Identifying patent infringement using SAO based semantic technological similarities |
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Authors: | Hyunseok Park Janghyeok Yoon Kwangsoo Kim |
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Affiliation: | (1) Department of Technology and Innovation Management, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, Pohang, Kyungbuk, 790-784, Republic of Korea;(2) Department of Industrial and Management Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, Pohang, Kyungbuk, 790-784, Republic of Korea |
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Abstract: | 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. |
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