Patent network analysis of core technology groups and convergence hubs in generative-AI-enabled digital transformation
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Abstract
Generative AI (GenAI) is redefining the technological architecture of enterprise digital transformation, yet its domain structure and convergence topology remain uncharacterized at the patent level. This study applies a three-stage patent analytics pipeline- Cooperative Patent Classification domain mapping, keyword-rule topic assignment across 15 GenAI application archetypes, and weighted co-occurrence network analysis- to 9,692 Lens.org patent records from ten jurisdictions, of which 7,623 published from 2018 onward form the analytical corpus. Filings grew at a compound annual rate of 42% over 2018–2025. Domain mapping identifies Computing & Information Systems (69.5%), AI & Machine Learning (36.5%) and Business & Commerce (25.2%) as a three-layer infrastructure, model and application stack. Large language model development and retrieval-augmented generation together account for 64.5% of filings, while agentic AI and edge inference clusters remain under 1% each. The co-occurrence network (26 nodes, 151 edges, density 0.465) identifies Computing & Information Systems as the dominant convergence hub (composite score 0.759) and Healthcare Informatics as a structural broker (betweenness 0.138) disproportionate to its filing volume. Cross-domain patenting peaked at 2.34 domains per patent in 2021 and moderated to 1.65 by 2025. The findings extend general-purpose technology convergence theory to GenAI and provide a structural map for technology managers.
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© 2026 The authors. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License.
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