用提示工程自动分析专利，快速掌握燃料电池技术全景
Automotive innovation landscaping using LLM
- 通过提示工程设计实现专利信息自动提取
- 从开源专利数据中构建燃料电池技术全景图
- 适合车企研发团队快速洞察技术趋势
通过专利分析进行汽车技术创新全景绘制对研发团队至关重要，有助于理解创新趋势、技术进展及竞争对手的最新技术。传统方法依赖大量人工操作，而大型语言模型（LLMs）的出现使该过程得以自动化，实现更快捷高效的专利分类与创新概念提取。本文提出一种基于提示工程的方法，用于提取专利所解决的问题、采用的技术及在车辆生态系统中的创新领域（如安全、高级驾驶辅助系统等）。该方法成功应用于开源专利数据，构建了燃料电池技术的全景图，全面呈现当前技术状态，为该领域的未来研发提供重要参考。
原文摘要 · Abstract (English)
The process of landscaping automotive innovation through patent analysis is crucial for Research and Development teams. It aids in comprehending innovation trends, technological advancements, and the latest technologies from competitors. Traditionally, this process required intensive manual efforts. However, with the advent of Large Language Models (LLMs), it can now be automated, leading to faster and more efficient patent categorization & state-of-the-art of inventive concept extraction. This automation can assist various R\&D teams in extracting relevant information from extensive patent databases. This paper introduces a method based on prompt engineering to extract essential information for landscaping. The information includes the problem addressed by the patent, the technology utilized, and the area of innovation within the vehicle ecosystem (such as safety, Advanced Driver Assistance Systems and more).The result demonstrates the implementation of this method to create a landscape of fuel cell technology using open-source patent data. This approach provides a comprehensive overview of the current state of fuel cell technology, offering valuable insights for future research and development in this field.
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