Machine-Learning Drives Patent Analysis

As business intelligence professionals increasingly rely on accurate patent landscapes to inform technology forecasting, machine learning (ML) is being called on to aid in the analytical process. In the recently published "Parameter tuning Naïve Bayes for automatic patent classification", Cassidy provides an analysis of available settings for automatic patent categorization. A modified Naïve Bayes classifier assigns International Patent Classification (IPC) section codes for a selection of 7,309 patent applications from the World Patent Information (WPI) Test Collection (Lupu, 2019). Several measures of accuracy are compared for a variety of meta-parameter settings including data smoothing and acceptance threshold. The optimized model is also applied to IPC class and group codes and the results for patent categorization are compared to classification of academic literature.

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