Automated classification of patents: A topic modeling approach

Abstract

Due to the rapid increase in technological innovation and corresponding increase in patent applications, automatic patent classification systems are very helpful for both individual inventors and patent attorneys in classifying patents. However, previous studies have neglected the question of what content patents include and how to represent patent content effectively in a structured form to predict the patent class. In response, this study suggests a topic model based on support vector machine (SVM) prediction for automatic patent classification. This study considers two important issues for patent classification: text representation and class prediction. For text representation, we use the topic modeling technique and employ latent Dirichlet allocation (LDA). The result of LDA is then used as the input for the second aspect: class prediction. We use SVM prediction for automatic patent classification. We also suggest potential improvement strategies to enhance the prediction performance of our suggested approach. This study contributes to the field in that it can lead to the automatic classification of patents without the need for any expert judgment during the process.