Generative AI Content Retrieval Standardization Framework

ChangeBridge: Patent Apps - AI & Computing (G06N)

Published March 26th, 2026

Detected April 1st, 2026

Summary

The USPTO published patent application US20260087080A1 by inventor Jian JIAO for a generative AI framework that standardizes user queries and content items into a common object format with normalized values. The system improves content retrieval accuracy by combining selective online and offline calls to the generative AI model with a distilled encoder neural network, enabling real-time results.

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What changed

This patent application describes a content retrieval system utilizing generative AI to standardize user queries and content items into a common object format with normalized values, improving retrieval accuracy. The system employs selective online and offline calls to the generative AI model combined with a distilled encoder neural network for efficiency and real-time results. The invention covers CPC classifications G06F 16/9535 and G06N 3/0475.

Patent applicants and technology companies developing AI-powered content retrieval systems should review this application to understand emerging standardization approaches in generative AI. No immediate compliance actions are required as this is a patent filing rather than an enforceable regulation.

Source document (simplified)

← USPTO Patent Applications

CONTENT RETRIEVAL STANDARDIZATION USING GENERATIVE ARTIFICIAL INTELLIGENCE (AI) MODELS

Application US20260087080A1 Kind: A1 Mar 26, 2026

Inventors

Jian JIAO

Abstract

This disclosure describes a framework for improving the retrieval of content items for user queries using a generative artificial intelligence (AI) model. Specifically, this disclosure describes a content retrieval system (e.g., a system for standardizing and retrieving content items) that utilizes a generative AI model to standardize user queries and content items into a common object format with normalized values, which improves the accuracy of content retrieval. Additionally, the content retrieval system improves system efficiency by enabling real-time results through a combination of selective online and offline calls to the generative AI model and a distilled encoder neural network.