2012 PatentMinerTopicDrivenPatentAna

• (Tang et al., 2012) ⇒ Jie Tang, Bo Wang, Yang Yang, Po Hu, Yanting Zhao, Xinyu Yan, Bo Gao, Minlie Huang, Peng Xu, Weichang Li, and Adam K. Usadi. (2012). “PatentMiner: Topic-driven Patent Analysis and Mining.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339741

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+PatentMiner%3A+Topic-driven+Patent+Analysis+and+Mining
• http://dl.acm.org/citation.cfm?id=2339530.2339741&preflayout=flat#citedby

Quotes

Author Keywords

• Company ranking; competitor analysis; database applications; information search and retrieval; patent analysis; social network

Abstract

Patenting is one of the most important ways to protect company's core business concepts and proprietary technologies. Analyzing large volume of patent data can uncover the potential competitive or collaborative relations among companies in certain areas, which can provide valuable information to develop strategies for intellectual property (IP), R&D, and marketing. In this paper, we present a novel topic-driven patent analysis and mining system. Instead of merely searching over patent content, we focus on studying the heterogeneous patent network derived from the patent database, which is represented by several types of objects (companies, inventors, and technical content) jointly evolving over time. We design and implement a general topic-driven framework for analyzing and mining the heterogeneous patent network. Specifically, we propose a dynamic probabilistic model to characterize the topical evolution of these objects within the patent network. Based on this modeling framework, we derive several patent analytics tools that can be directly used for IP and R&D strategy planning, including a heterogeneous network co-ranking method, a topic-level competitor evolution analysis algorithm, and a method to summarize the search results. We evaluate the proposed methods on a real-world patent database. The experimental results show that the proposed techniques clearly outperform the corresponding baseline methods.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 PatentMinerTopicDrivenPatentAna	Jie Tang Bo Gao Minlie Huang Bo Wang Yang Yang Po Hu Yanting Zhao Xinyu Yan Peng Xu Weichang Li Adam K. Usadi			PatentMiner: Topic-driven Patent Analysis and Mining				10.1145/2339530.2339741		2012