CLUSTERING PATENTS USING NON-EXHAUSTIVE OVERLAPS

Abstract

Patent documents are unique external sources of information that reveal the core technology underlying new inventions. Patents also serve as a strategic data source that can be mined to discover state-of-the-art technical development and subsequently help guide R&D investments. This research incorporates an ontology schema to extract and represent patent concepts. A clustering algorithm with non-exhaustive overlaps is proposed to overcome deficiencies with exhaustive clustering methods used in patent mining and technology discovery. The non-exhaustive clustering approach allows for the clustering of patent documents with overlapping technical findings and claims, a feature that enables the grouping of patents that define related key innovations. Legal advisors can use this approach to study potential cases of patent infringement or devise strategies to avoid litigation. The case study demonstrates the use of non-exhaustive overlaps algorithm by clustering US and Japan radio frequency identification (RFID) patents and by analyzing the legal implications of automated discovery of patent infringement.