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Novelty-focused patent mapping for technology opportunity analysis

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  • Lee, Changyong
  • Kang, Bokyoung
  • Shin, Juneseuk

Abstract

Patent maps are an effective means of discovering potential technology opportunities. However, this method has been of limited use in practice since defining and interpreting patent vacancies, as surrogates for potential technology opportunities, tend to be intuitive and ambiguous. As a remedy, we propose an approach to detecting novel patents based on systematic processes and quantitative outcomes. At the heart of the proposed approach is the text mining to extract the patterns of word usage and the local outlier factor to measure the degree of novelty in a numerical scale. The meanings of potential technology opportunities become more explicit by identifying novel patents rather than patent vacancies that are usually represented as a simple set of keywords. Finally, a novelty-focused patent identification map is developed to explore the implications on novel patents. A case study of the patents about thermal management technology of light emitting diode (LED) is exemplified. We believe the proposed approach could be employed in various research areas, serving as a starting point for developing more general models.

Suggested Citation

  • Lee, Changyong & Kang, Bokyoung & Shin, Juneseuk, 2015. "Novelty-focused patent mapping for technology opportunity analysis," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 355-365.
    • Handle: RePEc:eee:tefoso:v:90:y:2015:i:pb:p:355-365
    DOI: 10.1016/j.techfore.2014.05.010
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    References listed on IDEAS

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    1. Christian Sternitzke & Isumo Bergmann, 2009. "Similarity measures for document mapping: A comparative study on the level of an individual scientist," Scientometrics, Springer;Akadémiai Kiadó, vol. 78(1), pages 113-130, January.
    2. Lee, Changyong & Cho, Yangrae & Seol, Hyeonju & Park, Yongtae, 2012. "A stochastic patent citation analysis approach to assessing future technological impacts," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 16-29.
    3. Jan M. Gerken & Martin G. Moehrle, 2012. "A new instrument for technology monitoring: novelty in patents measured by semantic patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 645-670, June.
    4. Blackman, Michael, 1995. "Provision of patent information: a national patent office perspective," World Patent Information, Elsevier, vol. 17(2), pages 115-123, June.
    5. Reitzig, Markus, 2004. "Improving patent valuations for management purposes--validating new indicators by analyzing application rationales," Research Policy, Elsevier, vol. 33(6-7), pages 939-957, September.
    6. Dahlin, Kristina B. & Behrens, Dean M., 2005. "When is an invention really radical?: Defining and measuring technological radicalness," Research Policy, Elsevier, vol. 34(5), pages 717-737, June.
    7. Kristina Dahlin & Deans M. Behrens, 2005. "When is an invention really radical? Defining and measuring technological radicalness," Post-Print hal-00480416, HAL.
    8. Reitzig, Markus, 2003. "What determines patent value?: Insights from the semiconductor industry," Research Policy, Elsevier, vol. 32(1), pages 13-26, January.
    9. Fischer, Timo & Leidinger, Jan, 2014. "Testing patent value indicators on directly observed patent value—An empirical analysis of Ocean Tomo patent auctions," Research Policy, Elsevier, vol. 43(3), pages 519-529.
    Full references (including those not matched with items on IDEAS)

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