Uncovering the Complexities of Intellectual Property Management in the era of AI: Insights from a Bibliometric Analysis

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.