Skip to main content
SpringerLink
Log in

Anticipating multi-technology convergence: a machine learning approach using patent information

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

Technology convergence has been the subject of many prior studies, yet most have focussed on the structural patterns of convergence between a pair of technologies rather than the dynamic aspects of multi-technology convergence. This study proposes a machine learning approach to anticipating multi-technology convergence using patent information. For this, a patent database is first constructed using the United States Patent and Trademark Office database, distinguishing the primary class from other patent classes to consider the direction of multi-technology convergence. Second, association rule mining is employed to construct technology ecology networks describing the significant structural patterns of multi-technology convergence for different time periods in the form of a primary patent class → supplementary patent classes. Third, the technology ecology networks between the periods are compared to identify implications on the changing patterns of multi-technology convergence. Finally, link prediction analysis based on logistic regression models is utilised to provide insight into the prospects of multi-technology convergence by identifying the links to be added to or removed from the network. Based on this, we also discuss the characteristics of the proposed approach and the technological impact and uncertainty of the identified patterns of multi-technology convergence. The case of drug, bio-affecting, and body treating compositions technology is presented herein.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. According to the United States Patent and Trademark Office, every US patent has one and only one primary class (i.e. first bold class) that represents the main idea of invention described in the patent. It is double-vetted and reliable since the primary class is used for routing the application along the patent office. If there is a mistake in primary classification, the examiner will reject the patent, and it will be reclassified and routed to a different examiner (http://www.acclaimip.com/the-us-patent-classification-system-class-types/).

References

  • Adner, R. (2006). Match your innovation strategy to your innovation ecosystem. Harvard Business Review, 84(4), 98.

    Google Scholar 

  • Adner, R., & Kapoor, R. (2010). Value creation in innovation ecosystems: How the structure of technological interdependence affects firm performance in new technology generations. Strategic Management Journal, 31(3), 306–333.

    Article  Google Scholar 

  • Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD Record, 22(2), 207–216.

    Article  Google Scholar 

  • Aharonson, B. S., & Schilling, M. A. (2016). Mapping the technological landscape: Measuring technology distance, technological footprints, and technology evolution. Research Policy, 45(1), 81–96.

    Article  Google Scholar 

  • Athreye, S., & Keeble, D. (2000). Technological convergence, globalisation and ownership in the UK computer industry. Technovation, 20(5), 227–245.

    Article  Google Scholar 

  • Barberá-Tomás, D., Jiménez-Sáez, F., & Castelló-Molina, I. (2011). Mapping the importance of the real world: The validity of connectivity analysis of patent citations networks. Research Policy, 40(3), 473–486.

    Article  Google Scholar 

  • Bonchi, F., & Geothals, B. (2004). FP-Bonsai: The art of growing and pruning small fp-trees. In Pacific-Asia conference on knowledge discovery and data mining (pp. 155–160).

  • Caviggioli, F. (2016). Technology fusion: Identification and analysis of the drivers of technology convergence using patent data. Technovation, 55, 22–32.

    Article  Google Scholar 

  • Chaudhuri, S. (2005). The WTO and India’s pharmaceuticals industry: Patent protection, TRIPS, and developing countries. Oxford: Oxford University Press.

    Google Scholar 

  • Chen, Y. S., & Chang, K. C. (2010). The relationship between a firm’s patent quality and its market value: The case of US pharmaceutical industry. Technological Forecasting and Social Change, 77(1), 20–33.

    Article  MathSciNet  Google Scholar 

  • Choi, C., & Park, Y. (2009). Monitoring the organic structure of technology based on the patent development paths. Technological Forecasting and Social Change, 76(6), 754–768.

    Article  Google Scholar 

  • Curran, C. S., Bröring, S., & Leker, J. (2010). Anticipating converging industries using publicly available data. Technological Forecasting and Social Change, 77(3), 385–395.

    Article  Google Scholar 

  • Curran, C. S., & Leker, J. (2011). Patent indicators for monitoring convergence: Examples from NFF and ICT. Technological Forecasting and Social Change, 78(2), 256–273.

    Article  Google Scholar 

  • Dosi, G. (1984). Technical change and industrial transformation: The theory and an application to the semiconductor industry. Berlin: Springer.

    Book  Google Scholar 

  • Érdi, P., Makovi, K., Somogyvári, Z., Strandburg, K., Tobochnik, J., Volf, P., et al. (2013). Prediction of emerging technologies based on analysis of the US patent citation network. Scientometrics, 95(1), 225–242.

    Article  Google Scholar 

  • Fleming, L. (2001). Recombinant uncertainty in technological search. Management Science, 47(1), 117–132.

    Article  Google Scholar 

  • Fleming, L., & Sorenson, O. (2001). Technology as a complex adaptive system: Evidence from patent data. Research Policy, 30(7), 1019–1039.

