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.

中文翻译：

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专利的自动分类：主题建模方法

摘要 由于技术创新的快速增长和专利申请的相应增加，自动专利分类系统对于个人发明人和专利代理人进行专利分类都非常有帮助。然而，之前的研究忽略了专利包括哪些内容以及如何以结构化的形式有效地表示专利内容以预测专利类别的问题。作为回应，本研究提出了一种基于支持向量机 (SVM) 预测的主题模型，用于自动专利分类。本研究考虑了专利分类的两个重要问题：文本表示和类别预测。对于文本表示，我们使用主题建模技术并采用潜在狄利克雷分配 (LDA)。然后将 LDA 的结果用作第二个方面的输入：类预测。我们使用 SVM 预测进行自动专利分类。我们还提出了潜在的改进策略，以提高我们建议方法的预测性能。这项研究对该领域有贡献，因为它可以导致专利的自动分类，而无需在此过程中进行任何专家判断。