Analysis of innovation has been fundamentally limited by conventional approaches to broad, structural variables. This paper pushes the boundaries, taking an LLM approach to patent analysis with the groundbreaking ChatGPT technology. OpenAI's state-of-the-art textual embedding accesses complex information about the quality and impact of each invention to power deep learning predictive models. The nuanced embedding drives a 24% incremental improvement in R-squared predicting patent value and clearly isolates the worst and best applications. These models enable a revision of the contemporary Kogan, Papanikolaou, Seru, and Stoffman (2017) valuation of patents by a median deviation of 1.5 times, accounting for potential institutional predictions. Furthermore, the market fails to incorporate timely information about applications; a long-short portfolio based on predicted acceptance rates achieves significant abnormal returns of 3.3% annually. The models provide an opportunity to revolutionize startup and small-firm corporate policy vis-a-vis patenting.

翻译：创新分析长期以来受到传统宽泛结构变量的根本性限制。本文突破现有边界，采用基于突破性ChatGPT技术的大型语言模型方法进行专利分析。OpenAI最先进的文本嵌入技术能够获取每项发明在质量与影响力方面的复杂信息，从而驱动深度学习预测模型。这种精细嵌入使得专利价值预测的R平方值提升了24%，并清晰地区分出最差与最佳申请案例。这些模型对当代Kogan、Papanikolaou、Seru和Stoffman（2017）的专利估值进行了中位数为1.5倍的修订，同时考虑了潜在的机构预测。此外，市场未能及时纳入有关申请的信息；基于预测通过率的多空组合每年可获得3.3%的显著异常收益。这些模型为彻底改革初创企业和小型企业在专利方面的公司政策提供了契机。