Event-Based Dynamic Graph Representation Learning for Patent Application Trend Prediction

• Published in: IEEE transactions on knowledge and data engineering 2024-05, Vol.36 (5), p.1951-1963
• Main Authors: Zou, Tao, Yu, Le, Sun, Leilei, Du, Bowen, Wang, Deqing, Zhuang, Fuzhen
• Format: Article
• Language: English
• Subjects: Behavioral sciences; Classification; Classification codes; Codes; Companies; Company structure; dynamic representations; Graph representations; Graphical representations; hierarchical taxonomy; Learning; Message passing; patent application trend; Patent applications; Semantics; Task analysis; Taxonomy
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2023.3312333

Accurate prediction of what types of patents that companies will apply for in the next period of time can figure out their development strategies and help them discover potential partners or competitors in advance. Although important, this problem has been rarely studied in previous research due to the challenges in modeling companies' continuously evolving preferences and capturing the semantic correlations of classification codes. To fill this gap, we propose an event-based dynamic graph learning framework for patent application trend prediction. In particular, our method is founded on the memorable representations of both companies and patent classification codes. When a new patent is observed, the representations of the related companies and classification codes are updated according to the historical memories and the currently encoded messages. Moreover, a hierarchical message passing mechanism is provided to capture the semantic proximities of patent classification codes by updating their representations along the hierarchical taxonomy. Finally, the patent application trend is predicted by aggregating the representations of the target company and classification codes from static, dynamic and hierarchical perspectives. Experiments on real-world data demonstrate the effectiveness of our approach under various experimental conditions, and also reveal the abilities of our method in learning semantics of classification codes and tracking technology developing trajectories of companies.