Identifying artificial intelligence (AI) invention: a novel AI patent dataset

Alexander V. Giczy, Nicholas A. Pairolero and Andrew A. Toole ()
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Alexander V. Giczy: U.S. Patent and Trademark Office
Nicholas A. Pairolero: U.S. Patent and Trademark Office
Andrew A. Toole: U.S. Patent and Trademark Office

The Journal of Technology Transfer, 2022, vol. 47, issue 2, No 6, 476-505

Abstract: Abstract Artificial intelligence (AI) is an area of increasing scholarly and policy interest. To help researchers, policymakers, and the public, this paper describes a novel dataset identifying AI in over 13.2 million patents and pre-grant publications (PGPubs). The dataset, called the Artificial Intelligence Patent Dataset (AIPD), was constructed using machine learning models for each of eight AI component technologies covering areas such as natural language processing, AI hardware, and machine learning. The AIPD contains two data files, one identifying the patents and PGPubs predicted to contain AI and a second file containing the patent documents used to train the machine learning classification models. We also present several evaluation metrics based on manual review by patent examiners with focused expertise in AI, and show that our machine learning approach achieves state-of-the-art performance across existing alternatives in the literature. We believe releasing this dataset will strengthen policy formulation, encourage additional empirical work, and provide researchers with a common base for building empirical knowledge on the determinants and impacts of AI invention.

Keywords: Patent; Patent landscape; Artificial intelligence; AI; Machine learning; Patent dataset (search for similar items in EconPapers)
JEL-codes: C45 L86 O31 O34 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s10961-021-09900-2

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