Patent classification system using a new hybrid genetic algorithm support vector machine
Nowadays, decision-making activities of knowledge-intensive enterprises depend heavily on the successful classification of patents. A considerable amount of time is required to achieve successful classification because of the complexity associated with patent information and of the large number of potential patents. Several different patent classification approaches have been developed in the past, but most of these studies focus on using computational models for the International Patent Classification (IPC) system rather than using these models in real-world cases of patent classification. In contrast to previous studies that combined algorithms and the IPC system directly without using expert screening, this study proposes a novel artificial intelligence (AI)-aided patent decision-making process. In this process, an expert screening approach is integrated with a hybrid genetic-based support vector machine (HGA-SVM) model for developing a patent classification system with the high classification accuracy and generalization ability for real-world patent searching cases. The proposed approach is tested on a real-world casean expert's patent document searching history that contains 234 patent documents of semiconductor equipment components. The research results demonstrate that our proposed hybrid genetic algorithm approach can optimize all the parameters of the SVM for developing a patent classification system with a high accuracy. The proposed HGA-SVM model is able to dynamically and automatically classify patent documents by recording and learning the experts knowledge and logic. Finally, we propose a new decision-making process for improving the development of the SVM patent classification and searching system.
patent classification hybrid genetic-based support vector machine (hga-svm) parameter optimization