Patent Litigation Prediction: A Convolutional Tensor Factorization Approach

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.

[1]  Chia-Yi Liu,et al.  The relationship between patent attributes and patent litigation: Considering the moderating effects of managerial characteristics , 2017, Asia Pacific Management Review.

[2]  L. Christophorou Science , 2018, Emerging Dynamics: Science, Energy, Society and Values.

[3]  Lars Schmidt-Thieme,et al.  BPR: Bayesian Personalized Ranking from Implicit Feedback , 2009, UAI.

[4]  Enhong Chen,et al.  A Context-Enriched Neural Network Method for Recognizing Lexical Entailment , 2017, AAAI.

[5]  John C. Platt,et al.  Learning Discriminative Projections for Text Similarity Measures , 2011, CoNLL.

[6]  Donghyun Kim,et al.  Convolutional Matrix Factorization for Document Context-Aware Recommendation , 2016, RecSys.

[7]  MU-HSUAN HUANG,et al.  Constructing a patent citation map using bibliographic coupling: A study of Taiwan's high-tech companies , 2003, Scientometrics.

[8]  Wim Vanderbauwhede,et al.  Search system requirements of patent analysts , 2010, SIGIR '10.

[9]  Mark A. Schankerman,et al.  Characteristics of patent litigation: a window on competition , 2001 .

[10]  J. Meigs,et al.  WHO Technical Report , 1954, The Yale Journal of Biology and Medicine.

[11]  Ruiyun Yu,et al.  Minimizing Legal Exposure of High-Tech Companies through Collaborative Filtering Methods , 2016, KDD.

[12]  Jiyoun Lim Analysis of the Relationship between Patent Litigation and Citation: Subdivision of Citations , 2014 .

[13]  Hui Xiong,et al.  Mining Indecisiveness in Customer Behaviors , 2015, 2015 IEEE International Conference on Data Mining.

[14]  Nuria Oliver,et al.  Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering , 2010, RecSys '10.

[15]  Phil Blunsom,et al.  A Convolutional Neural Network for Modelling Sentences , 2014, ACL.

[16]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[17]  ScienceDirect World patent information , 1979 .

[18]  Steven Skiena,et al.  DeepWalk: online learning of social representations , 2014, KDD.

[19]  Hao Wang,et al.  Patent Quality Valuation with Deep Learning Models , 2018, DASFAA.

[20]  Matthew D. Zeiler ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[21]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[22]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[23]  Xin Jin,et al.  Patent Maintenance Recommendation with Patent Information Network Model , 2011, 2011 IEEE 11th International Conference on Data Mining.

[24]  Yelong Shen,et al.  Learning semantic representations using convolutional neural networks for web search , 2014, WWW.

[25]  Alan C. Marco,et al.  Patent Examination Quality and Litigation: Is There a Link? , 2017, International Journal of the Economics of Business.

[26]  Scott Duke Kominers,et al.  The growing problem of patent trolling , 2016, Science.

[27]  Han Tong Loh,et al.  Automatic classification of patent documents for TRIZ users , 2006 .

[28]  Hui Xiong,et al.  Personalized Travel Package Recommendation , 2011, 2011 IEEE 11th International Conference on Data Mining.

[29]  Yoav Goldberg,et al.  A Primer on Neural Network Models for Natural Language Processing , 2015, J. Artif. Intell. Res..

[30]  W. M. Campbell,et al.  Predicting and Analyzing Factors in Patent Litigation , 2016 .

[31]  W. Scott Spangler,et al.  COA: finding novel patents through text analysis , 2009, KDD.

[32]  Tao Li,et al.  Patent Mining: A Survey , 2015, SKDD.

[33]  Enhong Chen,et al.  Question Difficulty Prediction for READING Problems in Standard Tests , 2017, AAAI.