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Constructing Patent Maps Using Text Mining to Sustainably Detect Potential Technological Opportunities

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  • Hei Chia Wang

    (Department of Industrial and Information Management and Institute of Information Management, National Cheng Kung University, Tainan 701, Taiwan)

  • Yung Chang Chi

    (Department of Industrial and Information Management and Institute of Information Management, National Cheng Kung University, Tainan 701, Taiwan)

  • Ping Lun Hsin

    (Department of Industrial and Information Management and Institute of Information Management, National Cheng Kung University, Tainan 701, Taiwan)

Abstract

With the advent of the knowledge economy, firms often compete for intellectual property rights. Being the first to acquire high-potential patents can assist firms in achieving future competitive advantages. To identify patents capable of being developed, firms often search for a focus by using existing patent documents. Because of the rapid development of technology, the number of patent documents is immense. A prominent topic among current firms is how to use this large number of patent documents to discover new business opportunities while avoiding conflicts with existing patents. In the search for technological opportunities, a crucial task is to present results in the form of an easily understood visualization. Currently, natural language processing can help in achieving this goal. In natural language processing, word sense disambiguation (WSD) is the problem of determining which “sense” (meaning) of a word is activated in a given context. Given a word and its possible senses, as defined by a dictionary, we classify the occurrence of a word in context into one or more of its sense classes. The features of the context (such as neighboring words) provide evidence for these classifications. The current method for patent document analysis warrants improvement in areas, such as the analysis of many dimensions and the development of recommendation methods. This study proposes a visualization method that supports semantics, reduces the number of dimensions formed by terms, and can easily be understood by users. Since polysemous words occur frequently in patent documents, we also propose a WSD method to decrease the calculated degrees of distortion between terms. An analysis of outlier distributions is used to construct a patent map capable of distinguishing similar patents. During the development of new strategies, the constructed patent map can assist firms in understanding patent distributions in commercial areas, thereby preventing patent infringement caused by the development of similar technologies. Subsequently, technological opportunities can be recommended according to the patent map, aiding firms in assessing relevant patents in commercial areas early and sustainably achieving future competitive advantages.

Suggested Citation

  • Hei Chia Wang & Yung Chang Chi & Ping Lun Hsin, 2018. "Constructing Patent Maps Using Text Mining to Sustainably Detect Potential Technological Opportunities," Sustainability, MDPI, vol. 10(10), pages 1-18, October.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3729-:d:176192
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    References listed on IDEAS

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    1. Wang, Ming-Yeu & Fang, Shih-Chieh & Chang, Yu-Hsuan, 2015. "Exploring technological opportunities by mining the gaps between science and technology: Microalgal biofuels," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 182-195.
    2. J. Kruskal, 1964. "Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis," Psychometrika, Springer;The Psychometric Society, vol. 29(1), pages 1-27, March.
    3. Lothar Walter & Alfred Radauer & Martin G. Moehrle, 2017. "The beauty of brimstone butterfly: novelty of patents identified by near environment analysis based on text mining," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 103-115, April.
    4. Marti Sagarra & Cecilio Mar-Molinero & Miguel García-Cestona, 2015. "Spanish savings banks in the credit crunch: could distress have been predicted before the crisis? A multivariate statistical analysis," The European Journal of Finance, Taylor & Francis Journals, vol. 21(3), pages 195-214, February.
    5. Janghyeok Yoon & Kwangsoo Kim, 2011. "Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 213-228, July.
    6. Janghyeok Yoon & Kwangsoo Kim, 2012. "Detecting signals of new technological opportunities using semantic patent analysis and outlier detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 445-461, February.
    7. Hyunseok Park & Janghyeok Yoon & Kwangsoo Kim, 2013. "Identification and evaluation of corporations for merger and acquisition strategies using patent information and text mining," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 883-909, December.
    8. Jongchan Kim & Jaehyun Choi & Sangsung Park & Dongsik Jang, 2018. "Patent Keyword Extraction for Sustainable Technology Management," Sustainability, MDPI, vol. 10(4), pages 1-18, April.
    9. Hyunseok Park & Janghyeok Yoon & Kwangsoo Kim, 2012. "Identifying patent infringement using SAO based semantic technological similarities," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 515-529, February.
    10. Song, Kisik & Kim, Karp Soo & Lee, Sungjoo, 2017. "Discovering new technology opportunities based on patents: Text-mining and F-term analysis," Technovation, Elsevier, vol. 60, pages 1-14.
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    5. Munawir & Mochamad Donny Koerniawan & Bart Julien Dewancker, 2019. "Visitor Perceptions and Effectiveness of Place Branding Strategies in Thematic Parks in Bandung City Using Text Mining Based on Google Maps User Reviews," Sustainability, MDPI, vol. 11(7), pages 1-20, April.

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