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A hybrid approach to detecting technological recombination based on text mining and patent network analysis

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Abstract

Detecting promising technology groups for recombination holds the promise of great value for R&D managers and technology policymakers, especially if the technologies in question can be detected before they have been combined. However, predicting the future is always easier said than done. In this regard, Arthur’s theory (The nature of technology: what it is and how it evolves, Free Press, New York, 2009) on the nature of technologies and how science evolves, coupled with Kuhn’s theory of scientific revolutions (Kuhn in The structure of scientific revolutions, 1st edn, University of Chicago Press, Chicago, p 3, 1962), may serve as the basis of a shrewd methodological framework for forecasting recombinative innovation. These theories help us to set out quantifiable criteria and decomposable steps to identify research patterns at each stage of a scientific revolution. The first step in the framework is to construct a conceptual model of the target technology domain, which helps to refine a reasonable search strategy. With the model built, the landscape of a field—its communities, its technologies, and their interactions—is fleshed out through community detection and network analysis based on a set of quantifiable criteria. The aim is to map normal patterns of research in the domain under study so as to highlight which technologies might contribute to a structural deepening of technological recombinations. Probability analysis helps to detect and group candidate technologies for possible recombination and further manual analysis by experts. To demonstrate how the framework works in practice, we conducted an empirical study on AI research in China. We explored the development potential of recombinative technologies by zooming in on the top patent assignees in the field and their innovations. In conjunction with expert analysis, the results reveal the cooperative and competitive relationships among these technology holders and opportunities for future innovation through technological recombinations.

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References

  • Albert, R. (2002). Barab’asi: Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1), 47.

    Article  MathSciNet  MATH  Google Scholar 

  • Alpaydin, E. (2016). Neural networks and deep learning. Radial basis function networks (pp. 217–233). New York: Springer.

    Google Scholar 

  • Arjun, K. (2016). Japan’s Softbank to buy chip-design powerhouse ARM for $32 billion. https://www.cnbc.com/2016/07/17/softbank-poised-to-take-uks-arm-for-234-billion.html. Accessed 19 Mar 2019.

  • Arthur, W. B. (2009). The nature of technology: What it is and how it evolves. New York: Free Press.

    Google Scholar 

  • Babaeizadeh, M., Frosio, I., Tyree, S., et al. (2016). Reinforcement learning through asynchronous advantage actor-critic on a GPU. Published as a conference paper at international conference on learning representations (ICLR), France.

  • Babbar, S., Smart, P. A., & Behara, R. S. (2014). Leadership in OM research: A social network analysis of European researchers. International Journal of Operations & Production Management, 34(12), 1537–1563.

    Article  Google Scholar 

  • Bae, H. S., Lee, H. J., & Lee, S. G. (2016). Voice recognition-based on adaptive MFCC and deep learning for embedded systems. Journal of Institute of Control Robotics & Systems, 22, 1542–1546.

    Article  Google Scholar 

  • Barnett, G. (2011). Recombinant innovation. Science, 334, 1497.

    Article  Google Scholar 

  • Bunz, M., & Janciute, L. (2017). Artificial intelligence and the internet of things: Opportunities and challenges for UK’s policy makers. Washington, DC: Springer.

    Google Scholar 

  • Burse, K., Manoria, M., & Kirar, V. P. S. (2011). Improved back propagation algorithm to avoid local minima in multiplicative neuron model. In Information technology and mobile communication. Berlin: Springer.

  • Carley, S. F., Newman, N. C., Porter, A. L., & Garner, J. G. (2017). A measure of staying power: Is the persistence of emergent concepts more significantly influenced by technical domain or scale? Scientometrics, 111(3), 2077–2087.

    Article  Google Scholar 

  • Carley, S. F., Newman, N. C., Porter, A. L., & Garner, J. G. (2018). An indicator of technical emergence. Scientometrics, 115(1), 35–49.

    Article  Google Scholar 

  • Carnabuci, G., & Operti, E. (2013). Where do firms’ recombinant capabilities come from? Intraorganizational networks, knowledge, and firms’ ability to innovate through technological recombination. Strategic Management Journal, 34(13), 1591–1613.

    Article  Google Scholar 

  • China, Times. (2016). AMD helps Inspur to enter into the chip industry. http://tech.hqew.com/news_646696. Accessed 20 June 2018.

  • Choi, J., & Hwang, Y. S. (2014). Patent keyword network analysis for improving technology development efficiency. Technological Forecasting and Social Change, 83(1), 170–182.

