标题： 代理构思：一种使用代理人工智能从专利中生成产品创意的框架 显示英文标题

标题： Agent Ideate: A Framework for Product Idea Generation from Patents Using Agentic AI

摘要： 专利包含丰富的技术知识，可以激发创新的产品创意，但获取和解释这些信息仍然是一个挑战。 这项工作探讨了使用大型语言模型（LLMs）和自主代理从给定的专利中挖掘和生成产品概念。 在本工作中，我们设计了Agent Ideate，这是一个从专利中自动生成基于产品的商业创意的框架。 我们在三个领域：计算机科学、自然语言处理和材料化学中，对开源LLMs和基于代理的架构进行了实验。 评估结果表明，代理方法在创意质量、相关性和新颖性方面始终优于独立的LLMs。 这些发现表明，将LLMs与代理工作流程结合可以显著增强创新管道，通过释放从专利数据中生成商业创意的未开发潜力。 显示英文摘要

摘要： 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.