LLM Comparative Data Structure Generation Patent

ChangeBridge: Patent Apps - Business Methods (G06Q)

Published April 2nd, 2026

Detected April 2nd, 2026

Summary

USPTO published patent application US20260094193A1 by Bhavika Goyal et al. covering systems and methods for generating comparative data structures using large language models (LLMs). The application processes user queries and context data to extract differentiators from search results and generate comparison frameworks. The application was filed on September 30, 2024, under Application No. 18902513.

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What changed

Patent application US20260094193A1 discloses systems and methods for generating comparative data structures using LLMs. The invention involves obtaining user queries with context data, processing these through an LLM to generate search results, then defining a schema of differentiators based on the query and results. The LLM extracts information associated with these differentiators and generates a comparative data structure that organizes and compares search results.

Patent applications do not create regulatory obligations. Compliance officers should note this is informational—published patent applications indicate technology areas being protected but impose no compliance requirements. No action is required. The publication is relevant primarily for competitive intelligence and monitoring of AI technology patents in comparative analysis systems.

Source document (simplified)

← USPTO Patent Applications

Systems and Methods for the Generation of a Comparative Data Structure using a Large Language Model

Application US20260094193A1 Kind: A1 Apr 02, 2026

Inventors

Bhavika Goyal, Rushil Grover, Shahab Kamali, Aidan Oldershaw, Senthil Hariramasamy

Abstract

Systems and methods for the generation of a comparative data structure using a large language model. The method includes obtaining, by a computing system, input data comprising a user query and query context data; processing, by a large language model (LLM) operating on the computing system, the user query and the query context data to generate a set of search results; defining, by the LLM, a schema associated with a plurality of differentiators based on the user query, the query context data, and the set of search results; extracting, by the LLM, information associated with the plurality of differentiators from the set of search results; generating, by the LLM, a comparative data structure using the schema and the information associated with the plurality of differentiators; and comparing, by the LLM, the set of search results based on the information associated with the plurality of differentiators using the comparative data structure.