    Article  Google Scholar 

  • Choi, S., Yoon, J., Kim, K., et al. (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.

    Article  Google Scholar 

  • Corredoira, R. A., & Banerjee, P. M. (2015). Measuring patent’s influence on technological evolution: A study of knowledge spanning and subsequent inventive activity. Research Policy, 44(2), 508–521.

    Article  Google Scholar 

  • Corrocher, N., Malerba, F., & Montobbio, F. (2003). The emergence of new technologies in the ICT field: Main actors, geographical distribution and knowledge sources. Working papers of Faculty of Economics, Universit degli Studi dell’Insubri.

  • Corrocher, N., & Zirulia, L. (2010). Demand and innovation in services: The case of mobile communications. Research Policy, 39(7), 945–955.

    Article  MATH  Google Scholar 

  • Dhabe, P. S., & Yengul, N. R. (2017). GPU parallelization of fuzzy min–max clustering neural network for pattern recognition. Impact Journals, 5(6), 55–68.

    Google Scholar 

  • Dotsika, F., & Watkins, A. (2017). Identifying potentially disruptive trends by means of keyword network analysis. Technological Forecasting & Social Change, 119, 114–127.

    Article  Google Scholar 

  • Elish, M. C., & Boyd, D. (2017). Situating methods in the magic of big data and AI. Communication Monographs, 85(1), 1–24.

    Google Scholar 

  • Fleming, L. (2001). Recombinant uncertainty in technological search. Catonsville: INFORMS.

    Book  Google Scholar 

  • Fleming, L., & Sorenson, O. (2004). Science as map in technological search. Strategic Management Journal, 25(8–9), 909–928.

    Article  Google Scholar 

  • Fortunato, S. (2009). Community detection in graphs. Physics Reports, 486(3), 75–174.

    MathSciNet  Google Scholar 

  • Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41.

    Article  Google Scholar 

  • Freniere, C., Pathak, A., Raessi, M., & Khanna, G. (2016). The feasibility of Amazon’s cloud computing platform for parallel, GPU-accelerated, multiphase-flow simulations. Computing in Science & Engineering, 18(5), 68–77.

    Article  Google Scholar 

  • Frutospascual, M., & Garcia Zapirain, B. (2017). Review of the use of ai techniques in serious games: Decision making and machine learning. IEEE Transactions on Computational Intelligence & AI in Games, 9(2), 133–152.

    Article  Google Scholar 

  • Funk, R. J., & Owen-Smith, J. (2016). A dynamic network measure of technological change. Management Science, 63(3), 791–817.

    Article  Google Scholar 

  • Gallouj, F., & Weinstein, O. (1997). Innovation in services. Research Policy, 26(4–5), 537–556.

    Article  Google Scholar 

  • Gruber, M., & Hoisl, K. (2013). Knowledge recombination across technological boundaries: Scientists vs. engineers. Management Science, 59(4), 837–851.

    Article  Google Scholar 

  • Guan, J. C., & Yan, Y. (2016). Technological proximity and recombinative innovation in the alternative energy field. Research Policy, 45(7), 1460–1473.

    Article  Google Scholar 

  • Gys, M., & Hermanus, M. (2015). A review of intelligent driving style analysis systems and related artificial intelligence algorithms. Sensors, 15(12), 30653–30682.

    Article  Google Scholar 

  • Halim, Z., Kalsoom, R., Bashir, S., & Abbas, G. (2016). Artificial intelligence techniques for driving safety and vehicle crash prediction. Artificial Intelligence Review, 46(3), 351–387.

    Article  Google Scholar 

  • Hashimoto, D. A., Rosman, G., Rus, D., & Meireles, O. R. (2018). Artificial intelligence in surgery: Promises and perils. Annals of Surgery, 268(1), 1.

    Article  Google Scholar 

  • Henderson, R. M., & Clark, K. B. (1990). Architectural innovation: The reconfiguration of existing product technologies and the failure of established firms. Administrative Science Quarterly, 35(1), 9–30.

    Article  Google Scholar 

  • Hossain, L., Karimi, F., Wigand, R. T., et al. (2015). Evolutionary longitudinal network dynamics of global zoonotic research. Scientometrics, 103(2), 337–353.

    Article  Google Scholar 

  • iFlytek. (2014). HIT·iFLYTEK Language Cognitive Computing Lab has been established. https://mp.weixin.qq.com/s?__biz=MzA4NjM4ODQzNQ%3D%3D&idx=1&mid=201224250&sn=02be994930b7a57f0a83e794d040d6a0.

