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New RAG Framework Enhances LLM-Based Patent Matching Accuracy

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel self-knowledge retrieval-augmented generation (RAG) framework designed to improve patent matching accuracy. This framework guides large language models (LLMs) to autonomously extract key technical entities and build hierarchical ontological structures from patent queries. By integrating FAISS retrieval with a generative matching mechanism and leveraging self-knowledge, the system enhances the LLM's understanding of patent innovations, leading to more precise retrieval and matching. Experimental results on real-world patent datasets indicate the method's effectiveness and potential for intellectual property protection. AI

IMPACT This framework could improve the accuracy and efficiency of patent analysis and intellectual property protection.

RANK_REASON The cluster contains a research paper detailing a new framework for patent matching using LLMs and RAG.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New RAG Framework Enhances LLM-Based Patent Matching Accuracy

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Jian Zhang, Songlin Lei, Zhuohao Yang, Bangli Liu, Ziwei Wang, Xufeng Weng, Gehan Amaratunga, Yu Lin, Hongwei Wang ·

    Self-Knowledge Retrieval Augmented Generation Framework for Patent Matching

    arXiv:2608.11030v1 Announce Type: cross Abstract: Patent retrieval and matching based on large language models (LLMs) play a vital role in intellectual property protection. However, due to the complex structure of patent documents, dense technical terminology, and multi-modal inf…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Hongwei Wang ·

    Self-Knowledge Retrieval Augmented Generation Framework for Patent Matching

    Patent retrieval and matching based on large language models (LLMs) play a vital role in intellectual property protection. However, due to the complex structure of patent documents, dense technical terminology, and multi-modal information, traditional methods struggle to accurate…

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