Patent network analysis of core technology groups and convergence hubs in generative-AI-enabled digital transformation

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.