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.
05/22/2019 ∙ by Taoran Ji, et al. ∙ 2 ∙ share
This paper presents SAFEBIKE, a novel route recommendation system for bike-sharing service that utilizes station information to infer the number of available bikes in dock and recommend bike routes according to multiple factors such as distance and safety level. The system consists of a station level availability predictor that predicts bikes and docks amount at each station, and an efficient route recommendation service that considers safety and bike/dock availability factors. It targets users who are concerned about route safeness and station availability. We demonstrate the system by utilizing Citi Bike station availability and New York City crime data of Manhattan to show the effectiveness of our approach. Integrated with real-time station availability and historical crime data resources, our proposed system can effectively recommend an optimal bike route and improve the travel experience of bike users.
12/05/2017 ∙ by Weisheng Zhong, et al. ∙ 0 ∙ share
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