Patent Clustering and Outlier Ranking Methodologies for Attributed Patent Citation Networks for Technology Opportunity Discovery

Abstract

Effectively ranking patents in outlierness in a patent citation network is a crucial task for patent analysis, including as it relates to technological opportunity discovery (TOD). Previous studies in the area of TOD focus on patent textual data. In this paper, we introduce a new approach that addresses TOD via patent outlierness, leveraging both patent attributes and citations. We propose the following characteristics for patent outliers: 1) not highly clustered with other patents; 2) low node centrality within the citation network; and 3) low similarity to other patents in the network. Existing outlier ranking approaches have the drawback of not leveraging the unique characteristics of attributed patent citation networks. We propose new outlier ranking methods developed specifically for patents in attributed patent citation networks. Attribute data independently describe a patent, while citation network data relate patents to each other, thus capturing patent outlierness from two different aspects. The contributions of this paper are, given an attributed patent citation network: 1) patent clustering algorithm, and 2) method for scoring and ranking patents in outlierness. Developed methods are validated using artificial datasets. Proposed outlier ranking methods are evaluated using U.S. patents in the area of digital information and security.