  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.
