Abstract: Exploring the construction of artificial intelligence sleeping patent identification model and mining potential patents with market value is of great significance to improve the patent operation efficiency and promote the transformation of industrial digital intelligence. Based on China′s AI invention patent data, this paper conducted a holistic analysis of the patent operation situation and sleeping characteristics, and used the AdaBoost algorithm and SHAP interpretation method to construct a multi-featured fusion sleeping patent identification model for an empirical analysis. The results showed that (1) the number of Chinese AI operation patents shows a steady growth trend, and the overall sleep characteristics of "short sleep duration, flexible wake-up mechanism, and low wake-up intensity"; (2) the sleeping patent identification model based on AdaBoost algorithm has the best performance, and the inclusion of algorithmic features in the metrics system can significantly improve the identification accuracy of the model; (3) different patent features have interactive effects on the awakening of sleeping patents, and AI patents in the field of transportation and electronic equipment manufacturing have high potential for market operations; and (4) the recall rate of prediction results in the application scenarios is 0.982, and the proportion of patents with market value is 21.23%, which verifies the validity of the model. The above conclusions will enrich the research system of identification and evaluation of sleeping patents, and provide practical solutions for optimizing the patent operation decision-making of innovation subjects.

Key words: artificial intelligence, sleeping patent, AdaBoost algorithm, SHAP interpretation method, patent identification, patent operation