用智能代理从专利中自动生成创新产品创意
Agent Ideate: A Framework for Product Idea Generation from Patents Using Agentic AI
- 设计智能代理框架，分步解析专利并生成产品想法
- 在三个领域测试，代理方法在创意质量上显著优于单一模型
- 适合想从专利挖掘商业点子的研究者和创业者
专利蕴含丰富的技术知识，可激发创新产品构思，但获取与理解仍具挑战。本文探索利用大语言模型（LLMs）与自主智能体从给定专利中挖掘并生成产品概念。我们设计了 Agent Ideate 框架，实现从专利自动生成基于产品的商业创意。实验采用开源 LLM 与代理架构，在计算机科学、自然语言处理和材料化学三个领域展开评估。结果表明，代理方法在创意质量、相关性与新颖性方面均持续优于独立的 LLM。研究说明，将大语言模型与智能体工作流结合，能显著提升创新流程效率，释放专利数据在商业创意生成中的潜在价值。
原文摘要 · Abstract (English)
Patents contain rich technical knowledge that can inspire innovative product ideas, yet accessing and interpreting this information remains a challenge. This work explores the use of Large Language Models (LLMs) and autonomous agents to mine and generate product concepts from a given patent. In this work, we design Agent Ideate, a framework for automatically generating product-based business ideas from patents. We experimented with open-source LLMs and agent-based architectures across three domains: Computer Science, Natural Language Processing, and Material Chemistry. Evaluation results show that the agentic approach consistently outperformed standalone LLMs in terms of idea quality, relevance, and novelty. These findings suggest that combining LLMs with agentic workflows can significantly enhance the innovation pipeline by unlocking the untapped potential of business idea generation from patent data.
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