On the patent claim eligibility prediction using text mining techniques
Journal
Proceedings of the Annual Hawaii International Conference on System Sciences
Journal Volume
2018-January
Pages
587-596
ISBN
9780998133119
Date Issued
2018-01-01
Author(s)
Abstract
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.
Description
51st Annual Hawaii International Conference on System Sciences, HICSS 2018, Big Island, from 2 January 2018 to 6 January 2018
Type
conference paper