On the Patent Claim Eligibility Prediction Using Text Mining Techniques

Date
2018-01-03
Authors
Lai, Chia-Yu
Hwang, San-Yih
Wei, Chih-Ping
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With the widespread of computer software in recent decades, software patent has become controversial for the patent system. Of the many patentability requirements, patentable subject matter serves as a gatekeeping function to prevent a patent from preempting future innovation. Software patents may easily fall into the gray area of abstract ideas, whose allowance may hinder future innovation. However, without a clear definition of abstract ideas, determining the patent claim subject matter eligibility is a challenging task for examiners and applicants. In this research, in order to solve the software patent eligibility issues, we propose an effective model to determine patent claim eligibility by text-mining and machine learning techniques. Drawing upon USPTO issued guidelines, we identify 66 patent cases to design domain knowledge features, including abstractness features and distinguishable word features, as well as other textual features, to develop the claim eligibility prediction model. The experiment results show our proposed model reaches the accuracy of more than 80%, and domain knowledge features play a crucial role in our prediction model.
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Text Mining in Big Data Analytics, patent analysis, text-mining, patent claim, claim eligibility
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10 pages
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Proceedings of the 51st Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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