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<p>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.</p>

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The artificial intelligence patent dataset (AIPD) 2023 update

  • 期刊论文

  • 发表日期：

    2025年2月22日

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  • Nicholas A. Pairolero,
  • Alexander V. Giczy,
  • Gerard Torres,
  • Tisa Islam Erana,
  • Mark A. Finlayson,
  • Andrew A. Toole

摘要

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.