Identifying technology trends for R and D planning using TRIZ and text mining

Ming Yeu Wang, Dong Shang Chang, Chih Hsi Kao

Research output: Contribution to journalArticlepeer-review

52 Scopus citations


The current pace of technological development has forced many companies to invest significant capital and resources in research and development (R&D) activities. A systematic and efficient method of identifying technology trends and their evolutionary potentials can help companies guide their R&D planning and wisely allocate their R&D resources. This study proposes a framework combining the evolutionary trends developed by the Theory of Inventive Problem Solving, or Teoriya Reshniya Izobretatelskikh Zadatch (TRIZ) in Russian, with the visualization technique of text mining to systematically identify technology trends from patent documents. As technological information in patent documents is stored almost entirely in text format, the text mining method allows R&D personnel to efficiently identify technology trends and effectively conduct R&D planning. Utilizing text mining method on patents of magnetic random access memory (MRAM) systems and the underlying principles of TRIZ evolutionary trends, this study shows that MRAM includes 10 important technology trends. These trends have almost reached the evolutionary limit phase defined by TRIZ, which means that MRAM is fast becoming a mature technology. Therefore, for businesses that intend to acquire MRAM technology they do not possess, a wise R&D plan may be licensing the technology, buying the technology from others, or participating in a joint venture rather than using in-house R&D.

Original languageEnglish
Pages (from-to)491-509
Number of pages19
JournalR and D Management
Issue number5
StatePublished - Nov 2010


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