Owing to the large number of professional glossaries and unknown patent classification, analysts usually fail to collect and analyze patents efficiently. One solution to this problem is to conduct patent analysis using a patent classification system. However, in a corpus such as cloud patents, many keywords are common among different classes, making it difficult to classify the unknown class documents using the machine learning techniques proposed by previous studies. To remedy this problem, this study aims to establish an efficient classification system with a special focus on features extraction and application of extension theory. We first propose a compound method to determine the features, and then, we propose an extension-based classification method to develop an efficient patent classification system. Using cloud computing patents as the database, the experimental results show that our proposed scheme can outperform the classification quality of the traditional classifiers.

中文翻译：

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基于扩展的云计算专利分类系统

由于专业词汇量大、专利分类未知，分析师通常无法有效地收集和分析专利。该问题的一种解决方案是使用专利分类系统进行专利分析。然而，在云专利等语料库中，许多关键字在不同类别中是通用的，使得使用先前研究提出的机器学习技术难以对未知类别文档进行分类。为了解决这个问题，本研究旨在建立一个有效的分类系统，特别关注特征提取和可拓理论的应用。我们首先提出了一种确定特征的复合方法，然后，我们提出了一种基于扩展的分类方法来开发高效的专利分类系统。