    Article  Google Scholar 

  • Geum, Y., Kim, C., Lee, S., & Kim, M. S. (2012). Technological convergence of IT and BT: Evidence from patent analysis. ETRI Journal, 34(3), 439–449.

    Article  Google Scholar 

  • Granstrand, O., & Holgersson, M. (2020). Innovation ecosystems: A conceptual review and a new definition. Technovation, 90, 102098.

    Article  Google Scholar 

  • Gyorodi, C., Gyorodi, R., Cofeey, T., & Holban, S. (2003). Mining association rules using dynamic FP-trees. In Proceedings of irish signals and systems conference (pp. 76–81).

  • Hacklin, F., Battistini, B., & Von Krogh, G. (2013). Strategic choices in converging industries. MIT Sloan Management Review, 55(1), 65.

    Google Scholar 

  • Hacklin, F., Marxt, C., & Fahrni, F. (2009). Coevolutionary cycles of convergence: An extrapolation from the ICT industry. Technological Forecasting and Social Change, 76(6), 723–736.

    Article  Google Scholar 

  • Hacklin, F., Raurich, V., & Marxt, C. (2005). Implications of technological convergence on innovation trajectories: The case of ICT industry. International Journal of Innovation and Technology Management, 2(3), 313–330.

    Article  Google Scholar 

  • Han, J., Cheng, H., Xin, D., & Yan, X. (2007). Frequent pattern mining: Current status and future directions. Data Mining Knowledge Discovery, 15(1), 55–86.

    Article  MathSciNet  Google Scholar 

  • Harhoff, D., Scherer, F. M., & Vopel, K. (2003). Citations, family size, opposition and the value of patent rights. Research Policy, 32(8), 1343–1363.

    Article  Google Scholar 

  • Jang, H. J., Woo, H. G., & Lee, C. (2017). Hawkes process-based technology impact analysis. Journal of Informetrics, 11(2), 511–529.

    Article  Google Scholar 

  • Jeong, S., Kim, J. C., & Choi, J. Y. (2015). Technology convergence: What developmental stage are we in? Scientometrics, 104(3), 841–871.

    Article  Google Scholar 

  • Jiang, Q., & Luan, C. (2018). Diffusion, convergence and influence of pharmaceutical innovations: A comparative study of Chinese and US patents. Globalization and Health, 14(1), 92.

    Article  Google Scholar 

  • Karki, M. M. S. (1997). Patent citation analysis: A policy analysis tool. World Patent Information, 19(4), 269–272.

    Article  MathSciNet  Google Scholar 

  • Kim, E., Cho, Y., & Kim, W. (2014). Dynamic patterns of technological convergence in printed electronics technologies: Patent citation network. Scientometrics, 98(2), 975–998.

    Article  Google Scholar 

  • Kim, H., Hong, S., Kwon, O., & Lee, C. (2017). Concentric diversification based on technological capabilities: Link analysis of products and technologies. Technological Forecasting and Social Change, 118, 246–257.

    Article  Google Scholar 

  • Kim, J., Kim, S., & Lee, C. (2019). Anticipating technological convergence: Link prediction using Wikipedia hyperlinks. Technovation, 79, 25–34.

    Article  Google Scholar 

  • Kim, J., & Lee, S. (2017). Forecasting and identifying multi-technology convergence based on patent data: The case of IT and BT industries in 2020. Scientometrics, 111(1), 47–65.

    Article  Google Scholar 

  • Kim, N., Lee, H., Kim, W., Lee, H., & Suh, J. H. (2015). Dynamic patterns of industry convergence: Evidence from a large amount of unstructured data. Research Policy, 44(9), 1734–1748.

    Article  Google Scholar 

  • Kim, C., Lee, H., Seol, H., & Lee, C. (2011). Identifying core technologies based on technological cross-impacts: An association rule mining (ARM) and analytic network process (ANP) approach. Expert Systems with Applications, 38(10), 12559–12564.

    Article  Google Scholar 

  • Kwon, O., An, Y., Kim, M., & Lee, C. (2020). Anticipating technology-driven industry convergence: Evidence from large-scale patent analysis. Technology Analysis and Strategic Management, 32(4), 363–378.

    Article  Google Scholar 

  • Lee, C., Cho, Y., Seol, H., & Park, Y. (2012). A stochastic patent citation analysis approach to assessing future technological impacts. Technological Forecasting and Social Change, 79(1), 16–29.

    Article  Google Scholar 

  • Lee, W. S., Han, E. J., & Sohn, S. Y. (2015a). Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents. Technological Forecasting and Social Change, 100, 317–329.

