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论文类型：会议论文

发表时间：2016-08-13

发表刊物：Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

收录刊物：EI、Scopus

卷号：13-17-August-2016

页面范围：127-136

摘要：Patent litigation not only covers legal and technical issues, it is also a key consideration for managers of high-technology (high-tech) companies when making strategic decisions. Patent litigation inuences the market value of high-tech companies. However, this raises unique challenges. To this end, in this paper, we develop a novel recommendation framework to solve the problem of litigation risk prediction. We will introduce a specific type of patent-related litigation, that is, Section 337 investigations, which prohibit all acts of unfair competition, or any unfair trade practices, when exporting products to the United States. To build this recommendation framework, we collect and exploit a large amount of published information related to almost all Section 337 investigation cases. This study has two aims: (1) to predict the litigation risk in a specific industry category for high-tech companies and (2) to predict the litigation risk from competitors for high-tech companies. These aims can be achieved by mining historical investigation cases and related patents. Specifically, we propose two methods to meet the needs of both aims: a proximal slope one predictor and a time-aware predictor. Several factors are considered in the proposed methods, including the litigation risk if a company wants to enter a new market and the risk that a potential competitor would file a lawsuit against the new entrant. Comparative experiments using real-world data demonstrate that the proposed methods outperform several baselines with a significant margin. © 2016 ACM.