{"type":"ScholarlyArticle","title":"Patent Litigation Prediction: A Convolutional Tensor Factorization Approach","authors":[{"name":"Qi Liu","slug":"qi-liu","given":"Qi","family":"Liu","affiliation":"† Anhui Province Key Lab. of Big Data Analysis and Application , University of S&T of China"},{"name":"Han Wu","slug":"han-wu","given":"Han","family":"Wu","affiliation":"† Anhui Province Key Lab. of Big Data Analysis and Application , University of S&T of China"},{"name":"Yuyang Ye","slug":"yuyang-ye","given":"Yuyang","family":"Ye","affiliation":"† Anhui Province Key Lab. of Big Data Analysis and Application , University of S&T of China"},{"name":"Hongke Zhao","slug":"hongke-zhao","given":"Hongke","family":"Zhao","affiliation":"† Anhui Province Key Lab. of Big Data Analysis and Application , University of S&T of China"},{"name":"Chuanren Liu","slug":"chuanren-liu","given":"Chuanren","family":"Liu","affiliation":"‡ Decision Sciences & MIS Department , Drexel University"},{"name":"Dongfang Du","slug":"dongfang-du","given":"Dongfang","family":"Du","affiliation":"† Anhui Province Key Lab. of Big Data Analysis and Application , University of S&T of China"}],"abstract":"Patent litigation is an expensive legal process faced by many companies. To reduce the cost of patent litigation, one effective approach is proactive management based on predictive analysis. However, automatic prediction of patent litigation is still an open problem due to the complexity of lawsuits. In this paper, we propose a data-driven framework, Convolutional Tensor Factorization (CTF), to identify the patents that may cause litigations between two companies. Specifically, CTF is a hybrid modeling approach, where the content features from the patents are represented by the Network embedding-combined Convolutional Neural Network (NCNN) and the lawsuit records of companies are summarized in a tensor, respectively. Then, CTF integrates NCNN and tensor factorization to systematically exploit both content information and collaborative information from large amount of data. Finally, the risky patents will be returned by a learning to rank strategy. Extensive experimental results on real-world data demonstrate the effectiveness of our framework.","doi":"10.24963/ijcai.2018/701","url":"https://ijcai.org/proceedings/2018/701","pdf":"https://ijcai.org/proceedings/2018/0701.pdf","markdown":"https://ijcai.org/proceedings/2018/701.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/701","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Uncertainty in AI","track":"Main track","number":701,"pages":{"first":5052,"last":5059},"keywords":["Multidisciplinary Topics and Applications: Recommender Systems","Uncertainty in AI: Graphical Models","Machine Learning Applications: Other Applications"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal, 2016] Charu C Aggarwal. 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