Predictive Patentomics: Forecasting Innovation Success and Valuation with ChatGPT

TL;DR

This paper leverages ChatGPT's advanced textual embeddings to improve patent valuation predictions and market timing strategies, offering new insights into innovation success and valuation.

Contribution

It introduces a novel LLM-based approach to patent analysis that significantly enhances prediction accuracy and market timing, surpassing traditional methods.

Findings

24% improvement in patent value prediction accuracy

Median deviation of 1.5 times in patent valuation revisions

3.3% annual abnormal returns from market timing strategy

Abstract

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.