Statistical Analysis and Structural and Dynamic Characteristics of International Digital Technology Patents (2000–2024)

Relevance. The digital transformation of the economy and the growing volume of information necessitate the development of new tools for monitoring scientific and technological progress. Purpose. This study aims to provide a quantitative description of the scale, sectoral structure, temporal dynamics, geographical distribution, and corporate ownership of international digital technology patenting over the period 2000–2024. Methodology. The dataset was compiled using the Google Patents Public Datasets and covers eleven subclasses of the Cooperative Patent Classification (CPC). It consolidates the English-language titles and abstracts of related publications concerning the same invention. The study employs statistical, structural, and textual analysis methods using object-oriented and process-oriented dictionaries. Results. From a dataset of 16.0 million publications, 5.23 million digital patents dated 2000–2024 were identified. Over the study period, the annual number of patents increased from 65,700 to 541,200, with 46.78% of the patents in the sample falling within the 2020–2024 period. Geographically, 51.1% of the patents are concentrated in ten countries, including the United States, South Korea, China, and Japan. Textual analysis revealed the predominance of cross-sectoral patents in the invention-description category (89.4%). In terms of sector-specific trends, the number of patents in the artificial intelligence subclass (G06N) increased 238-fold. At the corporate level, the analysis demonstrated the predominance of patents originating from Asian countries, alongside a high degree of specialisation in patent portfolios. Practical implications. The findings and classification algorithms developed in this study can be used to forecast scientific and technological progress. Changes in the CPC structure and the growing share of multidisciplinary patents indicate a transition to the active phase of the Fourth Industrial Revolution.

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Publication Details

Journal
Accounting Analysis Auditing
Published
2026-10-27
DOI
https://doi.org/10.26794/2408-9303-2026-13-5-110-125
Primary Topic
Intellectual Property and Patents
Type
article
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article

Statistical Analysis and Structural and Dynamic Characteristics of International Digital Technology Patents (2000–2024)

Valentin Stepanov, М. А. Федотова, Елена Анатольевна Федорова
Accounting Analysis Auditing
Intellectual Property and Patents
article

Statistical Analysis and Structural and Dynamic Characteristics of International Digital Technology Patents (2000–2024)

Valentin Stepanov, М. А. Федотова, Елена Анатольевна Федорова
article en

Abstract

Relevance. The digital transformation of the economy and the growing volume of information necessitate the development of new tools for monitoring scientific and technological progress. Purpose. This study aims to provide a quantitative description of the scale, sectoral structure, temporal dynamics, geographical distribution, and corporate ownership of international digital technology patenting over the period 2000–2024. Methodology. The dataset was compiled using the Google Patents Public Datasets and covers eleven subclasses of the Cooperative Patent Classification (CPC). It consolidates the English-language titles and abstracts of related publications concerning the same invention. The study employs statistical, structural, and textual analysis methods using object-oriented and process-oriented dictionaries. Results. From a dataset of 16.0 million publications, 5.23 million digital patents dated 2000–2024 were identified. Over the study period, the annual number of patents increased from 65,700 to 541,200, with 46.78% of the patents in the sample falling within the 2020–2024 period. Geographically, 51.1% of the patents are concentrated in ten countries, including the United States, South Korea, China, and Japan. Textual analysis revealed the predominance of cross-sectoral patents in the invention-description category (89.4%). In terms of sector-specific trends, the number of patents in the artificial intelligence subclass (G06N) increased 238-fold. At the corporate level, the analysis demonstrated the predominance of patents originating from Asian countries, alongside a high degree of specialisation in patent portfolios. Practical implications. The findings and classification algorithms developed in this study can be used to forecast scientific and technological progress. Changes in the CPC structure and the growing share of multidisciplinary patents indicate a transition to the active phase of the Fourth Industrial Revolution.

Accounting Analysis AuditingVol. 13(5)
Financial University (RU)
Openalex Percentile: Top 7%
Intellectual Property and Patents
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