A Hybrid Model Combining SOMs with SVRs for Patent Quality Analysis and Classification
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) approa
- PDF / 405,681 Bytes
- 8 Pages / 439.37 x 666.142 pts Page_size
- 25 Downloads / 158 Views
Software School, Nanchang University, Nanchang, Jiangxi, China 2 Innovation Center for Big Data and Digital Convergence and Department of Information Management, Yuan Ze University, Taoyuan, Taiwan [email protected] 3 Institute of Information Science, Academia Sinica, Taipei, Taiwan 4 Science & Technology Policy Research and Information Center, National Applied Research Laboratories, Taipei, Taiwan
Abstract. 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 paper 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 model will cluster patents published before into different quality groups according to the patent quality indicators and defines group quality type instead of via experts. Then, 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. Keywords: Patent analysis Patent quality classification Machine learning
Data clustering Patent quality
1 Introduction Currently, there are various tools that are being utilized by organizations for analyzing patents. However, an important issue of patent analysis is patent quality analysis. The high-quality patent information can ensure success for business decision-making process or product development [1, 2]. This study reviewed the patent analysis approaches that can understand patent status like patent quality, novelty, litigation, trends and so on [3]. However, traditional patent analysis requires spending much time, cost and manpower. The potential patents for high quality determining approach need have shortened analysis at times. In general, the analysis approaches are statistical analysis or indicators computation. Recently, the clustering method is widely applied to cluster patent according to patent characteristics for patent trend [4]. The methods with © Springer International Publishing Switzerland 2016 Y. Tan and Y. Shi (Eds.): DMBD 2016, LNCS 9714, pp. 262–269, 2016. DOI: 10.1007/978-3-319-40973-3_26
A Hybrid Model Combining SOMs with SVRs
263
statistical analysis can help analysts to understand patent situation or trend of this time, but if we want to know the potential quality of a newly applied patent, it doesn’t provide effective rules or solutions to determination. The future patent evaluation is a key issue when a new patent is applied or published because patent has been producing impact on the industry according to the past industria
Data Loading...
