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Scientometrics

, Volume 118, Issue 1, pp 45–70 | Cite as

Investigating technology opportunities: the use of SAOx analysis

  • Kyuwoong Kim
  • Kyeongmin Park
  • Sungjoo LeeEmail author
Article
  • 130 Downloads

Abstract

A patent is regarded as one of the most reliable data sources to investigate such opportunities and has been analyzed in numerous ways. The recent trend of patent analysis has focused on the unstructured part of patent information to extract detailed technological information. In particular, information regarding the purpose or effect of technology, which can be pulled from the unstructured part of patent information, is expected to offer useful insights into expanding its application to other areas. Some previous attempts have been made to systematically use this information to identify new technology opportunities, partly due to difficulties in analyzing the unstructured text data in patent documents. To overcome the limitations of previous studies, this study aims to develop a new method, namely Subject–Action–Object–others (SAOx), which enables an in-depth examination of the purpose and effect of the technology in an efficient manner by analyzing “for” and “to” phrases as well as gerund forms for an object element. We also introduce 39 engineering parameters of TRIZ and technology-designative terms of patent documents to define SAO sets and improve information accuracy. The proposed method is applied to human–machine interaction technologies to understand technology trends and explore technology opportunities based on topic modeling. Methodologically, the research findings contribute to patent engineering by extending the range of information extracted from patent information. Practically, the proposed approach will support corporate decision making in R&D investment by providing comprehensive information regarding the purpose or effect of technology in a structured form, fully extracted from patent documents.

Keywords

Technology opportunity Patent SAO analysis Topic modeling 

Mathematics Subject Classification

68 W 

JEL Classification

O32 O34 

Notes

Acknowledgements

This paper was funded by National Research Foundation of Korea (NRF-2016R1D1A1B03933943).

