Uncovering the Complexities of Intellectual Property Management in the era of AI: Insights from a Bibliometric Analysis
Cristina Blanco González-Tejero, Antonio de Lucas Ancillo, Sorin Gavrila Gavrila, Antonio García Blanco
Keywords:
Intellectual Property, Artificial Intelligence, Natural Language Processing,Machine
Learning.
Abstract:
Intellectual property (IP) management has posed continuous problems in the digital world, so
understanding its associated concepts and the particularities they present is crucial. Within
artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) have
enabled the intelligent processing and analysis of large volumes of data, making them widely
used tools. In order to help fill the research gap that exists due to the novelty of the concepts, a
bibliometric analysis is proposed of 404 scientific documents linked to AI, ML, NLP and IP,
extracted from the Web of Science (WoS) core collection repository. The results demonstrate a
current trend in research on the management of IP, related to digital tools and highlight the
issues that arise from the management of IP stemming from their use. This research also
identifies how these tools have been used to facilitate the management and identification of IP.
In this sense, this study brings originality to the field of intellectual property management by
examining previous studies and proposing new avenues for future research, thus broadening the
current understanding of the subject. Entrepreneurs and business leaders can benefit from this
study as it uncovers the complexities of IP management and thus enhances understanding of
the opportunities and challenges in the AI era.
Fulltext download:
 Uncovering the Complexities of Intellectual Property Management in the era of AI: Insights from a Bibliometric Analysis [PDF file] [Filesize: 500.78 KB]
 10.7441/joc.2023.04.05
González-Tejero, C.B., Ancillo, A., Gavrila, S.G. & Blanco, A.G. (2023). Uncovering the Complexities of Intellectual Property Management in the
era of AI: Insights from a Bibliometric Analysis. Journal of Competitiveness, 15(4), 69-86. https://doi.org/10.7441/joc.2023.04.05
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