PatCit: A Comprehensive Dataset of Patent Citations

Verluise, Cyril; Cristelli, Gabriele; Higham, Kyle; Violon, Lucas; Rassenfosse, Gaétan de

doi:10.5281/zenodo.3710994

Cited by 2 publications

(4 citation statements)

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Section: Resultsmentioning

confidence: 99%

“… Top 3 academic papers cited in the patents in each of the six technologies on the front page retrieved using PatCit [ 35 ]. …”

Section: Resultsmentioning

confidence: 99%

“…They also provide insights about the main actors of the different technologies considered. Finally, we also use the PatCit dataset [35] and look at the top 3 most cited academic articles by patents in each technology. 4 reports the top 10 assignees for each technology by the number of patents they were granted worldwide.…”

Section: Plos Onementioning

confidence: 99%

“…As a last exercise, we use the PatCit database [35,41] to look at the most cited academic papers by technology. PatCit is a tool that lists all citations from patents to research articles (also known as Non Patent Literature citations) that were used as a source.…”

Section: Top Academic Publicationsmentioning

confidence: 99%

See 3 more Smart Citations

Identifying technology clusters based on automated patent landscaping

Antonin,

Cyril

2023

PLoS ONE

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We introduce a new general methodological approach for accurately and consistently retrieving a large set of patents related to specific technologies. We build upon the automated patent landscaping algorithm by incorporating a tractable amount of human supervision to improve the accuracy and consistency of our results. We demonstrate the efficacy of our approach by applying it to six novel and representative technologies: additive manufacturing, blockchain, computer vision, genome editing, hydrogen storage, and self-driving vehicles.

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Section: Resultsmentioning

confidence: 99%

“… Top 3 academic papers cited in the patents in each of the six technologies on the front page retrieved using PatCit [ 35 ]. …”

Section: Resultsmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Top Academic Publicationsmentioning

confidence: 99%

See 2 more Smart Citations

Identifying technology clusters based on automated patent landscaping

Antonin,

Cyril

2023

PLoS ONE

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Reliance on Science by Inventors: Hybrid Extraction of In-text Patent-to-Article Citations

Marx¹,

Fuegi²

2020

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We curate and characterize a complete set of citations from patents to scientific articles, including nearly 16 million from the full text of USPTO and EPO patents. Combining heuristics and machine learning, we achieve 25% higher performance than machine learning alone. At 99.4% accuracy, coverage of 87.6% is achieved, and coverage above 90% with accuracy above 93%. Performance is evaluated with a set of 5,939 randomly-sampled, cross-verified "known good" citations, which the authors have never seen. We compare these "in-text" citations with the "official" citations on the front page of patents. In-text citations are more diverse temporally, geographically, and topically. They are less self-referential and less likely to be recycled from one patent to the next. That said, in-text citations have been overshadowed by front-page in the past few decades, dropping from 80% of all paper-to-patent citations to less than 40%. In replicating two published articles that use only citations on the front page of patents, we show that failing to capture those in the body text leads to understating the relationship between academic science and commercial invention. All patent-to-article citations, as well as the knowngood test set, are available at http://relianceonscience.org.

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PatCit: A Comprehensive Dataset of Patent Citations

Cited by 2 publications

References 0 publications

Identifying technology clusters based on automated patent landscaping

Identifying technology clusters based on automated patent landscaping

Reliance on Science by Inventors: Hybrid Extraction of In-text Patent-to-Article Citations

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