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Supporting Patent Maintenance Decision: A Data Mining Approach

机译:支持专利维护决策:数据挖掘方法

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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,专利对公司在敏锐的竞争性商业环境下保护其权利和知识产权来说更为重要。但是,授予专利并不免费。在许多国家的专利制度中,专利持有人在初始申请后需要支付维护费,以保留他/她的发明的专利保护,直到保护期限到期。因为并非所有专利值得维护专利持有人,企业和组织需要识别维护和放弃“不重要专利”的“重要专利”,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所建议的变量,这些变量将从那些被遗弃的那些被遗弃的专利区进行歧视,然后采取数据挖掘方法根据这些变量来构建预测模型,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专利维护决策的有效性。同时,降低其专利维护决策的成本。我们的经验结果表明,我们提出的系统的有效性对于支持专利维持决策令人满意和实用。

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