Dataset
uspto-ai-patent-landscape
USPTO Chief Economist's compilation of patent assignment information covering January 1970 through December 2022, classifying 13,244,037 granted patents and published applications from 1976 to 2020 across eight artificial intelligence component categories using machine learning models.
Data preview
Dataset card
The USPTO AI Patent Landscape dataset, published on Hugging Face by user makeeoutchill, applies machine-learning classifications to more than 13 million USPTO patents and pre-grant publications from 1976 through 2020. It is positioned as an academic and research resource for studying patent assignment economics.
What this dataset contains
The dataset covers 13,244,037 granted patents and PGPubs issued between 1976 and 2020, labeled into eight artificial-intelligence component technologies. It is attributed to the USPTO Chief Economist's office and spans filings created or maintained from January 1970 through December 2022. The release was published on July 30, 2021.
Structure and fields
The file is distributed through the Hugging Face datasets API under a single 'train' split, accessible via load_dataset with the suchkow/uspto-ai-patent-landscape identifier. The supplied schema does not enumerate column names, so individual fields such as patent identifiers, AI component labels, or assignment metadata cannot be confirmed from the available documentation. Size is reported in the 10M–100M record band, consistent with the 13 million-plus patent records described.
How analysts use it
Researchers and academic users can use the dataset to study historical trends in AI-related patenting activity across eight technology categories, supporting econometric and innovation-policy work. Investment analysts focused on public patent filings and intellectual-property ownership could explore the data as a public-records signal on long-run technology diffusion, though column-level details would need to be verified before any quantitative workflow. The license is unstated, which limits redistribution and commercial reuse.
Documentation & references 1
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