Template-type: ReDIF-Paper 1.0
Author-Name: George Abi Younes
Author-Workplace-Name: Ecole polytechnique federale de Lausanne
Author-Name: Gaetan de Rassenfosse
Author-Workplace-Name: Ecole polytechnique federale de Lausanne
Author-Homepage: http://www.gder.info 
Title: Replicable Patent Indicators Using the Google Patents Public Datasets
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.
Classification-JEL: O34
Keywords: BigQuery language; data transparency; patent analytics; patent data
Length: 20 pages
Creation-Date: 2023-11
File-URL:  https://cdm-repec.epfl.ch/iip-wpaper/WP24.pdf
File-Format: application/pdf
Handle:  RePEc:iip:wpaper:24