Improving Product-related Patent Information Access with Automated Technology Ontology Extraction

Abstract

This thesis focused on patent text mining and knowledge reuse for product design and development. With the increase in the number of issued patents and the enhancement of people?s patent awareness, patent disputes become more and more frequent. To facilitate information reuse and avoid patent infringement, this thesis defined a new ontology, called technology ontology and proposed a framework to utilize the technology ontology. Two challenges were addressed: technology ontology extraction and invention comparison. The automated model extraction was treated as a Named Entity Recognition problem and a parsing problem, respectively. The Named Entity Recognition system was recognized in a cutting edge patent information access evaluation. To realize patent claim parsing, a new dependency grammar framework was proposed. It makes efficient and effective claim parsing possible. For the invention comparison, a new graph similarity measure was proposed. The proposed similarity measure can overcome the weakness of previous graph similarity measures. Moreover, it demonstrates its superiority in a patent classification problem. Two applications were given. The first application is an effect-oriented patent search engine, which offers more focused search results than conventional patent search engine. The second application is a patent visualization tool attached to the effect-oriented patent search engine. It is able to automatically generate patent growth map that groups inventions and facilitates selection of core inventions.