    Article  Google Scholar 

  • Lee, C., Kang, B., & Shin, J. (2015b). Novelty-focused patent mapping for technology opportunity analysis. Technological Forecasting and Social Change, 90, 355–365.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Lee, C., & Lee, G. (2019). Technology opportunity analysis based on recombinant search: Patent landscape analysis for idea generation. Scientometrics, 121(2), 603–632.

    Article  Google Scholar 

  • Liu, G., Lu, H., Yu, J. X., Wang, W., & Xiao, X. (2003). AFOPT: An efficient implementation of pattern growth approach. In Proceedings of ICDM workshop on frequent itemset mining implementations.

  • Narin, F., Noma, E., & Perry, R. (1987). Patents as indicators of corporate technological strength. Research Policy, 16(2–4), 143–155.

    Article  Google Scholar 

  • No, H. J., & Park, Y. (2010). Trajectory patterns of technology fusion: Trend analysis and taxonomical grouping in nanobiotechnology. Technological Forecasting and Social Change, 77(1), 63–75.

    Article  Google Scholar 

  • Oh, D. S., Phillips, F., Park, S., & Lee, E. (2016). Innovation ecosystems: A critical examination. Technovation, 54, 1–6.

    Article  Google Scholar 

  • Powers, D. M. (2011). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technologies, 2(1), 37–63.

    MathSciNet  Google Scholar 

  • Shih, M. J., Liu, D. R., & Hsu, M. L. (2010). Discovering competitive intelligence by mining changes in patent trends. Expert Systems with Applications, 37(4), 2882–2890.

    Article  Google Scholar 

  • Shin, J., Coh, B. Y., & Lee, C. (2013). Robust future-oriented technology portfolios: Black–Litterman approach. R&D Management, 43(5), 409–419.

    Article  Google Scholar 

  • Xu, G., Wu, Y., Minshall, T., & Zhou, Y. (2018). Exploring innovation ecosystems across science, technology, and business: A case of 3D printing in China. Technological Forecasting and Social Change, 136, 208–221.

    Article  Google Scholar 

  • Youden, W. J. (1950). Index for rating diagnostic tests. Cancer, 3(1), 32–35.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIP) (No. 2019R1A6A3A13096839) and and the Sogang University Research Grant of 2020 (No. 202010009.01).

Author information

Authors and Affiliations

  1. Graduate School of Management of Technology, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul, 04107, Republic of Korea

    Changyong Lee

  2. Department of Management Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulsan, 44919, Republic of Korea

    Suckwon Hong

  3. Department of Industrial Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulsan, 44919, Republic of Korea

    Juram Kim

Authors
  1. Changyong Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suckwon Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Juram Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Changyong Lee.

Appendices

Appendix 1: An example of constructing a technology ecology network

We present a step-by-step example of constructing a technology ecology network describing the significant structural patterns of multi-technology convergence. Five patents are used in this example, as shown in Table 8, and the threshold values for CP and CI are set to 0.2 and 0.5.

Table 8 Five patents used in this example
Full size table

First, we identify frequently co-occurred structural patterns based on the Apriori algorithm. Specifically, the frequent individual primary and supplementary classes (i.e. one-item set) that exceed the prescribed threshold values for CP (i.e. 0.2) are identified; the frequent patterns with one primary class and one supplementary class (i.e. two-item set) are identified from the one-item set; the two-item set is extended to the three-item set with one primary class and two supplementary classes by adding one supplementary class at a time. This extension process terminates when no further extensions are found. Table 9 shows the results of Apriori algorithm employed in this study, where the frequently co-occurred structural patterns derived from the five patents are highlighted in bold.

Table 9 Results of the Apriori algorithm employed in this study
Full size table

Next, we compute the values of CI and CS for the frequently co-occurred patterns, as shown in Table 10. Three patterns are identified as significant as their CI and CS values are greater than the threshold value for CI (i.e. 0.5) and one, respectively.

Table 10 List of the significant structural patterns
Full size table

Finally, the significant structural patterns are represented as the technology ecology network. In Fig. 5, a source and a target node are a primary class and supplementary classes; the size of a node represents the number of the corresponding pattern’s occurrence; a link represents the CI among the associated classes. White and grey nodes denote single and multiple patent classes; blue and red links indicate within-technology convergence where the primary patent class also appears in the supplementary patent class part and between-technology convergence where the primary patent class is not included in the patent class segment, respectively.

Fig. 5
figure 5

The resulting technology ecology network

Full size image

Appendix 2

See Table 11.

Table 11 The description of USPC mainline subclasses
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, C., Hong, S. & Kim, J. Anticipating multi-technology convergence: a machine learning approach using patent information. Scientometrics 126, 1867–1896 (2021). https://doi.org/10.1007/s11192-020-03842-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-020-03842-6

Keywords

Search

Navigation