The state of AI and intellectual property – a thematic scientometric assessment

Abstract This study presents a comprehensive scientometric analysis of the scholarly discourse on artificial intelligence and intellectual property law between 2013 and 2023. Drawing on metadata from Scopus, the research employs advanced bibliometric tools to map trends, publication patterns, and thematic clusters in the field. Our results reveal a dramatic surge in academic output post-2019, showing an accelerated scholarly interest, especially in light of generative AI’s emergence. The CiteSpace-based analysis identifies three dominant research clusters: (1) AI and Copyright Law, focusing on authorship, originality, and text/data mining; (2) AI and Digital Rights, exploring identity, privacy, and ethical dilemmas; and (3) AI and Patent Law addressing inventorship, patentability, and system reform. Furthermore, we also expose geographical disparities, with the United States and United Kingdom dominating the field of AI and IP law, alongside severe under-representation of Global South–affiliated authors within Scopus-indexed, English-language journal publications. A key finding is that despite growing regulatory responses, such as the EU AI Act and US Copyright Office guidelines, legal consensus remains elusive. In sum, the article at hand aims to offer a state-of-the-field mapping of Scopus-indexed scholarship at the AI–IP interface via consolidating the field’s “visible contours,” while identifying underexplored thematic and geographic areas for future interdisciplinary inquiry.

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Publication Details

Journal
SN Social Sciences
Published
2026-06-08
DOI
https://doi.org/10.1007/s43545-026-01479-5
Primary Topic
Law, AI, and Intellectual Property
Type
article
Field-Weighted Citation Impact
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article

The state of AI and intellectual property – a thematic scientometric assessment

Anett Pogacsas, Peter Mezei, Gergely Ferenc Lendvai
SN Social Sciences
Law, AI, and Intellectual Property
article

The state of AI and intellectual property – a thematic scientometric assessment

Anett Pogacsas, Peter Mezei, Gergely Ferenc Lendvai
article en

Abstract

Abstract This study presents a comprehensive scientometric analysis of the scholarly discourse on artificial intelligence and intellectual property law between 2013 and 2023. Drawing on metadata from Scopus, the research employs advanced bibliometric tools to map trends, publication patterns, and thematic clusters in the field. Our results reveal a dramatic surge in academic output post-2019, showing an accelerated scholarly interest, especially in light of generative AI’s emergence. The CiteSpace-based analysis identifies three dominant research clusters: (1) AI and Copyright Law, focusing on authorship, originality, and text/data mining; (2) AI and Digital Rights, exploring identity, privacy, and ethical dilemmas; and (3) AI and Patent Law addressing inventorship, patentability, and system reform. Furthermore, we also expose geographical disparities, with the United States and United Kingdom dominating the field of AI and IP law, alongside severe under-representation of Global South–affiliated authors within Scopus-indexed, English-language journal publications. A key finding is that despite growing regulatory responses, such as the EU AI Act and US Copyright Office guidelines, legal consensus remains elusive. In sum, the article at hand aims to offer a state-of-the-field mapping of Scopus-indexed scholarship at the AI–IP interface via consolidating the field’s “visible contours,” while identifying underexplored thematic and geographic areas for future interdisciplinary inquiry.

SN Social SciencesVol. 6(6)
Ludovika University of Public Service (HU), Vytautas Magnus University (LT), University of Szeged (HU), Pázmány Péter Catholic University (HU)
Peace, Justice and strong institutions
Openalex Percentile: Top 4%
Law, AI, and Intellectual Property
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The state of AI and intellectual property – a thematic scientometric assessment — Anett Pogacsas, Peter Mezei, et al. · SN Social Sciences (2026) | TGRS Research Map | TGRS