Emerging Technologies Based on Large AI Models and the Design of Support Policies: Patent Mining and Industry Trends

This study develops an integrated analytical framework that connects the early identification of emerging technologies with the design of targeted support policies. Leveraging large AI models and multi-source data—including global patent databases (e.g., WIPO, USPTO, Lens.org), scientific literature corpora, and industry intelligence platforms (e.g., CB Insights, Qichacha)—the research applies advanced techniques such as LDA topic modelling, BERT-based clustering, and co-citation analysis to detect innovation trajectories. Technologies such as AI-driven healthcare, quantum communication, hydrogen energy, and smart educational AI are identified as key domains of convergence. Temporal mapping and citation networks reveal distinct technology maturity patterns, which are visualised using S-curve and hype cycle models. These insights are triangulated with market data and sentiment analysis, confirming that public enthusiasm often outpaces actual technological readiness. A technology maturity classification categorises innovations into emerging, developing, and mature stages, forming the basis for strategic policy matching. To validate and prioritise policy instruments, Delphi rounds with domain experts and Analytic Hierarchy Process (AHP) weighting are employed. The resulting policy matrix includes R&D funding, regulatory sandboxes, public procurement incentives, and tax relief, tailored to each stage of technological evolution. The study concludes that a data-driven, foresight-based approach to policy design significantly enhances responsiveness, precision, and resource efficiency in science and technology governance. This framework offers a replicable model for governments and institutions seeking to proactively support high-potential innovations across sectors.