Development of a Patent Matching System Using a Hybrid Approach
Su-Houn Liu, Hsiu-Li Liao, Chou-Chih Hsieh · Advances in intelligent systems research/Advances in Intelligent Systems Research · 2006
There were many researches about applying various data mining or text mining tools to patent analysis, and there were many scholars and experts have verified the accuracy and the feasibility of those tools.However, since mining tools always tried to analyze the content using some mathematic methodology, such as linguistic algorithms, they neglect the fact that patent records are combinations of both structured and non-structured data; it contains not only the non-structured descriptive text but also many structured data related to each patent, such as inventors, assignees and citation information… etc.In another word, mining methodology tent to neglect this import features of patent records and handled them as pure text.This paper proposes a hybrid approach to conduct patent matching process.In this study, an experimental prototype call PMS (Patent Matching System) was developed by composing both data matching and mining approach.By entering several origin patents, the PMS will scan the patent database to generate a similarity ranking table, and then patents that most similar to those origin patents will be suggested to the user.As our sample testing reveals, the PMS achieved a remarkable patent matching capability, and show potential for further improvement.