Replicable Patent Indicators Using the Google Patents Public Datasets

20 Pages Posted: 26 Dec 2023

See all articles by George Abi Younes

George Abi Younes

Ecole Polytechnique Fédérale de Lausanne

Gaétan de Rassenfosse

École Polytechnique Fédérale de Lausanne

Date Written: November 26, 2023

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

Suggested Citation

Abi Younes, George and de Rassenfosse, Gaétan, Replicable Patent Indicators Using the Google Patents Public Datasets (November 26, 2023). Available at SSRN: https://ssrn.com/abstract=4644778 or http://dx.doi.org/10.2139/ssrn.4644778

George Abi Younes

Ecole Polytechnique Fédérale de Lausanne ( email )

Station 5
1015 Lausanne
Switzerland

Gaétan De Rassenfosse (Contact Author)

École Polytechnique Fédérale de Lausanne ( email )

Station 5
Odyssea 1.04
1015 Lausanne, CH-1015
Switzerland

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
329
Abstract Views
810
Rank
172,868
PlumX Metrics