IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v130y2025i7d10.1007_s11192-025-05342-x.html
   My bibliography  Save this article

Exploring technological landscape to uncover technological opportunities for immersive technologies in the Metaverse using patent data

Author

Listed:
  • Juite Wang

    (National Chung Hsing University)

Abstract

Understanding the technological landscape of immersive technologies within the Metaverse is essential for identifying emerging opportunities and guiding strategic R&D investments. This study presents a patent landscaping approach that integrates context-aware textual analysis using Augmented Sentence-BERT (AugSBERT) with structured patent classification data to assess technological development. AugSBERT is employed to generate semantic representations of patent texts, while classification data offer a complementary structured perspective. Similarity scores derived from both sources are used to identify technological clusters through spectral clustering, which is well-suited for detecting complex patterns in high-dimensional data. These clusters are visualized using a technology strategy map, and their significance is evaluated through key patent indicators to uncover areas of innovation potential. The findings highlight several key trends in immersive technologies. Human–computer interaction remains a highly competitive area, while mixed reality demonstrates notable growth potential despite a lower current impact. Emerging applications in healthcare and robotics are becoming increasingly relevant, while domains such as commerce and stereoscopic imaging present uncertain prospects that may require more cautious investment strategies. Meanwhile, head-mounted displays (HMDs) and gaming technologies appear to be technologically mature, suggesting the need for selective and strategic engagement. This study provides a structured exploration of immersive technology developments within the Metaverse, identifying key clusters and highlighting emerging areas of interest. The results offer practical insights for firms and policymakers seeking to align R&D efforts with evolving technological trends in this rapidly developing field.

Suggested Citation

  • Juite Wang, 2025. "Exploring technological landscape to uncover technological opportunities for immersive technologies in the Metaverse using patent data," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(7), pages 3505-3536, July.
    • Handle: RePEc:spr:scient:v:130:y:2025:i:7:d:10.1007_s11192-025-05342-x
    DOI: 10.1007/s11192-025-05342-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-025-05342-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-025-05342-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Suominen, Arho & Toivanen, Hannes & Seppänen, Marko, 2017. "Firms' knowledge profiles: Mapping patent data with unsupervised learning," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 131-142.
    2. David J. TEECE, 2008. "Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy," World Scientific Book Chapters, in: The Transfer And Licensing Of Know-How And Intellectual Property Understanding the Multinational Enterprise in the Modern World, chapter 5, pages 67-87, World Scientific Publishing Co. Pte. Ltd..
    3. Jasjit Singh & Ajay Agrawal, 2011. "Recruiting for Ideas: How Firms Exploit the Prior Inventions of New Hires," Management Science, INFORMS, vol. 57(1), pages 129-150, January.
    4. Junmo Kim & Juneseuk Shin, 2018. "Mapping extended technological trajectories: integration of main path, derivative paths, and technology junctures," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1439-1459, September.
    5. Klevorick, Alvin K. & Levin, Richard C. & Nelson, Richard R. & Winter, Sidney G., 1995. "On the sources and significance of interindustry differences in technological opportunities," Research Policy, Elsevier, vol. 24(2), pages 185-205, March.
    6. Jarneving, Bo, 2007. "Bibliographic coupling and its application to research-front and other core documents," Journal of Informetrics, Elsevier, vol. 1(4), pages 287-307.
    7. Jaeyoung Kim & Janghyeok Yoon & Eunjeong Park & Sungchul Choi, 2020. "Patent document clustering with deep embeddings," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 563-577, May.
    8. Luciano Kay & Nils Newman & Jan Youtie & Alan L. Porter & Ismael Rafols, 2014. "Patent overlay mapping: Visualizing technological distance," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(12), pages 2432-2443, December.
    9. Aharonson, Barak S. & Schilling, Melissa A., 2016. "Mapping the technological landscape: Measuring technology distance, technological footprints, and technology evolution," Research Policy, Elsevier, vol. 45(1), pages 81-96.
    10. Sam Arts & Bruno Cassiman & Juan Carlos Gomez, 2018. "Text matching to measure patent similarity," Strategic Management Journal, Wiley Blackwell, vol. 39(1), pages 62-84, January.
    11. Changyong Lee & Gyumin Lee, 2019. "Technology opportunity analysis based on recombinant search: patent landscape analysis for idea generation," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 603-632, November.
    12. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    13. 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.
    14. Harhoff, Dietmar & Scherer, Frederic M. & Vopel, Katrin, 2004. "Erratum to "Citations, family size, opposition and the value of patent rights" [Research Policy 32 (2003) 1343-1363]," Research Policy, Elsevier, vol. 33(2), pages 363-364, March.
    15. Luis Diestre & Nandini Rajagopalan, 2012. "Are all ‘sharks’ dangerous? new biotechnology ventures and partner selection in R&D alliances," Strategic Management Journal, Wiley Blackwell, vol. 33(10), pages 1115-1134, October.
    16. Eko Harry Pratisto & Nik Thompson & Vidyasagar Potdar, 2022. "Immersive technologies for tourism: a systematic review," Information Technology & Tourism, Springer, vol. 24(2), pages 181-219, June.
    17. 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).
    18. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    19. Kim, Gabjo & Bae, Jinwoo, 2017. "A novel approach to forecast promising technology through patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 228-237.
    20. Breitzman, Anthony & Thomas, Patrick, 2015. "The Emerging Clusters Model: A tool for identifying emerging technologies across multiple patent systems," Research Policy, Elsevier, vol. 44(1), pages 195-205.
    21. Yoon, Byungun & Park, Inchae & Coh, Byoung-youl, 2014. "Exploring technological opportunities by linking technology and products: Application of morphology analysis and text mining," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 287-303.
    22. Benner, Mary & Waldfogel, Joel, 2008. "Close to you? Bias and precision in patent-based measures of technological proximity," Research Policy, Elsevier, vol. 37(9), pages 1556-1567, October.
    23. Juite Wang & Tzu-Yen Hsu, 2023. "Early discovery of emerging multi-technology convergence for analyzing technology opportunities from patent data: the case of smart health," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4167-4196, August.
    24. Flavián, Carlos & Ibáñez-Sánchez, Sergio & Orús, Carlos, 2019. "The impact of virtual, augmented and mixed reality technologies on the customer experience," Journal of Business Research, Elsevier, vol. 100(C), pages 547-560.
    25. 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.
    26. Flavia Filippin, 2021. "Do main paths reflect technological trajectories? Applying main path analysis to the semiconductor manufacturing industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6443-6477, August.
    27. Kollmer, Holger & Dowling, Michael, 2004. "Licensing as a commercialisation strategy for new technology-based firms," Research Policy, Elsevier, vol. 33(8), pages 1141-1151, October.
    28. Lee Fleming & Olav Sorenson, 2004. "Science as a map in technological search," Strategic Management Journal, Wiley Blackwell, vol. 25(8‐9), pages 909-928, August.
    29. Bowen Song & Chunjuan Luan & Danni Liang, 2023. "Identification of emerging technology topics (ETTs) using BERT-based model and sematic analysis: a perspective of multiple-field characteristics of patented inventions (MFCOPIs)," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(11), pages 5883-5904, November.
    Full references (including those not matched with items on IDEAS)

