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Published March 14, 2020 | Version 0.3.0
Dataset Open

PatCit: A Comprehensive Dataset of Patent Citations

  • 1. Collège de France
  • 2. École Polytechnique Fédérale de Lausanne
  • 3. Hitotsubashi University
  • 4. HEC Paris


patCit: A Comprehensive Dataset of Patent Citations [NewsletterGitHub]

Patents are at the crossroads of many innovation nodes: science, industry, products, competition, etc. Such interactions can be identified through citations in a broad sense.

It is now common to use front-page patent citations to study some aspects of the innovation system. However, there is much more buried in the Non Patent Literature (NPL) citations and in the patent text itselfpatCit extracts and structures these citations.

Want to know more? Read patCit academic presentation or dive into usage and technical guides on patCit documentation website.


At patCit, we are building a comprehensive dataset of patent citations to help the community explore this terra incognita. patCit has the following features:

  • global coverage
  • front-page and in-text citations
  • all categories of NPL documents


patCit builds on DOCDB, the largest database of Non Patent Literature (NPL) citations. First, we deduplicate this corpus and organize it into 10 categories (bibliographical reference, database, norm & standard, etc). Then, we design and apply category specific information extraction models using spaCy. Eventually, when possible, we enrich the data using external domain specific high quality databases (e.g. Crossref for bibliographical references).


patCit builds on Google Patents corpus of USPTO full-text patents. First, we extract patent and bibliographical reference citations. Then, we parse detected in-text citations into a series of category dependent attributes using grobid. Patent citations are matched with a standard publication number using the Google Patents matching API and bibliographical references are matched with a DOI using biblio-glutton. Eventually, when possible, we enrich the data using external domain specific high quality databases (e.g. Crossref for bibliographical references).



Find - The patCit dataset is available on BigQuery in an interactive environment. For those who have a smattering of SQL, this is the perfect place to explore the data. It can also be downloaded on Zenodo.

Interoperate - Interoperability is at the core of patCit ambition. We take care to extract unique identifiers whenever it is possible to enable data enrichment for domain specific high quality databases. This includes the DOI, PMID and PMCID for bibliographical references, the Technical Doc Number for standards, the Accession Number for Genetic databases, the publication number for PATSTAT and Claims, etc. See specific table for more details.

Reproduce - Our gitHub repository is the project factory. You can learn more about data recipes and models on the patCit documentation website.


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