Abstract
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-tosequence model which employs an attention-ofattention 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