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Patent value evaluation is very important in the areas of patent transaction and patent financing. By integrating K-means clustering and C-F uncertainty reasoning model with sample data analytics, we propose a patent value evaluation algorithm to quantify patent value according to the aspects of patent content, patent applied ranges and patent research background. The process and implementation method of patent value evaluation algorithm are discussed, and the example to evaluate application value of patents in the area of railway industry in international patent database produced by the European Patent Office is introduced. The results can adequately verify effectiveness of our proposed algorithm.
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