Abstract
Abstract Patents have become much more important for companies to protect their rights and intellectual assets. Patent licensing is therefore an important issue in a firm’s patent management to gain competitive advantage, generate financial revenue, and make a measurement of technology portfolio. Companies such as IBM, Microsoft, and Texas Instruments have demonstrated the power of patent licensing to improve the financial performance by managing and carefully reviewing the patents. However, companies still need to cooperate with intellectual property lawyers or utilize the tools to assess the patent licensability. To avoid spending a lot of time, money, and manpower, the service of automatically estimating patent licesability is necessary. In this work, we first examine the factors influencing patent licensability by the Delphi study. Then, in our patent licensing decision support system, we adopt a data mining approach to build a patent licensability estimation model by using the important factors. Finally, we investigate the performance of our patent licensability estimation model by conducting a series of experiments. The experimental results demonstrate that our proposed patent licensing decision support system is superior to the benchmark. Moreover, our proposed system is capable of early detection. Keywords: Patent License, Data Mining, Patent Analysis