Potential Analysis of Technological Value in the Intelligent Connected Vehicles Field from the Patent Licensing Perspective

Patent licensing is essential for sustainable technological diffusion, fostering innovation and strengthening industrial resilience. However, the determinants influencing patent licensing decisions remain underexplored. This study investigates these factors at both the enterprise and patent levels, emphasizing their role in promoting sustainable industrial innovation and knowledge transfer. Given the low proportion of licensed patents, this research proposes a measurement framework to identify thematically similar but unlicensed patents and applies a conditional logistic regression model to analyze the factors affecting licensing decisions. Using patent abstracts from the intelligent connected vehicles (ICVs) sector, topic modeling is conducted to classify technological themes, and Kullback–Leibler divergence is applied to measure differences between licensed and unlicensed patents. The results indicate that technological prestige and depth negatively influence licensing, whereas technological breadth, advancement, and stability have a positive effect. From a sustainability perspective, enterprises should optimize technology management to support responsible knowledge transfer and green innovation. Universities should enhance patent quality and innovation impact to contribute more effectively to sustainable development. Policymakers should refine patent licensing frameworks to foster an efficient, inclusive, and sustainable intellectual property ecosystem, thereby facilitating cross-sectoral technology diffusion, advancing eco-friendly industrial transformation, and promoting sustainable economic growth.