Exploring Intellectual Property Trends in Smart Cataloguing and Classification

This study explores intellectual property (IP) trends in smart cataloguing and classification technologies, emphasizing how artificial intelligence (AI), machine learning, and semantic analysis drive innovation in knowledge organization systems. It aims to uncover global patterns of patenting, technological focus, corporate strategies, and emerging research priorities.

A comprehensive patent analytics approach was employed using 2,175 patents (2000–2024) retrieved from Lens.org. The study applied descriptive statistics, CPC classification analysis, citation mapping, and legal status evaluation to assess trends in technology development, corporate and geographic contributions, patent activity, and scholarly influence on innovation.

Results indicate rapid growth in patent activity, particularly after 2016, reflecting breakthroughs in AI, deep learning, and natural language processing. The U.S., China, and India lead in patent filings, while emerging hubs such as Israel and Ireland show increasing contributions. Machine learning (G06N20/00) and neural networks (G06N3/08) dominate technological focus, with applications spanning healthcare, visual recognition, and multimodal classification. The high number of patent applications relative to grants highlights a dynamic innovation pipeline. Strategic implications include guiding R&D investments, prioritizing interdisciplinary collaboration, and supporting policy decisions on IP infrastructure, knowledge management, and commercialization of emerging cataloguing technologies.

The study relies on patent based analysis, without empirical assessment of adoption or market outcomes.

By linking patent trends with technological, corporate, and geographic insights, the study informs policymakers, research institutions, and industry leaders on how to strategically manage IP, foster innovation, and shape the future of AI-driven smart cataloguing systems.