摘要
目前,专利已成为企业发展的重要驱动力和核心竞争力。专利侵权案件数量不断攀升,然而我国司法部门在判决专利侵权损害赔偿额时,依然存在不能较为准确地得出赔偿金额的缺陷,比如法官多采用法定赔偿进行判决。由于过多适用法定赔偿不利于完全弥补权利人的损失,威慑侵权人行为,甚至有损我国司法部门的公信力及权威。因此,笔者试图从资产评估学科的视角来思考专利侵权损害赔偿的价值评估,并采用熵权法和多元线性回归模型来修正专利侵权损害赔偿价值评估模型。首先,从三个维度构建了专利价值指标体系。其次,在赋予指标权重时,采用熵权法客观赋权。最后,通过多元线性回归分析拟合侵权因素。本文旨在为相关职能部门和研究人员对专利侵权损害赔偿价值评估研究提供一个新的视角和思路。
At present,patents have become an important driving force and core competitiveness of enterprise development.The number of patent infringement cases continues to rise.However,when judging the amount of patent infringement damages,the judicial departments of our country still have some defects that can’t get the compensation amount accurately.For example,judges often use legal compensation to make judgments.Excessive application of legal compensation is not conducive to fully compensating for the loss of the obligee,deterring the infringer’s behavior,and even damaging the credibility and authority of our judicial department.Therefore,the author tries to think about the value evaluation of patent infringement damages from the perspective of asset evaluation discipline,and uses entropy weight method and multiple linear regression model to revise the value evaluation model of patent infringement damages.Firstly,the patent value index system is constructed from three dimensions.Secondly,the entropy weight method is used to give the index weights objectively.Finally,the infringement factors are fitted by multiple linear regression analysis.The purpose of this paper is to provide a new perspective and ideas for relevant functional departments and researchers to evaluate the value of patent infringement damages.
作者
谭东丽
孟昱君
曾令超
Tan Dongli;Meng Yujun;Zeng Lingchao(Guangxi University of Scinence and Technology,Liuzhou 545005)
出处
《中国资产评估》
2022年第4期41-46,共6页
Appraisal Journal of China
基金
2021—2022年度柳州市哲学社会科学规划研究课题(21DSL30):深化法治柳州建设维护社会稳定研究
广西工业高质量发展研究中心2021年资产评估研究专项委托课题:基于不同评估背景的知识产权价值评估研究
广西教育厅中青年提升项目(2019KY0456):专利侵权损害赔偿数额确定研究。
关键词
专利侵权
价值评估
熵权法
多元线性回归
Evaluation of value
Patent infringement damages
Entropy weight method
Multiple linear regression