Clustering Analysis and Visualization of TCM Patents Based on Deep Learning
In the process of medicine innovation, pharmaceutical enterprises tend to seize the intellectual property highland actively. They engage in research and development independently, apply for patents for core technologies, or take the initiative to acquire patents from others. Before applying for patents by their own efforts or purchasing patents from others, pharmaceutical companies need to search for related patents in the patent pool and make a comparative analysis of them, in order to find technology blank areas as R&D objectives, or find valuable patents as potential acquisition targets. In this paper, we use deep learning technology and propose a semantic-based clustering algorithm for Traditional Chinese Medicine (TCM) patents, discarding the traditional literal–based text clustering method. We also give a visualization method for TCM patents, so as to facilitate pharmaceutical enterprises to intuitively understand the relevant patents.
This work was supported by National Key Research and Development Program of China under Grant 2017YFC1405403; National Natural Science Foundation of China under Grant 61075059; Philosophical and Social Sciences Research Project of Hubei Education Department under Grant 19Q054; Green Industry Technology Leading Project (product development category) of Hubei University of Technology under Grant CPYF2017008; Research Foundation for Advanced Talents of Hubei University of Technology under Grant BSQD12131; Natural Science Foundation of Anhui Province under Grant 1708085MF161; and Key Project of Natural Science Research of Universities in Anhui under Grant KJ2015A236.
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