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NSYSU

1. Lai, Chia-Yu. The Prediction of Software Patent Claim Eligibility and Patent Value using Text-mining Techniques.

Degree: PhD, Information Management, 2018, NSYSU

With the widespread of computer software in recent decades, software patent has become controversial for the patent system. Software patents may easily fall into the gray area of abstract ideas, whose allowance may hinder future innovation. However, without a precise definition of abstract ideas, determining the patent claim subject matter eligibility is a challenging task for examiners and applicants.   In this research, we address the software patent eligibility issues by proposing an effective model to determine patent claim eligibility and examine the patent examination process to predict patentability. Furthermore, with patent claim features and important prosecution events, we attempt to identify important indicators to valuable patents. We collect patent claims, patent examination records, and patent litigation data of software patents from USPTO website, USPTO PAIR, Google Patents, and MaxVal's Patent Litigation Databank. The experiment results show our patent claim eligibility model reaches the accuracy of more than 80%, and domain knowledge features play a crucial role in our prediction model. Using sequence learning on patentability, our patentability predictive model can achieve around 90% accuracy based on our time-duration features. With the value indicators identified by previous models and prior studies, the accuracy of our patent value model can reach up to 88%. Advisors/Committee Members: Duen-Ren Liu (chair), Fu-ren Lin (chair), Yen-Liang Chen (chair), San-Yih Hwang (committee member), Chih-Ping Wei (committee member).

Subjects/Keywords: text-mining; prediction; patent analysis; patent eligibility; patent value

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lai, C. (2018). The Prediction of Software Patent Claim Eligibility and Patent Value using Text-mining Techniques. (Doctoral Dissertation). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0016118-155537

Chicago Manual of Style (16th Edition):

Lai, Chia-Yu. “The Prediction of Software Patent Claim Eligibility and Patent Value using Text-mining Techniques.” 2018. Doctoral Dissertation, NSYSU. Accessed March 25, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0016118-155537.

MLA Handbook (7th Edition):

Lai, Chia-Yu. “The Prediction of Software Patent Claim Eligibility and Patent Value using Text-mining Techniques.” 2018. Web. 25 Mar 2019.

Vancouver:

Lai C. The Prediction of Software Patent Claim Eligibility and Patent Value using Text-mining Techniques. [Internet] [Doctoral dissertation]. NSYSU; 2018. [cited 2019 Mar 25]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0016118-155537.

Council of Science Editors:

Lai C. The Prediction of Software Patent Claim Eligibility and Patent Value using Text-mining Techniques. [Doctoral Dissertation]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0016118-155537

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