    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. Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    2. Yuan Zhou & Fang Dong & Yufei Liu & Liang Ran, 2021. "A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of CNC machine tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 969-994, February.
    3. Hain, Daniel S. & Jurowetzki, Roman & Buchmann, Tobias & Wolf, Patrick, 2022. "A text-embedding-based approach to measuring patent-to-patent technological similarity," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    4. Song, Kisik & Kim, Kyuwoong & Lee, Sungjoo, 2018. "Identifying promising technologies using patents: A retrospective feature analysis and a prospective needs analysis on outlier patents," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 118-132.
    5. Sun, Bixuan & Kolesnikov, Sergey & Goldstein, Anna & Chan, Gabriel, 2021. "A dynamic approach for identifying technological breakthroughs with an application in solar photovoltaics," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    6. Antonio Messeni Petruzzelli & Daniele Rotolo & Vito Albino, 2014. "Determinants of Patent Citations in Biotechnology: An Analysis of Patent Influence Across the Industrial and Organizational Boundaries," SPRU Working Paper Series 2014-05, SPRU - Science Policy Research Unit, University of Sussex Business School.
    7. Lee, Changyong & Kwon, Ohjin & Kim, Myeongjung & Kwon, Daeil, 2018. "Early identification of emerging technologies: A machine learning approach using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 291-303.
    8. Changyong Lee & Gyumin Lee, 2019. "Technology opportunity analysis based on recombinant search: patent landscape analysis for idea generation," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 603-632, November.
    9. Lee, Changyong & Jeon, Daeseong & Ahn, Joon Mo & Kwon, Ohjin, 2020. "Navigating a product landscape for technology opportunity analysis: A word2vec approach using an integrated patent-product database," Technovation, Elsevier, vol. 96.
    10. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    11. Yoruk, Esin & Radosevic, Slavo & Fischer, Bruno, 2023. "Technological profiles, upgrading and the dynamics of growth: Country-level patterns and trajectories across distinct stages of development," Research Policy, Elsevier, vol. 52(8).
    12. Li, Munan & Porter, Alan L. & Suominen, Arho & Burmaoglu, Serhat & Carley, Stephen, 2021. "An exploratory perspective to measure the emergence degree for a specific technology based on the philosophy of swarm intelligence," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    13. José Manuel López‐Fernández & Mariluz Maté‐Sánchez‐Val & Francisco Manuel Somohano‐Rodriguez, 2021. "The effect of micro‐territorial networks on industrial small and medium enterprises' innovation: A case study in the Spanish region of Cantabria," Papers in Regional Science, Wiley Blackwell, vol. 100(1), pages 51-77, February.
    14. Uijun Kwon & Youngjung Geum, 2020. "Identification of promising inventions considering the quality of knowledge accumulation: a machine learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1877-1897, December.
    15. Youngjae Choi & Sanghyun Park & Sungjoo Lee, 2021. "Identifying emerging technologies to envision a future innovation ecosystem: A machine learning approach to patent data," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5431-5476, July.
    16. Wooseok Jang & Yongtae Park & Hyeonju Seol, 2021. "Identifying emerging technologies using expert opinions on the future: A topic modeling and fuzzy clustering approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6505-6532, August.
    17. van Burg, Elco & Du, Jingshu & Kers, Jannigje Gerdien, 2021. "When do academics patent outside their university? An in-depth case study," Technovation, Elsevier, vol. 107(C).
    18. Jumi Hwang & Kyung Hee Kim & Jong Gyu Hwang & Sungchan Jun & Jiwon Yu & Chulung Lee, 2020. "Technological Opportunity Analysis: Assistive Technology for Blind and Visually Impaired People," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
    19. Katsuyuki Kaneko & Yuya Kajikawa, 2023. "Novelty Score and Technological Relatedness Measurement Using Patent Information in Mergers and Acquisitions: Case Study in the Japanese Electric Motor Industry," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(2), pages 163-177, June.
    20. Veugelers, Reinhilde & Cassiman, Bruno & Arts, Sam, 2012. "Mind the gap: capturing value from basic research: boundary crossing inventors and partnerships," CEPR Discussion Papers 9215, C.E.P.R. Discussion Papers.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:spr:scient:v:130:y:2025:i:7:d:10.1007_s11192-025-05342-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.