Predictive Patentomics: Forecasting Innovation Success and Valuation
  with ChatGPT
Stephen Yang
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.