Construction of a Prediction Model for Pharmaceutical Patentability Using Nonlinear SVM

Kei Miyaoka, Takako Akakura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Japanese Patent Act follows a first-to-file principle, so it is crucial that important patent applications must be filed earlier than those by other inventors. However, inventors will not be awarded a patent if the description of the invention in the application is insufficient. Regarding this problem, a previous study investigated use of logistic regression in a prediction model for patentability (probability of acquiring patent rights). However, that model used linear discrimination, so the discrimination accuracy was not high. To increase prediction accuracy, this study instead uses a nonlinear support vector machine (SVM) in the predictive model for patentability. Evaluation experiments using the SVM model show that the prediction accuracy of the SVM-based model is better than that of the model used in the previous research. These results suggested that a nonlinear SVM model is effective for constructing a prediction model for pharmaceutical patentability.

Original languageEnglish
Title of host publicationHuman Interface and the Management of Information. Information in Intelligent Systems - Thematic Area, HIMI 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Proceedings
EditorsHirohiko Mori, Sakae Yamamoto
PublisherSpringer Verlag
Pages244-253
Number of pages10
ISBN (Print)9783030226480
DOIs
Publication statusPublished - 1 Jan 2019
EventThematic Area on Human Interface and the Management of Information, HIMI 2019, held as part of the 21st International Conference on Human-Computer Interaction, HCI International 2019 - Orlando, United States
Duration: 26 Jul 201931 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11570 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceThematic Area on Human Interface and the Management of Information, HIMI 2019, held as part of the 21st International Conference on Human-Computer Interaction, HCI International 2019
CountryUnited States
CityOrlando
Period26/07/1931/07/19

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Keywords

  • Nonlinear
  • Patentability
  • SVM

Cite this

Miyaoka, K., & Akakura, T. (2019). Construction of a Prediction Model for Pharmaceutical Patentability Using Nonlinear SVM. In H. Mori, & S. Yamamoto (Eds.), Human Interface and the Management of Information. Information in Intelligent Systems - Thematic Area, HIMI 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Proceedings (pp. 244-253). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11570 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-22649-7_20