Predictive Patentomics: Forecasting Innovation Success and Valuation with ChatGPT
This work addresses the challenge of forecasting innovation success and valuation for startups and firms, though it is incremental as it builds on existing LLM methods applied to a new domain.
The paper tackled the problem of predicting patent value and innovation success by using ChatGPT's textual embeddings to analyze patents, resulting in a 24% improvement in R-squared for predicting patent value and achieving 3.3% annual abnormal returns from a portfolio based on predicted acceptance rates.
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.