  • Inspur. (2016). Inspur-Cisco Joint Venture is officially inaugurated. Inspur company news. http://www.inspur.com/inspur/495255/494903/2313436/index.html. Accessed 17 Apr 2018.

  • Jesus, S. G., et al. (2015). Sentiment analysis: A review and comparative analysis of web services. Information Sciences, 311, 18–38.

    Article  Google Scholar 

  • Kassianos, G., Kuchar, E., Nitsch-Osuch, A., Kyncl, J., Galev, A., Humolli, I., et al. (2018). Motors of influenza vaccination uptake and vaccination advocacy in healthcare workers: A comparative study in six european countries. Vaccine, 36, 6546–6552.

    Article  Google Scholar 

  • Kelly, K. (2016). The inevitable: Understanding the 12 technological forces that will shape our future. In The inevitable: Understanding the 12 technological forces that will shape our future.

  • Keupp, M. M., & Gassmann, O. (2013). Resource constraints as triggers of radical innovation: Longitudinal evidence from the manufacturing sector. Research Policy, 42(8), 1457–1468.

    Article  Google Scholar 

  • Kim, B., Gazzola, G., Lee, J. M., et al. (2014). Inter-cluster connectivity analysis for technology opportunity discovery. Scientometrics, 98(3), 1811–1825.

    Article  Google Scholar 

  • Kuhn, T. S. (1962). The structure of scientific revolutions (1st ed., p. 3). Chicago, IL: University of Chicago Press. ISBN: 0-226-45807-5.

    Google Scholar 

  • Kuhn, T. S. (1996). The structure of scientific revolutions (3rd ed.). Chicago, IL: University of Chicago Press. ISBN: 9780226458083.

    Book  Google Scholar 

  • Lee, P. C., & Su, H. N. (2010). Investigating the structure of regional innovation system research through keyword co-occurrence and social network analysis. Innovation, 12(1), 26–40.

    Article  Google Scholar 

  • Lee, S., Yoon, B., Lee, C., et al. (2009). Business planning based on technological capabilities: Patent analysis for technology-driven roadmapping. Technological Forecasting and Social Change, 76(6), 769–786.

    Article  Google Scholar 

  • Lee, Y., Kim, S. Y., Song, I., et al. (2014). Technology opportunity identification customized to the technological capability of SMEs through two-stage patent analysis. Scientometrics, 100(1), 227–244.

    Article  Google Scholar 

  • Liu, B., & Zhang, L. (2012). Mining Text Data. A survey of opinion mining and sentiment analysis (pp. 415–463). New York: Springer.

    Google Scholar 

  • Liu, Z., Yin, Y., Liu, W., et al. (2015). Visualizing the intellectual structure and evolution of innovation systems research: A bibliometric analysis. Scientometrics, 103(1), 135–158.

    Article  Google Scholar 

  • Ma, T., Zhang, Y., Huang, L., et al. (2017). Text mining to gain technical intelligence for acquired target selection: A case study for China’s computer numerical control machine tools industry. Technological Forecasting and Social Change, 116, 162–180.

    Article  Google Scholar 

  • Makridakis, S. (2017). The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46–60.

    Article  Google Scholar 

  • MediaTek. (2017a). MediaTek Unveils its first NB-IoT SoC and announces China mobile collaboration to build industry’s smallest NB-IoT module. https://www.mediatek.com/news-events/press-releases/mediatek-unveils-its-first-nb-iot-soc-and-announces-china-mobile-collaboration-to-build-industrys-smallest-nb-iot-module. Accessed 27 July 2019.

  • MediaTek. (2017b). MediaTek announces interoperability tests with SoftBank to drive NB-IoT development in Japan. https://www.mediatek.com/news-events/press-releases/mediatek-announces-interoperability-tests-with-softbank-to-drive-nb-iot-development-in-japan. Accessed 19 July 2019.

  • Meyer, D. (2014). Google unveils open automotive alliance, featuring GM, Audi, Nvidia and others. https://gigaom.com/2014/01/06/google-unveils-open-automotive-alliance-featuring-gm-audi-nvidia-and-others/. Accessed 28 Mar 2018.

  • Miao, Y., & Metze, F. (2016). Open-domain audio-visual speech recognition: A deep learning approach. In INTERSPEECH (pp. 3414–3418).

