Identifying patent infringement using SAO based semantic technological similarities

• Published in: Scientometrics Vol. 90; no. 2; pp. 515 - 529
• Main Authors: Park, Hyunseok, Yoon, Janghyeok, Kim, Kwangsoo
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.02.2012; Springer
• Subjects: Bibliometrics. Scientometrics; Bibliometrics. Scientometrics. Evaluation; Computer Science; Data mining; Detection; Exact sciences and technology; Information and communication sciences; Information science. Documentation; Information Storage and Retrieval; Infringements; Library and information science. General aspects; Library Science; Natural language processing; Sciences and techniques of general use; Semantic relations; SAO; C63; NLP; Patent litigation; Patent mining; Natural language processing; Patent risk; C82; Subject–action–object; Multidimensional scaling; Patent analysis; Patents; Similarity; Correlation analysis; Multidimensional space; Scientific research; Cluster; Measurement method; Algorithm
• ISSN: 0138-9130; 1588-2861
• DOI: 10.1007/s11192-011-0522-7

Companies should investigate possible patent infringement and cope with potential risks because patent litigation may have a tremendous financial impact. An important factor to identify the possibility of patent infringement is the technological similarity among patents, so this paper considered technological similarity as a criterion for judging the possibility of infringement. Technological similarities can be measured by transforming patent documents into abstracted forms which contain specific technological key-findings and structural relationships among technological components in the invention. Although keyword-based technological similarity has been widely adopted for patent analysis related research, it is inadequate for identifying patent infringement because a keyword vector cannot reflect specific technological key-findings and structural relationships among technological components. As a remedy, this paper exploited a subject–action–object (SAO) based semantic technological similarity. An SAO structure explicitly describes the structural relationships among technological components in the patent, and the set of SAO structures is considered to be a detailed picture of the inventor’s expertise, which is the specific key-findings in the patent. Therefore, an SAO based semantic technological similarity can identify patent infringement. Semantic similarity between SAO structures is automatically measured using SAO based semantic similarity measurement method using WordNet, and the technological relationships among patents were mapped onto a 2-dimensional space using multidimensional scaling (MDS). Furthermore, a clustering algorithm is used to automatically suggest possible patent infringement cases, allowing large sets of patents to be handled with minimal effort by human experts. The proposed method will be verified by detecting real patent infringement in prostate cancer treatment technology, and we expect this method to relieve human experts’ work in identifying patent infringement.