuances and relationships within user’s input, e.g. reasoning results).
As per Claims 10-20 the limitations are substantially similar to those set forth in Claims 1-9 and are therefore rejected based on the same reasons and rationale set forth in the rejections of Claims 1-9 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mystetskyi et al. (US 2026/0032155) Integrating generative AI within Software as a Service platforms to automate operations, synchronize workflows and prompt agents with information supporting natural language explanation sessions.
Clark et al. (US 2022/0147890) System to facilitate risk relationship document guided navigation of direct access databases for advance analytics.
Amaral et al. (US 2022/0207617) System to provide a risk relationship life event analytical modeling platform via back end application computer server of an enterprise.
Wu et al. (US 2025/0390517) Digital Content Generation with In-Prompt Hallucination Management for Conversational Agent.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEPHANIE Z DELICH whose telephone number is (571)270-1288. The examiner can normally be reached on Monday - Friday 7-3:30.
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/STEPHANIE Z DELICH/Primary Examiner, Art Unit 3623
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