用AI多智能体系统自动处理专利分析，提升效率与准确性。
Towards Automated Patent Workflows: AI-Orchestrated Multi-Agent Framework for Intellectual Property Management and Analysis
- 设计元智能体协调多个专业代理，分工完成分类、摘要、权利要求生成等任务。
- 引入图检索增强生成技术，使多专利分析结果更准确相关。
- 配备纠错评估代理，提供反馈并保障决策可解释性，适合专利机构使用。
专利是创新的货币，其管理与保护至关重要。随着专利文档复杂度上升及申请量激增，自动化专利分析成为迫切需求。本文提出PatExpert，一个自主的多智能体对话框架，用于优化专利相关工作流程。该框架包含一个元智能体，负责协调多个任务专用专家代理，如专利分类、接受判断、权利要求生成、抽象摘要、多专利分析及科学假设生成；同时配备批判性评估代理（Gold-LLM-as-a-Judge和Reward-LLM-as-a-Judge），通过迭代反馈实现错误检测与修正。针对多专利分析，框架引入图检索增强生成（GRAG）方法，结合语义相似性与知识图谱，提升响应准确性和相关性。整体设计强调可解释性，确保决策过程透明。实证表明，该框架在多项专利处理任务中显著提升性能，为专利管理与分析提供高效、可靠、合规的自动化解决方案。
原文摘要 · Abstract (English)
Patents are the currency of innovation, and like any currency, they need to be managed and protected (Gavin Potenza). Patents, as legal documents that secure intellectual property rights, play a critical role in technological innovation. The growing complexity of patent documents and the surge in patent applications have created a need for automated solutions in patent analysis. In this work, we present PatExpert, an autonomous multi-agent conversational framework designed to streamline and optimize patent-related tasks. The framework consists of a metaagent that coordinates task-specific expert agents for various patent-related tasks and a critique agent for error handling and feedback provision. The meta-agent orchestrates specialized expert agents, each fine-tuned for specific tasks such as patent classification, acceptance, claim generation, abstractive summarization, multi-patent analysis, and scientific hypothesis generation. For multi-patent analysis, the framework incorporates advanced methods like Graph Retrieval-Augmented Generation (GRAG) to enhance response accuracy and relevance by combining semantic similarity with knowledge graphs. Error handling is managed by critique agents (Gold-LLM-as-a-Judge and Reward-LLM-as-a-Judge), which evaluate output responses for accuracy and provide iterative feedback. The framework also prioritizes explainability, ensuring transparent justifications for decisions made during patent analysis. Its comprehensive capabilities make it a valuable tool for automating complex patent workflows, enhancing efficiency, accuracy, and compliance in patent-related tasks. Empirical evidence demonstrates significant improvements in patent processing tasks, concluding that the framework offers a robust solution for automating and optimizing patent analysis.
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