Abstract
The proliferation of large language models (LLMs) has significantly expanded the landscape of research on technology opportunity identification. However, there remains a crucial need to enhance the accuracy and interpretability of results obtained through emerging technology topic identification. In this paper, we present a novel approach that leverages a BERT-based model and semantic analysis to identify emerging technology topics (ETTs) from the perspective of multiple-field characteristics of patented inventions (MFCOPIs). By utilizing a unique dataset encompassing MFCOPI, our methodology emphasizes an increased proportion of novel technical processes in the analysis content while mitigating the interference of redundant technical information. To enhance the interpretability of recognition results, our proposed model employs the BERT model for detecting potential content similarities in inventive characteristics and incorporates semantic structure analysis to expand the technical process content. We empirically validate our model by employing nanotechnology as a case study, demonstrating its effectiveness and accuracy. Through our research, we extend the existing methodologies for recognizing emerging technology, ultimately elevating the quality of recognition results.
Similar content being viewed by others
References
Abernathy, W. J., & Utterback, J. M. (1978). Patterns of industrial innovation. Technology Review, 80(7), 40–47.
Adner, R., & Levinthal, D. A. (2002). The emergence of emerging technologies. California Management Review, 45(1), 50–66.
Alʹtshuller, G. S. (1999). The innovation algorithm: TRIZ, systematic innovation and technical creativity. Technical Innovation Center Inc.
Blei, D. M., & Lafferty, J. D. (2006). Dynamic topic models. In Paper presented at the machine learning, proceedings of the twenty-third international conference (ICML 2006), Pittsburgh, Pennsylvania, USA, June 25–29, 2006.
Brockhoff, K. (1992). Instruments for patent data analyses in business firms. Technovation, 12(1), 41–59.
Chen, C. (2004). Searching for intellectual turning points: Progressive knowledge domain visualization. Proceedings of the National Academy of Sciences, 101(1), 5303–5310.
Chen, J., Jiang, S., Wang, M., Xie, X., & Su, X. (2021). Self-assembled dual-emissive nanoprobe with metal-organic frameworks as scaffolds for enhanced ascorbic acid and ascorbate oxidase sensing. Sensors and Actuators B: Chemical, 339, 129910.
Choi, S., Yoon, J., Kim, K., Lee, J. Y., & Kim, C. H. (2011). SAO network analysis of patents for technology trends identification: A case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells. Scientometrics, 88(3), 863–883.
Choudhury, N., Faisal, F., & Khushi, M. (2020). Mining temporal evolution of knowledge graphs and genealogical features for literature-based discovery prediction. Journal of Informetrics, 14(3), 101057.
Christensen, C., & Raynor, M. (2013). The innovator’s solution: Creating and sustaining successful growth. Harvard Business Review Press.
Day, G., Schoemaker, P., & Gunther, R. E. (2000). Wharton on managing emerging technologies. Wiley.
Derwent. (2023). Derwent innovations index: Derwent innovations index user guide. Retrieved from https://clarivate.com/webofsciencegroup/support/wos/dii/.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805.
Érdi, P., Makovi, K., Somogyvári, Z., Strandburg, K., Tobochnik, J., Volf, P., & Zalányi, L. (2013). Prediction of emerging technologies based on analysis of the US patent citation network. Scientometrics, 95(1), 225–242.
Ernst, H. (2003). Patent information for strategic technology management. World Patent Information, 25(3), 233–242.
Furukawat, M. (2015). Identifying the evolutionary process of emerging technologies: A chronological network analysis of World Wide Web conference sessions. Technological Forecasting and Social Change, 2015(91), 280–294.
Gerken, J. M., & Moehrle, M. G. (2012). A new instrument for technology monitoring: Novelty in patents measured by semantic patent analysis. Scientometrics, 91(3), 645–670.
Hassan, S. U., Imran, M., Iqbal, S., Aljohani, N. R., & Nawaz, R. (2018). Deep context of citations using machine-learning models in scholarly full-text articles. Scientometrics, 117(3), 1645–1662.
Hayashi, A. M. (2004). Technology trajectories and the birth of new industries: Markets develop according to the specific paths by which innovations in a given field occur. MIT Sloan Management Review, 45(3), 7–9.
Jaffe, A. B., & De Rassenfosse, G. (2017). Patent citation data in social science research: Overview and best practices. Journal of the Association for Information Science and Technology, 68(6), 1360–1374.
Joung, J., & Kim, K. (2017). Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data. Technological Forecasting and Social Change, 114, 281–292.
Kleinberg, J. (2003). Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery, 7(4), 373–397.
Kreuchauff, F., & Korzinov, V. (2017). A patent search strategy based on machine learning for the emerging field of service robotics. Scientometrics, 111(2), 743–772.
Kuznets, S. (1962). Inventive activity: Problems of definition and measurement. The rate and direction of inventive activity: Economic and social factors (pp. 19–52). Princeton University Press.
Lee, C. (2021). A review of data analytics in technological forecasting. Technological Forecasting and Social Change, 166, 120646.
Lee, C., Kwon, O., Kim, M., & Kwon, D. (2018). Early identification of emerging technologies: A machine learning approach using multiple patent indicators. Technological Forecasting and Social Change, 127, 291–303.
Liang, Z., Mao, J., Lu, K., Ba, Z., & Li, G. (2021). Combining deep neural network and bibliometric indicator for emerging research topic prediction. Information Processing & Management, 58(5), 102611.
Ma, T., Zhou, X., Liu, J., Lou, Z., Hua, Z., & Wang, R. (2021). Combining topic modeling and SAO semantic analysis to identify technological opportunities of emerging technologies. Technological Forecasting and Social Change, 173, 121159.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 14(1), 281–297.