  • Movidius. (2016). Movidius + Intel = vision for the future of autonomous devices. https://www.movidius.com/news/ceo-post-september-2016. Accessed 29 Apr 2019.

  • Nakamura, H., Suzuki, S., Sakata, I., & Kajikawa, Y. (2015). Knowledge combination modeling: The measurement of knowledge similarity between different technological domains. Technological Forecasting and Social Change, 94(116), 187–201.

    Article  Google Scholar 

  • Naveen, R. (2016). Intel + Nervana. https://www.intelnervana.com/intel-nervana/. Accessed 5 May 2019.

  • Pan, Y. (2016). Heading toward artificial intelligence 2.0. Engineering, 2(4), 409–413.

    Article  MathSciNet  Google Scholar 

  • Park, H., Kim, K., Choi, S., et al. (2013). A patent intelligence system for strategic technology planning. Expert Systems with Applications, 40(7), 2373–2390.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Perez, C. A. (2018). The brain of the future and the viability of democratic governance: The role of artificial intelligence, cognitive machines, and viable systems. Futures, 103, 5–16.

    Article  Google Scholar 

  • Peter, S., Rodney, B., & Erik, B. (2016). One hundred year study on artificial intelligence (AI100), Stanford University. https://ai100.stanford.edu. Accessed 19 May 2019.

  • Pokkuluri, K. S., & Nedunuri, U. D. (2017). Deep learning the future of artificial intelligence. Turkey, DC: LAP LAMBERT Academic Publishing.

    Google Scholar 

  • Porter, A. L., & Cunningham, S. W. (2005). Tech mining: Exploiting new technologies for competitive advantage. New York: Wiley.

    Google Scholar 

  • Rajpurkar, P., Zhang, J., Lopyrev, K., et al. (2016). SQuAD: 100,000+ questions for machine comprehension of text. Proceedings of the 2016 conference on empirical methods in natural language processing (pp. 2383–2392), Texas.

  • Rodan, S., & Galunic, D. C. (1998). Resource recombinations in the firm: Knowledge structures and the potential for schumpeterian innovation. Strategic Management Journal, 19, 1193–1201.

    Article  Google Scholar 

  • Rotolo, D., Hicks, D., & Martin, B. R. (2015). What is an emerging technology? Research Policy, 44(10), 1827–1843.

    Article  Google Scholar 

  • Ruiz, M., Alvarez, E., Serrano, A., et al. (2016). The convergence between wireless sensor networks and the internet of things; challenges and perspectives: A survey. IEEE Latin America Transactions, 14(10), 4249–4254.

    Article  Google Scholar 

  • Sabidussi, G. (1966). The centrality index of a graph. Psychometrika, 31(4), 581–603.

    Article  MathSciNet  MATH  Google Scholar 

  • Saltos, R., Weber, R., & Maldonado, S. (2017). Dynamic rough-fuzzy support vector clustering. IEEE Transactions on Fuzzy Systems, 25(6), 1508–1521.

    Article  Google Scholar 

  • Schilling, M. A., & Green, E. (2011). Recombinant search and breakthrough idea generation: An analysis of high impact papers in the social sciences. Research Policy, 40(10), 1321–1331.

    Article  Google Scholar 

  • Shah, S. A. A., Bennamoun, M., & Boussaid, F. (2016). Iterative deep learning for image set based face and object recognition. Neurocomputing, 174, 866–874.

    Article  Google Scholar 

  • Small, H., Boyack, K. W., & Klavans, R. (2014). Identifying emerging topics in science and technology. Research Policy, 43(8), 1450–1467.

    Article  Google Scholar 

  • SOHU. (2003). Huazhong University of Science and Technology-Inspur High Performance Bioinformatics Center was officially opened. http://news.sohu.com/10/04/news214870410.shtml. Accessed 28 Aug 2018.

  • SOHU. (2017). HUAWEI’s new Kirin chip will support the development of 5G. http://www.sohu.com/a/149537785_631742. Accessed 6 Oct 2018.

  • Stergiou, C., & Psannis, K. E. (2017). Recent advances delivered by mobile cloud computing and Internet of Things for big data applications: A survey. International Journal of Network Management, 27(3), e1930.

    Article  Google Scholar 

  • Sundbo, J., & Gallouj, F. (2000). Innovation systems in the service economy. Innovation as a loosely coupled system in services (pp. 15–36). Berlin: Springer.

    Google Scholar 

  • Takano, Y., Mejia, C., & Kajikawa, Y. (2016). Unconnected component inclusion technique for patent network analysis: Case study of Internet of Things-related technologies. Journal of Informetrics, 10(4), 967–980.

