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

  • Wang M
  • Chang D
  • Kao C
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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.

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  • Ming Yeu Wang

  • Dong Shang Chang

  • Chih Hsi Kao

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