Marsili, O. (2001). The anatomy and evolution of industries: Technological change and industrial dynamics. Edward Elgar Publishing.
Mendonça, S., Pereira, T. S., & Godinho, M. M. (2004). Trademarks as an indicator of innovation and industrial change. Research Policy, 33(9), 1385–1404.
Nelson, R. R. (1985). An evolutionary theory of economic change. Harvard University Press.
Newman, D., Bonilla, E. V., & Buntine, W. (2011). Improving topic coherence with regularized topic models. Advances in Neural Information Processing Systems, 24.
Özel, S. Ö., & Pénin, J. (2016). Exclusive or open? An economic analysis of university intellectual property patenting and licensing strategies. Journal of Innovation Economics Management, 21(3), 133–153.
Park, H., Yoon, J., & Kim, K. (2012). Identifying patent infringement using SAO based semantic technological similarities. Scientometrics, 90(2), 515–529.
Podolny, J. M., & Stuart, T. E. (1995). A role-based ecology of technological change. American Journal of Sociology, 100(5), 1224–1260.
Porter, A. L., & Detampel, M. J. (1995). Technology opportunities analysis. Technological Forecasting and Social Change, 49(3), 237–255.
Porter, A. L., Roessner, J. D., Jin, X. Y., & Newman, N. C. (2002). Measuring national ‘emerging technology’ capabilities. Science and Public Policy, 29(3), 189–200.
Reardon, S. (2014). Text-mining offers clues to success. Nature, 509(7501), 410.
Roco, M. C., & Bainbridge, W. S. (2002). Converging technologies for improving human performance: Integrating from the nanoscale. Journal of Nanoparticle Research, 4(4), 281–295.
Rotolo, D., Hicks, D., & Martin, B. R. (2015). What is an emerging technology. Research Policy, 44(10), 1827–1843.
Schmookler, J. (1957). Inventors past and present. The Review of Economics and Statistics, 39(3), 321–333.
Song, B. W., & Luan, C. J. (2019). Impact indicator on measuring multi-dimension technological convergence. In 17th international conference on scientometrics & informetrics (ISSI2019) (Vol. I).
Song, K., Kim, K., & Lee, S. (2018). Identifying promising technologies using patents: A retrospective feature analysis and a prospective needs analysis on outlier patents. Technological Forecasting and Social Change, 128, 118–132.
Taylor, W. L. (1953). “Cloze procedure”: A new tool for measuring readability. Journalism Quarterly, 30(4), 415–433.
Teece, D. J. (1986). Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy. Research Policy, 15(6), 285–305.
Tseng, F. M., Cheng, A. C., & Peng, Y. N. (2009). Assessing market penetration combining scenario analysis, Delphi, and the technological substitution model: The case of the OLED TV market. Technological Forecasting and Social Change, 76(7), 897–909.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Wang, Q. (2018). A bibliometric model for identifying emerging research topics. Journal of the Association for Information Science and Technology, 69(2), 290–304.
WIPO. (2023). Guidelines for the wording of titles of inventions in the patent documents. Retrieved from https://www.wipo.int/export/sites/www/standards/en/pdf/03-15-01.pdf.
Yoon, B., Kim, S., Kim, S., & Seol, H. (2021). Doc2vec-based link prediction approach using SAO structures: Application to patent network. Scientometrics, 1–30.
Yoon, J., Choi, S., & Kim, K. (2011). Invention property-function network analysis of patents: A case of silicon-based thin film solar cells. Scientometrics, 86(3), 687–703.
Yun, Y., Jeonger, G. H., & Kim, S. H. (1991). A Delphi technology forecasting approach using a semi-Markov concept. Technological Forecasting and Social Change, 40(3), 273–287.
Zhang, R., Zhang, Y., Dong, Z. C., Jiang, S., Zhang, C., Chen, L. G., Zhang, L., Liao, Y., Aizpurua, J., Luo, Y. E., & Yang, J. L. (2013). Chemical mapping of a single molecule by plasmon-enhanced Raman scattering. Nature, 498(7452), 82–86.
Zhang, Y., Lu, J., Liu, F., Liu, Q., Porter, A., Chen, H., & Zhang, G. (2018). Does deep learning help topic extraction? A kernel k-means clustering method with word embedding. Journal of Informetrics, 12(4), 1099–1117.
Zhang, Y., Wu, M., Miao, W., Huang, L., & Lu, J. (2021). Bi-layer network analytics: A methodology for characterizing emerging general-purpose technologies. Journal of Informetrics, 15(4), 101202.
Zhang, Y., Zhang, G., Chen, H., Porter, A. L., Zhu, D., & Lu, J. (2016). Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research. Technological Forecasting and Social Change, 105, 179–191.
Zhang, Y., Zhang, G., Zhu, D., & Lu, J. (2017). Scientific evolutionary pathways: Identifying and visualizing relationships for scientific topics. Journal of the Association for Information Science and Technology, 68(8), 1925–1939.
Zhou, Y., Dong, F., Liu, Y., Li, Z., Du, J., & Zhang, L. (2020). Forecasting emerging technologies using data augmentation and deep learning. Scientometrics, 123(1), 1–29.
Funding
Funding was provided by National Natural Science Foundation of China (Grant Nos. 71774020, 71473028).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Song, B., Luan, C. & Liang, D. Identification of emerging technology topics (ETTs) using BERT-based model and sematic analysis: a perspective of multiple-field characteristics of patented inventions (MFCOPIs). Scientometrics 128, 5883–5904 (2023). https://doi.org/10.1007/s11192-023-04819-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-023-04819-x