Replicable Patent Indicators Using the Google Patents Public Datasets

George Abi Younes and Gaétan de Rassenfosse ()
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George Abi Younes: Ecole polytechnique federale de Lausanne

Working Papers from Chair of Science, Technology, and Innovation Policy

Abstract: Recognizing the increasing accessibility and importance of patent data, the paper underscores the need for standardized and transparent data analysis methods. By leveraging the BigQuery language, we illustrate the construction and relevance of commonly used patent indicators derived from Google Patents Public Datasets. The indicators range from citation counts to more advanced metrics like patent text similarity. The code is available in an open Kaggle notebook, explaining operational intricacies and potential data issues. By providing clear, adaptable queries and emphasizing transparent methodologies, this paper hopes to contribute to the standardization and accessibility of patent analysis, offering a valuable resource for researchers and practitioners alike.

Keywords: BigQuery language; data transparency; patent analytics; patent data (search for similar items in EconPapers)
JEL-codes: O34 (search for similar items in EconPapers)
Pages: 20 pages
Date: 2023-11
New Economics Papers: this item is included in nep-big, nep-ind, nep-ino, nep-ipr and nep-tid
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Persistent link: https://EconPapers.repec.org/RePEc:iip:wpaper:24

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