| Summary: | 碩士 === 中原大學 === 資訊管理研究所 === 93 === There were many researches about applying various data and text mining tools to patent analysis, and there were many scholars and experts having verified the accuracy and the feasibility of those data and text mining methodology in patent analysis. However, data and text mining methodology handled those patents as no more than “data” or “text”, and then tried to analyze the content using some methodology, maybe some linguistic algorithms, but they neglected some import features of patent itself. The Patent Matching System (PMS) is composing of patent’s bibliography matching engine and data mining matching engine. These two engines will generate a fusion of similarity rank table by weighting model, and then the related patents will be suggested to the user.
There are evidences that patent has become very important given by increasing lawsuits of patent. Accordingly, patent has become the critical weapon on the war of knowledge-based competition. If you have the “critical technology’s patent”, it means that you have the admission ticket to the victories.
Unfortunately, it was time-consuming for patent searchers to just find out the patents what he wanted; it was not only because the mass quantity of patent, but also needed the searcher’s specialty and experience to reduce the range of necessary patents by adopting various searching methods step by step. Even so, there were still many useless patents in the reduced collection of patents, and it was really a heavy job to read patents piece by piece.
There were many researches about applying various data and text mining tools to patent analysis, and there were many scholars and experts having verified the accuracy and the feasibility of those data and text mining methodology in patent analysis. However, data and text mining methodology handled those patents as no more than “data” or “text”, and then tried to analyze the content using some methodology, maybe some linguistic algorithms, but they neglected some import features of patent itself.
Because the feature of semi-structural content of patent data, it contains many critical information about each patent, such as citation’s information and patent assignees’ information etc. Trying to put more attention on that critical information and then trying to combine the feature of those patents with data and text mining tools may derive better efficiency.
Indeed, an aim of this article is trying to propose a patent matching architecture by combining the patent’s bibliography matching model with data and text mining matching model by weighting mode. After trying to build up a patent matching system (PMS) according to that architecture, we have verified the efficiency of the PMS.
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