The Markov-modulated Poisson process (MMPP) cookbook
Performance Evaluation
COA: finding novel patents through text analysis
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Distributed Algorithms for Topic Models
The Journal of Machine Learning Research
Latent graphical models for quantifying and predicting patent quality
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Patent Maintenance Recommendation with Patent Information Network Model
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
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Patents are critical for a company to protect its core technologies. Effective patent mining in massive patent databases can provide companies with valuable insights to develop strategies for IP management and marketing. In this paper, we study a novel patent mining problem of automatically discovering core patents (i.e., patents with high novelty and influence in a domain). We address the unique patent vocabulary usage problem, which is not considered in traditional word-based statistical methods, and propose a topic-based temporal mining approach to quantify a patent's novelty and influence. Comprehensive experimental results on real-world patent portfolios show the effectiveness of our method.