# US 2026/0099883 A1 · Generative ai-based system and method for claim data processing and evaluation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0099883 A1 - Document ID: US20260099883A1 - Status: Published application - Kind: A1 - Application number: 19/174,284 - Filed: 2025-04-09 - Published: 2026-04-09 - Earliest claimed: 2024-10-07 - Assignees: Cognizant Technology Solutions India Pvt. Ltd. - Applicants: Cognizant Technology Solutions India Pvt. Ltd - Inventors: Debojyoti Hazra; Latoya James; Mohamed Abdul Sattar; Guruprasad Krishnadoss; Jigar Ashok Parikh; Chandra Sekhar Dasika; Muniraju Bhadrappa; Milind Tilak; Richa Chaudhary - Web page: https://patentgrep.com/patent/US20260099883A1 ## CPC - G06Q 40/08: Insurance - G06F 40/205: Parsing - G06F 40/30: Semantic analysis - G06N 3/042: Knowledge-based neural networks; Logical representations of neural networks - G06N 3/0475: Generative networks - G06N 3/10: Interfaces, programming languages or software development kits, e.g. for simulating neural networks - G06N 20/00: Machine learning - G06F 40/103: Formatting, i.e. changing of presentation of documents (automatic justification G06F40/189; automatic line break hyphenation G06F40/191) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A system and method for Gen AI based claim data processing and evaluation is provided. The present invention enables generating prompt data by processing parsed standard operation procedure data and rules data associated with a first set of pre-defined rules. The prompt data is provided as a first prompt data to a Large Language Model (LLM) to generate a set of first rules. Set of first rules is provided along with output generation instructions as a second prompt data to the LLM to generate a set of second rules. Non-adjudicated claims data is extracted along with corresponding non-automated edit codes for evaluation. An output is generated in the form of recommendations by validating non-adjudicated claims data and corresponding non-automated edit codes based on a comparison with set of second rules. The recommendations are provided for resolving the non-adjudicated claims data. ## Claims ### Claim 1 A system for Generative Artificial Intelligence (Gen AI) based claim data processing and evaluation, the system comprising: a memory storing program instructions; a processor executing the program instructions stored in the memory; and a Gen AI based data processing engine executed by the processor and configured to: generate prompt data by processing parsed Standard Operation Procedure (SOP) data and rules data associated with a first set of pre-defined rules; provide the prompt data as a first prompt data to a Large Language Model (LLM) to generate a set of first rules; provide the set of first rules along with one or more output generation instructions as a second prompt data to the LLM to generate a set of second rules, wherein the set of second rules is employed for evaluating one or more non-adjudicated claims data, the non-adjudicated claims data is extracted along with corresponding one or more non-automated edit codes from an adjudication unit for evaluation; and generate an output in the form of one or more recommendations by validating the non-adjudicated claims data and the corresponding non-automated edit codes based on a comparison with the set of second rules, wherein the recommendations are provided to the adjudication unit for resolving the non-adjudicated claims data. ### Claim 2 (depends on 1) The system as claimed in claim 1, wherein the Gen AI based data processing engine comprises a prompt generation unit executed by the processor and is configured to fetch the SOP data along with edit codes and the rules data from a SOP data unit, and wherein the SOP data represents a pre-defined series of steps and corresponding resolution steps for resolving the claims data. ### Claim 3 (depends on 2) The system as claimed in claim 2, wherein the prompt generation unit parses the SOP data and the rules data to generate the first prompt data by employing one or more prompt engineering techniques, and wherein the SOP data is labelled by highlighting the text present in SOP data with a pre-defined color. ### Claim 4 (depends on 1) The system as claimed in claim 1, wherein the data processing engine comprises a rule generation unit executed by the processor and is configured to convert the set of first rules to a comprehensive natural language format using natural language processing techniques, and wherein the set of first rules is generated by employing NLP techniques, and wherein the rule generation unit adds missing clauses to the set of first rules, removes irrelevant clauses and fine tunes the set of first rules. ### Claim 5 (depends on 4) The system as claimed in claim 4, wherein the set of second rules is generated by the rule generation unit in a JavaScript Object Notation (JSON) format, and wherein the set of second rules includes field mappings corresponding to one or more edit codes associated with the claims data, the field mappings are carried out by mapping the SOP data to the set of first rules and mapping the set of first rules to the set of second rules, and wherein an individual JSON file is created for each of the edit codes, the JSON file comprises the set of second rules to be applied for processing the non-adjudicated claims data along with corresponding non-automated edit codes. ### Claim 6 (depends on 1) The system as claimed in claim 1, wherein the data processing engine comprises an extraction unit executed by the processor and configured to extract the non-adjudicated claims data along with corresponding non-automated edit codes based on a second set of pre-defined rules using robotic process automation and/or an application program interface. ### Claim 7 (depends on 1) The system as claimed in claim 1, wherein the data processing engine comprises a validation and recommendation generation unit executed by the processor and is configured to fetch the set of second rules from a knowledge database, and the non-adjudicated claims data and all the corresponding non-automated edit codes are fetched from an