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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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This content will become publicly available on October 4, 2026
Automated neural patent landscaping in the small data regime using citations and CPC codes
Patent landscaping is the process of identifying all patents related to a particular technological area, and is important for assessing various aspects of the intellectual property context. Traditionally, constructing patent landscapes is intensely laborious and expensive, and the rapid expansion of patenting activity in recent decades has driven an increasing need for efficient and effective automated patent landscaping approaches. In particular, it is critical that we be able to construct patent landscapes using a minimal number of labeled examples, as labeling patents for a narrow technology area requires highly specialized (and hence expensive) technical knowledge. We present an automated neural patent landscaping system that demonstrates significantly improved performance on difficult examples (0.69 on ‘hard’ examples, versus 0.6 for previously reported systems), and also significant improvements with much less training data (overall 0.75 on as few as 24 examples). Furthermore, in evaluating such automated landscaping systems, acquiring good data is challenge; we demonstrate a higher-quality training data generation procedure by merging (Abood and Feltenberger Artif Intell Law 26:103–125 2018) “seed/anti-seed” approach with active learning to collect difficult labeled examples near the decision boundary. Using this procedure we created a new dataset of labeled AI patents for training and testing. As in prior work we compare our approach with a number of baseline systems, and we release our code and data for others to build upon “(Code and data may be downloaded from https://doi.org/10.34703/gzx1-9v95/QDLKVWCode and data are released under the Creative Commons NC-BY 4.0 license at https://creativecommons.org/licenses/by-nc/4.0/)”.
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- Award ID(s):
- 1749917
- PAR ID:
- 10644695
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- Artificial Intelligence and Law
- ISSN:
- 0924-8463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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