用大模型+检索增强生成，自动高效查专利。
An automatic patent literature retrieval system based on LLM-RAG
- 大模型生成嵌入向量，结合外部文档检索专利。
- 在谷歌专利数据集上达80.5%语义匹配准确率，召回率92.1%。
- 适合知识产权管理、企业研发部门使用。
随着技术创新加速，专利文献的高效检索与分类已成为知识产权管理和企业研发的关键。传统关键词与规则方法难以应对复杂查询意图或跨技术领域的语义关联，导致结果不完整且相关性低。本研究提出一种融合大语言模型（LLMs）与检索增强生成（RAG）技术的自动化专利检索框架。系统包含三个模块：1）专利数据标准化预处理；2）基于大模型生成嵌入的高效向量检索引擎；3）结合外部文档检索与上下文感知生成的RAG查询模块。在包含数百万条全球专利记录的Google Patents数据集（2006–2024）上评估，所提出的gpt35turbo0125RAG配置达到80.5%的语义匹配准确率和92.1%的召回率，较基线大模型方法提升28个百分点。该框架在跨领域分类与语义聚类任务中也表现出强泛化能力。结果验证了LLM-RAG集成在智能专利检索中的有效性，为下一代AI驱动的知识产权分析平台奠定基础。
原文摘要 · Abstract (English)
With the acceleration of technological innovation efficient retrieval and classification of patent literature have become essential for intellectual property management and enterprise RD Traditional keyword and rulebased retrieval methods often fail to address complex query intents or capture semantic associations across technical domains resulting in incomplete and lowrelevance results This study presents an automated patent retrieval framework integrating Large Language Models LLMs with RetrievalAugmented Generation RAG technology The system comprises three components: 1) a preprocessing module for patent data standardization, 2) a highefficiency vector retrieval engine leveraging LLMgenerated embeddings, and 3) a RAGenhanced query module that combines external document retrieval with contextaware response generation Evaluations were conducted on the Google Patents dataset 20062024 containing millions of global patent records with metadata such as filing date domain and status The proposed gpt35turbo0125RAG configuration achieved 805 semantic matching accuracy and 92.1% recall surpassing baseline LLM methods by 28 percentage points The framework also demonstrated strong generalization in crossdomain classification and semantic clustering tasks These results validate the effectiveness of LLMRAG integration for intelligent patent retrieval providing a foundation for nextgeneration AIdriven intellectual property analysis platforms
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