extraction unit for generating the recommendations by employing one or more Gen AI techniques, ### Claim 8 (depends on 7) The system as claimed in claim 7, wherein recommendations are provided based on the validated non-adjudicated claims data using a python post processing technique, which provides a configurable list of priority actions, and wherein the recommendations comprise one or more reasons for failure in resolving or processing the non-adjudicated claims data along with sequence of action steps for resolving the non-adjudicated claims data, and wherein the recommendations comprise a second set of pre-defined rule checklists with one or more remarks and summary of the recommendations provided in a consolidated form, which has a configurable list of priority actions. ### Claim 9 (depends on 8) The system as claimed in claim 8, wherein the validation and recommendation generation unit provides the recommendations to the adjudication unit using robotic process automation and/or an application program interface for resolving the non-adjudicated claims data, and wherein the recommendations are rendered on a graphical user interface of a user interface unit for receiving a feedback from users with respect to the generated recommendations, the feedback is processed by the data processing engine for fine-tuning a rule generation unit by using supervised active learning technique which modifies and refines the recommendations. ### Claim 10 A method for Generative Artificial Intelligence (Gen AI) based claim data processing and evaluation, the method is implemented by a processor executing instructions stored in a memory, the method comprises: generating prompt data by processing parsed Standard Operation Procedure (SOP) data and rules data associated with a first set of pre-defined rules; providing the prompt data as a first prompt data to a Large Language Model (LLM) to generate set of first rules; providing the set of first rules along with one or more output generation instructions as a second prompt data to the LLM to generate a set of second rules, and wherein the set of second rules is employed for evaluating one or more non-adjudicated claims data, the non-adjudicated claims data is extracted along with corresponding one or more non-automated edit codes from an adjudication unit for evaluation; and generating an output in the form of one or more recommendations by validating the non-adjudicated claims data and the corresponding non-automated edit codes based on a comparison with the set of second rules, wherein the recommendations are provided to the adjudication unit for resolving the non-adjudicated claims data. ### Claim 11 (depends on 10) The method as claimed in claim 10, wherein the SOP data and the rules data are parsed to generate the first prompt data by employing one or more prompt engineering techniques, and wherein the SOP data is labelled by highlighting text present in the SOP data with a pre-defined color. ### Claim 12 (depends on 10) The method as claimed in claim 10, wherein the set of first rules is converted to a comprehensive natural language format using natural language processing techniques, and wherein the set of first rules is generated by employing NLP techniques, and wherein missing clauses are added to the set of first rules, irrelevant clauses are removed and the set of first rules are fine-tuned. ### Claim 13 (depends on 10) The method as claimed in claim 10, wherein the set of second rules is generated in a JavaScript Object Notation (JSON) format, and wherein the set of second rules includes field mappings corresponding to one or more edit codes associated with the claims data, the field mappings are carried out by mapping the SOP data to the set of first rules and mapping the set of first rules to the set of second rules, and wherein an individual JSON file is created for each of the edit codes, the JSON file comprises the set of second rules to be applied for processing the non-adjudicated claims data along with corresponding non-automated edit codes. ### Claim 14 (depends on 10) The method as claimed in claim 10, wherein the non-adjudicated claims data corresponding to the non-automated edit codes are extracted based on a second set of pre-defined rules using robotic process automation and/or an application program interface. ### Claim 15 (depends on 10) The method as claimed in claim 10, wherein the recommendations comprise one or more reasons for failure in resolving or processing the non-adjudicated claims data along with sequence of action steps for resolving the non-adjudicated claims data, and wherein the recommendations are rendered via a graphical user interface for receiving a feedback from users with respect to the generated recommendations, the feedback is processed by the data processing for fine-tuning by using supervised active learning technique which modifies and refines the recommendations. ### Claim 16 A computer program product comprising: a non-transitory computer-readable medium having computer program code stored thereon, the computer-readable program code comprising instructions that, when executed by a processor, causes the processor to: generate prompt data by processing parsed Standard Operation Procedure (SOP) data and rules data associated with a first set of pre-defined rules; provide the prompt data as a first prompt data to a Large Language Model (LLM) to generate a set of first rules; provide the set of first rules along with one or more output generation instructions as a second prompt data to the LLM to generate a set of second rules, wherein the set of second rules is employed for evaluating one or more non-adjudicated claims data, and wherein the non-adjudicated claims data is extracted along with corresponding one or more non-automated edit codes from an adjudication unit for evaluation; and generate an output in the form of one or more recommendations by validating the non-adjudicated claim data and the corresponding non-automated edit codes based on a comparison with the set of second rules, wherein the recommendations are provided to the adjudication unit for resolving the non-adjudicated claims data.