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Supporting Patent Maintenance Decision: A Data Mining Approach

机译:支持专利维护决策的数据挖掘方法

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摘要

Nowadays, patents become much more important for companies to protect their rights and intellectual assets under the keen competitive business environments. However, it is not free for a granted patent. In the patent systems of many countries, a patent holder is required to pay a maintenance fee after the initial application to retain patent protection on his/her invention until the expiration of the protection period. Because not all the patents are worth maintaining by patent holders, firms and organizations need to identify "important patents" for maintenance and abandon "unimportant patents" to avoid unnecessary patent maintenance costs. In this paper, we employ the variables suggested by prior studies that would discriminate renewed patents from those abandoned ones and then take the data mining approach to construct a prediction model(s) on the basis of these variables for supporting patent maintenance decisions. Such a data-mining-based patent maintenance decision support system can help firms and organizations improve the effectiveness of their patent maintenance decisions and. at the same time, decrease the cost of their patent maintenance decisions. Our empirical results indicate that the effectiveness of our proposed system is satisfactory and practical for supporting patent maintenance decisions.
机译:如今,在竞争激烈的商业环境中,专利对于公司保护其权利和知识资产变得越来越重要。但是,获得专利并不是免费的。在许多国家的专利制度中,专利持有人必须在首次申请后缴纳维持费,以保持对其发明的专利保护,直至保护期届满。因为并非所有专利都值得拥有人维护,所以公司和组织需要确定要维护的“重要专利”并放弃“不重要的专利”,以避免不必要的专利维护成本。在本文中,我们采用了先前研究建议的变量,这些变量可以将更新的专利与那些废弃的专利区分开,然后采用数据挖掘方法在这些变量的基础上构建一个预测模型,以支持专利维护决策。这种基于数据挖掘的专利维护决策支持系统可以帮助公司和组织提高其专利维护决策的效率。同时,降低其专利维护决策的成本。我们的经验结果表明,我们提出的系统在支持专利维护决策方面的有效性令人满意。

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