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How important is access to patent documents for subsequent innovation? We examine the expansion of the USPTO Patent Library system after 1975. Patent libraries provided access to patents before the Internet. We find that after patent library opening, local patenting increases by 8–20 percent relative to similar regions. Additional analyses suggest that disclosure of technical information drives this effect: inventors increasingly take up ideas from outside their region, and the effect is strongest in technologies where patents are more informative. We thus provide evidence that disclosure plays an important role in cumulative innovation. (JEL D83, K11, O31, O34, R11)
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This content will become publicly available on February 22, 2026
The artificial intelligence patent dataset (AIPD) 2023 update
The 2023 update to the Artificial Intelligence Patent Dataset (AIPD) extends the original AIPD to all United States Patent and Trademark Office (USPTO) patent documents (i.e., patents and pre-grant publications, or PGPubs) published through 2023, while incorporating an improved patent landscaping methodology to identify AI within patents and PGPubs. This new approach substitutes BERT for Patents for the Word2Vec embeddings used previously, and uses active learning to incorporate additional training data closer to the “decision boundary” between AI and not-AI to help improve predictions. We show that this new approach achieves substantially better performance than the original methodology on a set of patent documents where the two methods disagreed—on this set, the AIPD 2023 achieved precision of 68.18 percent and recall of 78.95 percent, while the original AIPD achieved 50 percent and 21.05 percent, respectively. To help researchers, practitioners, and policy-makers better understand the determinants and impacts of AI invention, we have made the AIPD 2023 publicly available on the USPTO’s economic research web page.
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- Award ID(s):
- 1749917
- PAR ID:
- 10644694
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- The Journal of Technology Transfer
- ISSN:
- 0892-9912
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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