Content-based Link Prediction for Patent Marketing

Patent licensing is a significant source of revenue for a business with a patent portfolio as large as IBM’s. Successful marketing of such a portfolio requires methodology to match the technology covered by each patent to the requirements of other companies which are potential licensees for this intellectual property. In this paper, we address this problem using a purely content-based recommendation methodology to identify new opportunities by matching companies and patents for which no prior linkages exists. In this context we highlight the important concept of learning ‘equality’ and explore why existing content-based approaches typically take an indirect approach of pre-defined similarities. We show theoretically and empirically that this equality concept is easily addressed by the standard feature-vector representation and second order polynomial kernel SVMs.

By: Claudia Perlich; Grzegorz Swirszcz; Rick Lawrence

Published in: RC24857 in 2009


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