patccat: A classifier for patent claims

Item Type:	Dataset
Title:	patccat: A classifier for patent claims
Date:	2022
Creator:	Ganglmair, Bernhard ORCID: 0000-0002-3821-2813 ; Robinson, W. Keith ; Seeligson, Michael
Divisions:	School of Law and Economics > Innovation u. Wettbewerb (Ganglmair 2020-)

DDC Classification:	330 Economics
Abstract:	This is version 3.3.0 of the patccat data (patent claim classification by algorithmic text analysis). Patent claims define an invention. A patent application is required to have one or more claims that distinctly claim the subject matter which the patent applicant regards as her invention or discovery. We construct a classifier of patent claims that identifies three distinct claim types: process claims, product claims, and product-by-process claims. For this classification, we combine information obtained from both the preamble and the body of a claim. The preamble is a general description of the invention (e.g., a method, an apparatus, or a device), whereas the body identifies steps and elements (specifying in detail the invention laid out in the preamble) that the applicant is claiming as the invention. The combination of the preamble type and the body type provides us with a more detailed and more accurate classification of claims than other approaches in the literature. This approach also accounts for unconventional drafting approaches. We eventually validate our classification using close to 10,000 manually classified claims. The data files contain the results of our classification. We provide claim-level information for each independent claim of U.S. utility patents granted between 1836 and 2020. We also provide patent-level information, i.e., the counts of different claim types for a given patent. For a detailed description of our classification approach, please take a look at the accompanying paper (Ganglmair, Robinson, and Seeligson 2022).
External Identifier for Data:	https://doi.org/10.5281/zenodo.6395308

URL:	https://madata.bib.uni-mannheim.de/1052/
Access (Controlled):	Only Metadata
License (Controlled):	Creative Commons: CC-BY | Attribution 4.0 (recommended)

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Date Deposited:	06 Jul 2026 16:10
Last Modified:	06 Jul 2026 16:10