Abstract

It is a challenging task for firms to assess the importance of a patent and identify valuable patents as early as possible. Counting the number of citations received is a widely used method to assess the value of a patent. However, recently granted patents have few citations received, which makes the use of citation counts infeasible. In this paper, we propose a novel idea to evaluate the value of new or recently granted patents using recommended relevant prior patents. Our approach is to exploit trends in temporal patterns of relevant prior patents, which are highly related to patent values. We evaluate the proposed approach using two patent value evaluation tasks with a large-scale collection of U.S. patents. Experimental results show that the models created based on our idea significantly enhance those using the baseline features or patent backward citations.

Original languageEnglish (US)
Pages (from-to)545-556
Number of pages12
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8443 LNAI
Issue numberPART 1
DOIs
StatePublished - Jan 1 2014
Event18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014 - Tainan, Taiwan, Province of China
Duration: May 13 2014May 16 2014

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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