A novel patent technology characterization method based on heterogeneous network message passing algorithm and patent classification system

Patents are widely recognized as important data for generating innovation. Accurate and rational characterizing patent technology content is a prerequisite for applying innovation generation algorithms. Existing research widely employs classification codes that annotate the technology content of patents to represent patents. However, the oversight of technology similarity information within patent classification systems results in deficiencies in the accuracy and effectiveness of their representations. To fill this research gap, we analyze the hierarchical structure of patent classification systems to extract the technology similarity information embedded within them. Then, we propose a novel patent technology characterization method based on the heterogeneous network message passing algorithm, which integrates the technology similarity information in the classification code co-occurrence information and the patent classification system to obtain a more accurate patent characterization. Subsequently, several evaluation experiments were conducted to compare our method with typical existing methods. The results demonstrate that our method outperforms these methods in accuracy and effectiveness. Finally, we conducted a case study to validate the reliability and practicality of our approach. In summary, our method exhibited superior performance, thereby providing robust support for innovation generation methods based on patent characterization, with high application value and extension prospects.