Application of the Honeybee Mating Optimization Algorithm to Patent Document Classification in Combination with the Support Vector Machine

Abstract

Patent rights have the property of exclusiveness. Inventors can protect their rights in the legal range and have monopoly for their open inventions. People are not allowed to use an invention before the inventors permit them to use it. Companies try to avoid the research and development investment in inventions that have been protected by patent. Patent retrieval and categorization technologies are used to uncover patent information to reduce the cost of torts. In this research, we propose a novel method which integrates the Honey-Bee Mating Optimization algorithm with Support Vector Machines for patent categorization. First, the CKIP method is utilized to extract phrases of the patent summary and title. Then we calculate the probability that a specific key phrase contains a certain concept based on Term Frequency - Inverse Document Frequency (TF-IDF) methods. By combining frequencies and the probabilities of key phases generated by using the Honey-Bee Mating Optimization algorithm, our proposed method is expected to obtain better representative input values for the SVM model. Finally, this research uses patents from Chemical Mechanical Polishing (CMP) as case examples to illustrate and demonstrate the superior results produced by the proposed methodology.

Keywords

• Honey-Bee Mating Optimization
• Support Vectors Machine
• TF-IDF
• Document Categorization