用大模型分析专利，找技术演进趋势中的新机会
DiTTO-LLM: Framework for Discovering Topic-based Technology Opportunities via Large Language Model
- 基于专利文本和大模型提取技术主题，追踪其随时间变化
- 发现人工智能正向日常应用方向演进，呈现可及性提升趋势
- 适合战略规划、技术预见与创新管理人群参考
技术机会是推动科技、产业与创新发展的关键信息。本文提出一种基于技术间时序关系的框架，用于识别新兴技术机会。该框架首先从专利数据集中提取文本，通过文本主题映射发现技术间的关联关系；再通过追踪这些主题随时间的变化来识别技术机会。为提升效率，框架利用大语言模型进行主题抽取，并设计对话式提示（prompt）支持技术机会的发现。在美专利商标局提供的人工智能专利数据集上进行了评估，实验结果表明人工智能技术正朝着提升日常可及性的方向演进。该方法展示了识别未来技术机会的潜力。
原文摘要 · Abstract (English)
Technology opportunities are critical information that serve as a foundation for advancements in technology, industry, and innovation. This paper proposes a framework based on the temporal relationships between technologies to identify emerging technology opportunities. The proposed framework begins by extracting text from a patent dataset, followed by mapping text-based topics to discover inter-technology relationships. Technology opportunities are then identified by tracking changes in these topics over time. To enhance efficiency, the framework leverages a large language model to extract topics and employs a prompt for a chat-based language model to support the discovery of technology opportunities. The framework was evaluated using an artificial intelligence patent dataset provided by the United States Patent and Trademark Office. The experimental results suggest that artificial intelligence technology is evolving into forms that facilitate everyday accessibility. This approach demonstrates the potential of the proposed framework to identify future technology opportunities.
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