Patent Analytic Citation-Based VSM: Challenges and Applications

This is the authors' abstract. We don't add key points for this paper.

Patent citations are significant components of patents, which play a vital role in the implementation of patent analysis. However, most of the existed models only focus on the text of patents and do not realize that citations can remedy missing information in the text. A method for citation modeling in patent analysis is proposed to generate patent citation trees in this paper. Correspondingly, a specific neural network is designed for extracting abstract features in patent citation trees. Then, on the basis of extracted features, a new citation-based vector space model (CVSM) combining citations with text of the patent database is constructed for the subsequent applications. An experiment is conducted based on real patents of USPTO. The experimental results show that the proposed CVSM has good performances in several applications, which demonstrate the effectiveness of the proposed CVSM.