References

  1. Altshuller, G. S. (1984). Creativity as an exact science: The theory of the solution of inventive problems. London: Gordon and Breach.Google Scholar
  2. Chang, H. T., & Chen, J. L. (2004). The conflict-problem-solving CAD software integrating TRIZ into eco-innovation. Advances in Engineering Software, 35(8), 553–566.CrossRefGoogle Scholar
  3. Cho, C., Yoon, B., Coh, B. Y., & Lee, S. (2016). An empirical analysis on purposes, drivers and activities of technology opportunity discovery: The case of Korean SMEs in the manufacturing sector. R & D Management, 46(1), 13–35.CrossRefGoogle Scholar
  4. Choi, S., Kim, H., Yoon, J., Kim, K., & Lee, J. Y. (2013). An SAO-based text-mining approach for technology roadmapping using patent information. R&D Management, 43(1), 52–74.CrossRefGoogle Scholar
  5. 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.CrossRefGoogle Scholar
  6. Choi, S., Park, H., Kang, D., Lee, J. Y., & Kim, K. (2012). An SAO-based text mining approach to building a technology tree for technology planning. Expert Systems with Applications, 39(13), 11443–11455.CrossRefGoogle Scholar
  7. Dao, T. N., & Simpson, T. (2005). Measuring similarity between sentences. WorldNet.Net, Technical report.Google Scholar
  8. Ernst, H. (2003). Patent information for strategic technology management. World Patent Information, 25(3), 233–242.CrossRefGoogle Scholar
  9. 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.CrossRefGoogle Scholar
  10. Guo, J., Wang, X., Li, Q., & Zhu, D. (2016). Subject–action–object-based morphology analysis for determining the direction of technological change. Technological Forecasting and Social Change, 105, 27–40.CrossRefGoogle Scholar
  11. Jeong, Y., & Yoon, B. (2015). Development of patent roadmap based on technology roadmap by analyzing patterns of patent development. Technovation, 39, 37–52.CrossRefGoogle Scholar
  12. Jeong, B., & Yoon, J. (2017). Competitive intelligence analysis of augmented reality technology using patent information. Sustainability, 9(4), 497.CrossRefGoogle Scholar
  13. Kim, J., & Lee, S. (2015). Patent databases for innovation studies: A comparative analysis of USPTO, EPO, JPO and KIPO. Technological Forecasting and Social Change, 92, 332–345.CrossRefGoogle Scholar
  14. Kim, Y. G., Suh, J. H., & Park, S. C. (2008). Visualization of patent analysis for emerging technology. Expert Systems with Applications, 34(3), 1804–1812.CrossRefGoogle Scholar
  15. Lafferty, J. D., & Blei, D. M. (2006). Correlated topic models. Advances in neural information processing systems, 18, 147–154.Google Scholar
  16. Lee, J., Kim, C., & Shin, J. (2017). Technology opportunity discovery to R&D planning: Key technological performance analysis. Technological Forecasting and Social Change, 119, 53–63.CrossRefGoogle Scholar
  17. Lee, Y., Kim, S. Y., Song, I., Park, Y., & Shin, J. (2014). Technology opportunity identification customized to the technological capability of SMEs through two-stage patent analysis. Scientometrics, 100(1), 227–244.CrossRefGoogle Scholar
  18. Lee, S., Lee, S., Seol, H., & Park, Y. (2008). Using patent information for designing new product and technology: Keyword based technology roadmapping. R&D Management, 38(2), 169–188.CrossRefGoogle Scholar
  19. Lee, C., Song, B., & Park, Y. (2013). How to assess patent infringement risks: A semantic patent claim analysis using dependency relationships. Technology Analysis & Strategic Management, 25(1), 23–38.CrossRefGoogle Scholar
  20. Lee, S., Yoon, B., Lee, C., & Park, J. (2009a). Business planning based on technological capabilities: Patent analysis for technology-driven roadmapping. Technological Forecasting and Social Change, 76(6), 769–786.CrossRefGoogle Scholar
  21. Lee, S., Yoon, B., & Park, Y. (2009b). An approach to discovering new technology opportunities: Keyword-based patent map approach. Technovation, 29(6), 481–497.CrossRefGoogle Scholar
  22. Mann, D. (2001). An introduction to TRIZ: The theory of inventive problem solving. Creativity and Innovation Management, 10(2), 123–125.MathSciNetCrossRefGoogle Scholar
  23. Moehrle, M. G., Walter, L., Geritz, A., & Muller, S. (2005). Patent-based inventor profiles as a basis for human resource decisions in research and development. R&D Management, 35(5), 513–524.CrossRefGoogle Scholar
  24. No, H. J., & Lim, H. (2009). Exploration of nanobiotechnologies using patent data. The Journal of Intellectual Property, 4(3), 109–129.Google Scholar
  25. Noh, H., Jo, Y., & Lee, S. (2015). Keyword selection and processing strategy for applying text mining to patent analysis. Expert Systems with Applications, 42(9), 4348–4360.CrossRefGoogle Scholar
  26. Park, H., Ree, J. J., & Kim, K. (2013a). Identification of promising patents for technology transfers using TRIZ evolution trends. Expert Systems with Applications, 40(2), 736–743.CrossRefGoogle Scholar
  27. Park, Y., & Yoon, J. (2017). Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering. Technological Forecasting and Social Change, 118, 170–183.CrossRefGoogle Scholar
  28. Park, H., Yoon, J., & Kim, K. (2011). Identifying patent infringement using SAO based semantic technological similarities. Scientometrics, 90(2), 515–529.CrossRefGoogle Scholar
  29. Park, H., Yoon, J., & Kim, K. (2013b). Using function-based patent analysis to identify potential application areas of technology for technology transfer. Expert Systems with Applications, 40(13), 5260–5265.CrossRefGoogle Scholar
  30. Park, Y., Yoon, B., & Lee, S. (2005). The idiosyncrasy and dynamism of technological innovation across industries patent citation analysis. Technology in Society, 27(4), 471–485.CrossRefGoogle Scholar
  31. Pilkington, A., Lee, L. L., Chan, C. K., & Ramakrishna, S. (2009). Defining key inventors: A comparison of fuel cell and nanotechnology industries. Technological Forecasting and Social Change, 76(1), 118–127.CrossRefGoogle Scholar
  32. Wang, X., Wang, Z., Huang, Y., Liu, Y., Zhang, J., Heng, X., et al. (2017). Identifying R&D partners through subject–action–object semantic analysis in a problem & solution pattern. Technology Analysis & Strategic Management, 29, 1–14.CrossRefGoogle Scholar
  33. Wich, Y., Warschat, J., Spath, D., Ardilio, A., König-Urban, K., & Uhlmann, E. (2013, July). Using a text mining tool for patent analyses: Development of a new method for the repairing of gas turbines. In 2013 Proceedings of PICMET’13 Technology Management in the IT-Driven Services (PICMET) (pp. 1010–1016). IEEE.Google Scholar
  34. Yau, C. K., Porter, A., Newman, N., & Suominen, A. (2014). Clustering scientific documents with topic modeling. Scientometrics, 100(3), 767–786.CrossRefGoogle Scholar
  35. Yoon, J., & Kim, K. (2011a). An automated method for identifying TRIZ evolution trends from patents. Expert Systems with Applications, 38(12), 15540–15548.CrossRefGoogle Scholar
  36. Yoon, J., & Kim, K. (2011b). Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks. Scientometrics, 88(1), 213–228.CrossRefGoogle Scholar
  37. Yoon, J., & Kim, K. (2012a). Detecting signals of new technological opportunities using semantic patent analysis and outlier detection. Scientometrics, 90(2), 445–461.CrossRefGoogle Scholar
  38. Yoon, J., & Kim, K. (2012b). TrendPerceptor: A property–function based technology intelligence system for identifying technology trends from patents. Expert Systems with Applications, 39(3), 2927–2938.CrossRefGoogle Scholar
  39. Yoon, B., & Park, Y. (2005). A systematic approach for identifying technology opportunities: Keyword-based morphology analysis. Technological Forecasting and Social Change, 72(2), 145–160.CrossRefGoogle Scholar
  40. Yoon, B., Park, I., & Coh, B. Y. (2014). Exploring technological opportunities by linking technology and products: Application of morphology analysis and text mining. Technological Forecasting and Social Change, 86, 287–303.CrossRefGoogle Scholar
  41. Yoon, J., Park, H., & Kim, K. (2013). Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis. Scientometrics, 94(1), 313–331.CrossRefGoogle Scholar
  42. Yoon, J., Park, H., Seo, W., Lee, J. M., Coh, B. Y., & Kim, J. (2015). Technology opportunity discovery (TOD) from existing technologies and products: A function-based TOD framework. Technological Forecasting and Social Change, 100, 153–167.CrossRefGoogle Scholar
  43. Yoon, B. U., Yoon, C. B., & Park, Y. T. (2002). On the development and application of a self-organizing feature map-based patent map. R&D Management, 32(4), 291–300.CrossRefGoogle Scholar
  44. Zhang, Y., Zhou, X., Porter, A. L., & Gomila, J. M. V. (2014). How to combine term clumping and technology roadmapping for newly emerging science & technology competitive intelligence: “Problem & solution” pattern based semantic TRIZ tool and case study. Scientometrics, 101(2), 1375–1389.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2018

Authors and Affiliations

  1. 1.Department of Industrial EngineeringAjou UniversitySuwonRepublic of Korea

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