Mapping the technological evolution of generative AI: a patent network analysis

This study systematically maps the technological evolution and innovation landscape of Generative Artificial Intelligence (GAI) through a large-scale analysis of patent networks. Leveraging advanced text mining, network modeling, and community detection techniques on over 20,000 patents from Lens.org, we identify key technological domains, trace conceptual shifts, and highlight emerging trends across three major periods (pre-2016, 2016-2020, 2021-2025). Results reveal a pivotal transition post-2016 from early modular and domain-specific innovations, such as neuromorphic computing and bioengineering, toward integrated generative frameworks, API-driven platforms, and multi-modal capabilities. The post-2016 era is characterized by rapid growth in patent volume, increased conceptual diversity, and diminishing modularity, reflecting the convergence of previously distinct subfields. Key emergent clusters include generative AI frameworks, advanced medical imaging, personalized interactive systems, media authentication, and privacy-preserving AI. Notably, growing attention to content authenticity and user personalization underscores the interplay between technological maturation and societal concerns. Comparative analysis with baseline and alternative clustering models demonstrates the robustness and interpretability of the chosen network approach. While limited by reliance on English-language patents and TF-IDF extraction, this work provides an actionable roadmap for R&D leaders, policymakers, and investors to navigate the dynamic GAI landscape and informs future research directions involving multilingual and semantic-rich analyses.