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 modelling companies' continuously evolving preferences and capturing the semantic correlations of classification codes. To fill in 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.

翻译：准确预测企业下一阶段将申请何种类型的专利，能够揭示其发展战略，并帮助其提前发现潜在合作伙伴或竞争对手。尽管这一问题至关重要，但由于难以建模企业持续演变的偏好以及捕捉分类代码的语义关联，此前研究鲜有涉及。为弥补这一空白，我们提出了一种基于事件的动态图学习框架，用于专利申请趋势预测。具体而言，我们的方法建立在企业和专利分类代码的可记忆表示之上。当观察到新专利时，相关企业和分类代码的表示将根据历史记忆和当前编码信息进行更新。此外，我们设计了一种分层消息传递机制，通过沿分层分类体系更新专利分类代码的表示，来捕捉其语义邻近性。最终，通过从静态、动态和分层三个角度聚合目标企业与分类代码的表示，实现对专利申请趋势的预测。真实数据实验表明，我们的方法在各种实验条件下均具有有效性，并展示了其在学习分类代码语义及追踪企业技术发展轨迹方面的能力。