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Replicable Patent Indicators Using the Google Patents Public Datasets

Author

Listed:
  • George Abi Younes

    (Ecole polytechnique federale de Lausanne)

  • Gaetan de Rassenfosse

    (Ecole polytechnique federale de Lausanne)

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.

Suggested Citation

  • George Abi Younes & Gaetan de Rassenfosse, 2023. "Replicable Patent Indicators Using the Google Patents Public Datasets," Working Papers 24, Chair of Science, Technology, and Innovation Policy.
    • Handle: RePEc:iip:wpaper:24
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    File URL: http://cdm-it.epfl.ch/RePEc/iip-wpaper/WP24.pdf
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    More about this item

    Keywords

    BigQuery language; data transparency; patent analytics; patent data;
    All these keywords.

    JEL classification:

    • O34 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Intellectual Property and Intellectual Capital

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