Modeling and forecasting forward citations to a patent is a central task for
the discovery of emerging technologies and for measuring the pulse of inventive
progress. Conventional methods for forecasting these forward citations cast the
problem as analysis of temporal point processes which rely on the conditional
intensity of previously received citations. Recent approaches model the
conditional intensity as a chain of recurrent neural networks to capture memory
dependency in hopes of reducing the restrictions of the parametric form of the
intensity function. For the problem of patent citations, we observe that
forecasting a patent's chain of citations benefits from not only the patent's
history itself but also from the historical citations of assignees and
inventors associated with that patent. In this paper, we propose a
sequence-to-sequence model which employs an attention-of-attention mechanism to
capture the dependencies of these multiple time sequences. Furthermore, the
proposed model is able to forecast both the timestamp and the category of a
patent's next citation. Extensive experiments on a large patent citation
dataset collected from USPTO demonstrate that the proposed model outperforms
state-of-the-art models at forward citation forecasting