The global patent application count has steadily increased, achieving eight consecutive years of growth.The global patent industry has shown a general trend of expansion. This is attributed to the increasing innovation activities, particularly in the fields of technology, healthcare, and biotechnology. Some emerging market countries, such as China and India, have experienced significant growth in the patent domain, becoming important participants in global patent activities.

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.

Patents are widely regarded as a proxy for inventive output which is valuable and can be commercialized by various means. Individual patent information such as technology field, classification, claims, application jurisdictions are increasingly available as released by different venues. This work has relied on a long-standing hypothesis that the citation received by a patent is a proxy for knowledge flows or impacts of the patent thus is directly related to patent value. This paper does not fall into the line of intensive existing work that test or apply this hypothesis, rather we aim to address the limitation of using so-far received citations for patent valuation. By devising a point process based patent citation type aware (self-citation and non-self-citation) prediction model which incorporates the various information of a patent, we open up the possibility for performing predictive patent valuation which can be especially useful for newly granted patents with emerging technology. Study on real-world data corroborates the efficacy of our approach. Our initiative may also have policy implications for technology markets, patent systems and all other stakeholders. The code and curated data will be available to the research community.