A patent quality analysis and classification system using self-organizing maps with support vector machine

Wu Jheng-Long; Chang Pei-Chann; Tsao Cheng-Chin; Fan Chin-Yuan

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A patent quality analysis and classification system using self-organizing maps with support vector machine

机译：使用自组织映射和支持向量机的专利质量分析和分类系统

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A plethora of patents are approved by the patent officers each year and current patent systems face a solemn quandary of evaluating these patents' qualities. Traditional researchers and analyzers have fixated on developing sundry patent quality indicators only, but these indicators do not have further prognosticating power on incipient patent applications or publications. Therefore, the data mining (DM) approaches are employed in this article to identify and to classify the new patent's quality in time. An automatic patent quality analysis and classification system, namely SOM-KPCA-SVM, is developed according to patent quality indicators and characteristics, respectively. First, the self-organizing map (SOM) approach is used to cluster patents published before into different quality groups according to the patent quality indicators and defines group quality type instead of via experts. The kernel principal component analysis (KPCA) approach is used to transform nonlinear feature space in order to improve classification performance. Finally, the support vector machine (SVM) is used to build up the patent quality classification model. The proposed SOM-KPCA-SVM is applied to classify patent quality automatically in patent data of the thin film solar cell. Experimental results show that our proposed system can capture the analysis effectively compared with traditional manpower approach. (C) 2016 Elsevier B.V. All rights reserved.

机译：每年，专利官员都会批准大量专利，而当前的专利体系面临评估这些专利质量的严峻难题。传统的研究人员和分析人员仅专注于开发各种专利质量指标，但是这些指标对初期专利申请或出版物没有进一步的预测能力。因此，本文采用数据挖掘（DM）方法来及时识别和分类新专利的质量。分别根据专利质量指标和特点，开发了自动专利质量分析分类系统SOM-KPCA-SVM。首先，自组织图（SOM）方法用于根据专利质量指标将之前发布的专利分为不同的质量组，并定义组质量类型，而不是通过专家。内核主成分分析（KPCA）方法用于变换非线性特征空间，以提高分类性能。最后，使用支持向量机（SVM）建立专利质量分类模型。所提出的SOM-KPCA-SVM被应用于在薄膜太阳能电池的专利数据中自动对专利质量进行分类。实验结果表明，与传统的人工方法相比，我们提出的系统可以有效地捕获分析结果。（C）2016 Elsevier B.V.保留所有权利。

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来源
《Applied Soft Computing》 |2016年第null期|共12页
作者
Wu Jheng-Long; Chang Pei-Chann; Tsao Cheng-Chin; Fan Chin-Yuan;
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正文语种 eng
中图分类计算机软件;
关键词
Patent analysis; Patent quality; Data clustering; Patent quality classification; Machine learning;

机译：专利分析;专利质量;数据聚类;专利质量分类;机器学习;

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1. A patent quality analysis and classification system using self-organizing maps with support vector machine [J] . Wu Jheng-Long, Chang Pei-Chann, Tsao Cheng-Chin, Applied Soft Computing . 2016,第Null期

机译：使用自组织映射和支持向量机的专利质量分析和分类系统
2. Using self-organizing map (SOM) and support vector machine (SVM) for classification of selectivity of ACAT inhibitors [J] . Ling Wang, Maolin Wang, Aixia Yan, Molecular Diversity . 2013,第1期

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A patent quality analysis and classification system using self-organizing maps with support vector machine

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