IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i10p3729-d176192.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/10/3729/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/10/3729/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Daiho Uhm & Sunghae Jun, 2022. "Zero-Inflated Patent Data Analysis Using Generating Synthetic Samples," Future Internet, MDPI, vol. 14(7), pages 1-11, July.
    2. Choi, Seokkyu & Lee, Hyeonju & Park, Eunjeong & Choi, Sungchul, 2022. "Deep learning for patent landscaping using transformer and graph embedding," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    3. Xuan Shi & Lingfei Cai & Hongfang Song, 2019. "Discovering Potential Technology Opportunities for Fuel Cell Vehicle Firms: A Multi-Level Patent Portfolio-Based Approach," Sustainability, MDPI, vol. 11(22), pages 1-22, November.
    4. 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.
    5. Byung-Yun Son & Eul-Bum Lee, 2019. "Using Text Mining to Estimate Schedule Delay Risk of 13 Offshore Oil and Gas EPC Case Studies During the Bidding Process," Energies, MDPI, vol. 12(10), pages 1-25, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    2. 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.
    3. Xuefeng Wang & Huichao Ren & Yun Chen & Yuqin Liu & Yali Qiao & Ying Huang, 2019. "Measuring patent similarity with SAO semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 1-23, October.
    4. Jinzhu Zhang & Wenqian Yu, 2020. "Early detection of technology opportunity based on analogy design and phrase semantic representation," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 551-576, October.
    5. Seunghyun Oh & Jaewoong Choi & Namuk Ko & Janghyeok Yoon, 2020. "Predicting product development directions for new product planning using patent classification-based link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1833-1876, December.
    6. Guo, Junfang & Wang, Xuefeng & Li, Qianrui & Zhu, Donghua, 2016. "Subject–action–object-based morphology analysis for determining the direction of technological change," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 27-40.
    7. An, Xin & Li, Jinghong & Xu, Shuo & Chen, Liang & Sun, Wei, 2021. "An improved patent similarity measurement based on entities and semantic relations," Journal of Informetrics, Elsevier, vol. 15(2).
    8. Jiwon Yu & Jong-Gyu Hwang & Jumi Hwang & Sung Chan Jun & Sumin Kang & Chulung Lee & Hyundong Kim, 2020. "Identification of Vacant and Emerging Technologies in Smart Mobility Through the GTM-Based Patent Map Development," Sustainability, MDPI, vol. 12(22), pages 1-22, November.
    9. Changbae Mun & Sejun Yoon & Hyunseok Park, 2019. "Structural decomposition of technological domain using patent co-classification and classification hierarchy," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 633-652, November.
    10. Janghyeok Yoon & Hyunseok Park & Kwangsoo Kim, 2013. "Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 313-331, January.
    11. Yang, Chao & Huang, Cui & Su, Jun, 2018. "An improved SAO network-based method for technology trend analysis: A case study of graphene," Journal of Informetrics, Elsevier, vol. 12(1), pages 271-286.
    12. Yoon, Janghyeok & Park, Hyunseok & Seo, Wonchul & Lee, Jae-Min & Coh, Byoung-youl & Kim, Jonghwa, 2015. "Technology opportunity discovery (TOD) from existing technologies and products: A function-based TOD framework," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 153-167.
    13. Woondong Yeo & Seonho Kim & Byoung-Youl Coh & Jaewoo Kang, 2013. "A quantitative approach to recommend promising technologies for SME innovation: a case study on knowledge arbitrage from LCD to solar cell," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(2), pages 589-604, August.
    14. Kyuwoong Kim & Kyeongmin Park & Sungjoo Lee, 2019. "Investigating technology opportunities: the use of SAOx analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 45-70, January.
    15. Choi, Kwang Hun & Kwon, Gyu Hyun, 2023. "Strategies for sensing innovation opportunities in smart grids: In the perspective of interactive relationships between science, technology, and business," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    16. Roman Jurowetzki, 2015. "Unpacking Big Systems - Natural Language Processing meets Network Analysis. A Study of Smart Grid Development in Denmark," SPRU Working Paper Series 2015-15, SPRU - Science Policy Research Unit, University of Sussex Business School.
    17. Wu, Yingwen & Ji, Yangjian, 2023. "Identifying firm-specific technology opportunities from the perspective of competitors by using association rule mining," Journal of Informetrics, Elsevier, vol. 17(2).
    18. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    19. Chen, Liang & Xu, Shuo & Zhu, Lijun & Zhang, Jing & Yang, Guancan & Xu, Haiyun, 2022. "A deep learning based method benefiting from characteristics of patents for semantic relation classification," Journal of Informetrics, Elsevier, vol. 16(3).
    20. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3729-:d:176192. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.