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Support Vector Machine to Forecast Reexamination Invalidation Decisions for Utility Model Patent

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  • Mei‐Hsin Wang
  • Hui‐Chung Che

Abstract

There are 21,999 China utility model patents with existing decisions of invalidation reexamination from 2000 to 2021 to explore application of support vector machine (SVM) with Gaussian radial basis function (RBF) kernel. This study identified significant patent indicators using analysis of variance (ANOVA), Kruskal–Wallis test, and Jonckheere–Terpstra ordered‐alternatives test and employed SVM incorporating significant patent indicators to forecast decision of invalidation reexamination with highest accuracy for patents with fully invalid claims. The study confirmed SVM with RBF to forecast patent sustainability and providing support for due diligence in mergers and acquisitions and litigation strategies.

Suggested Citation

  • Mei‐Hsin Wang & Hui‐Chung Che, 2026. "Support Vector Machine to Forecast Reexamination Invalidation Decisions for Utility Model Patent," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(1), pages 241-259, January.
    • Handle: RePEc:wly:jforec:v:45:y:2026:i:1:p:241-259
    DOI: 10.1002/for.70033
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