2011 LatentGraphicalModelsforQuantif

• (Liu et al., 2011) ⇒ Yan Liu, Pei-yun Hseuh, Rick Lawrence, Steve Meliksetian, Claudia Perlich, and Alejandro Veen. (2011). “Latent Graphical Models for Quantifying and Predicting Patent Quality.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020586

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222011%22+Latent+Graphical+Models+for+Quantifying+and+Predicting+Patent+Quality
• http://dl.acm.org/citation.cfm?id=2020408.2020586&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; data mining; design; experimentation; graphical models; measurement models; text mining

Abstract

The number of patents filed each year has increased dramatically in recent years, raising concerns that patents of questionable validity are restricting the issuance of truly innovative patents. For this reason, there is a strong demand to develop an objective model to quantify patent quality and characterize the attributes that lead to higher-quality patents. In this paper, we develop a latent graphical model to infer patent quality from related measurements. In addition, we extract advanced lexical features via natural language processing techniques to capture the quality measures such as clarity of claims, originality, and importance of cited prior art. We demonstrate the effectiveness of our approach by validating its predictions with previous court decisions of litigated patents.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 LatentGraphicalModelsforQuantif	Yan Liu Claudia Perlich Pei-yun Hseuh Rick Lawrence Steve Meliksetian Alejandro Veen			Latent Graphical Models for Quantifying and Predicting Patent Quality				10.1145/2020408.2020586		2011