用多大模型协作分析专利，比GPT-4更懂技术突破
EvoPat: A Multi-LLM-based Patents Summarization and Analysis Agent
- 分角色调用多个大模型，分工完成规划、创新点识别与对比评估
- 在NLP领域测试中，专利摘要与技术评价性能超越GPT-4
- 能自动检索外部数据，实时融合文献、产品库等多源信息
科学和技术的快速发展体现在每年新申请专利数量的指数级增长。尽管专利推动创新，却给研究人员和工程师（尤其是新手）带来巨大负担。为避免在庞大复杂的专利体系中手动寻找趋势与突破，亟需高效工具实现专利的摘要、评估与上下文理解，揭示其创新贡献与科学原理。为此，我们提出EvoPat，一个基于多大语言模型的专利分析代理系统，结合检索增强生成（RAG）与高级搜索策略。EvoPat调用多个大模型，分别承担规划、识别创新点及开展对比评估等专项任务，并整合本地数据库（含专利、文献、产品目录、企业资料）与在线搜索数据，实现对原始数据库外信息的自动补充。在自然语言处理（NLP）领域的广泛测试表明，EvoPat在专利摘要、对比分析与技术评估任务中优于GPT-4。EvoPat代表了构建智能专利分析工具的重要进展，助力研究者高效应对专利信息复杂性。
原文摘要 · Abstract (English)
The rapid growth of scientific techniques and knowledge is reflected in the exponential increase in new patents filed annually. While these patents drive innovation, they also present significant burden for researchers and engineers, especially newcomers. To avoid the tedious work of navigating a vast and complex landscape to identify trends and breakthroughs, researchers urgently need efficient tools to summarize, evaluate, and contextualize patents, revealing their innovative contributions and underlying scientific principles.To address this need, we present EvoPat, a multi-LLM-based patent agent designed to assist users in analyzing patents through Retrieval-Augmented Generation (RAG) and advanced search strategies. EvoPat leverages multiple Large Language Models (LLMs), each performing specialized roles such as planning, identifying innovations, and conducting comparative evaluations. The system integrates data from local databases, including patents, literature, product catalogous, and company repositories, and online searches to provide up-to-date insights. The ability to collect information not included in original database automatically is also implemented. Through extensive testing in the natural language processing (NLP) domain, we demonstrate that EvoPat outperforms GPT-4 in tasks such as patent summarization, comparative analysis, and technical evaluation. EvoPat represents a significant step toward creating AI-powered tools that empower researchers and engineers to efficiently navigate the complexities of the patent landscape.
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