    Article  Google Scholar 

  • Tripathy, B. K. (2017). Internet of Things and artificial intelligence: A new road to future digital world. In B. K. Tripathy (Ed.), Internet of Things (pp. 41–58). New York: CRC Press.

    Google Scholar 

  • van den Bergh, J. C. J. M. (2008). Optimal diversity: Increasing returns versus recombinant innovation. Journal of Economic Behavior & Organization, 68(3), 565–580.

    Article  Google Scholar 

  • Viejo, C. G., Fuentes, S., Howell, K., Torrico, D., & Dunshea, F. R. (2018). Robotics and computer vision techniques combined with non-invasive consumer biometrics to assess quality traits from beer foamability using machine learning: A potential for artificial intelligence applications. Food Control, 92, 72–79.

    Article  Google Scholar 

  • Wang, X., Zheng, X., Zhang, Q., et al. (2016a). Crowdsourcing in ITS: The state of the work and the networking. IEEE Transactions on Intelligent Transportation Systems, 17(6), 1596–1605.

    Article  Google Scholar 

  • Wang, Y., Widrow, B., Zadeh, L. A., et al. (2016b). Cognitive intelligence: Deep learning, thinking, and reasoning by brain-inspired systems. International Journal of Cognitive Informatics & Natural Intelligence, 10(41–20), 1–20.

    Google Scholar 

  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440.

    Article  MATH  Google Scholar 

  • Williams, O., & Genio, C. I. D. (2014). Degree correlations in directed scale-free networks. PLoS ONE, 9(10), e110121–e110121.

    Article  Google Scholar 

  • Wood, J., & Khan, G. F. (2015). International trade negotiation analysis: Network and semantic knowledge infrastructure. Scientometrics, 105(1), 1–20.

    Article  Google Scholar 

  • Xiong, C. (2017). Recent progress in deep reinforcement learning for computer vision and NLP. In Workshop (pp. 1–1).

  • Yang, S., Han, R., Wolfram, D., et al. (2016). Visualizing the intellectual structure of information science (2006–2015): Introducing author keyword coupling analysis. Journal of Informetrics, 10(1), 132–150.

    Article  Google Scholar 

  • Yayavaram, S., & Ahuja, G. (2008). Decomposability in knowledge structures and its impact on the usefulness of inventions and knowledge-base malleability. Administrative Science Quarterly, 53(2), 333–362.

    Article  Google Scholar 

  • Ye, C., Liu, D., Chen, N., et al. (2015). Mapping the topic evolution using citation-topic model and social network analysis. In International conference on fuzzy systems and knowledge discovery (pp. 2648–2653). IEEE.

  • Yoon, B., & Park, Y. (2007). Development of new technology forecasting algorithm: Hybrid approach for morphology analysis and conjoint analysis of patent information. IEEE Transactions on Engineering Management, 54(3), 588–599.

    Article  Google Scholar 

  • Zaltz, A. M., O’Brien, O., Strano, E., et al. (2013). The structure of spatial networks and communities in bicycle sharing systems. PLoS ONE, 8(9), e74685.

    Article  Google Scholar 

  • Zhang, Q. R., Li, Y., Liu, J. S., et al. (2017a). A dynamic co-word network-related approach on the evolution of China’s urbanization research. Scientometrics, 111(3), 1623–1642.

    Article  Google Scholar 

  • Zhang, Y., Porter, A. L., Hu, Z., Guo, Y., & Newman, N. C. (2014). “Term clumping” for technical intelligence: A case study on dye-sensitized solar cells. Technological Forecasting and Social Change, 85, 26–39.

    Article  Google Scholar 

  • Zhang, Y., Shang, L., Huang, L., et al. (2016). A hybrid similarity measure method for patent portfolio analysis. Journal of Informetrics, 10(4), 1108–1130.

    Article  Google Scholar 

  • Zhang, Y., Zhang, G., Zhu, D., et al. (2017b). Scientific evolutionary pathways: Identifying and visualizing relationships for scientific topics. Journal of the Association for Information Science & Technology, 68(8), 1925–1939.

    Article  Google Scholar 

  • Zhou, X., Porter, A., Robinson, D. K. R., et al. (2013). Analyzing research publication patterns to gauge future innovation pathways for nano-enabled drug delivery. In Technology management in the it-driven services (pp. 2188–2199). IEEE.

  • Zhou, X., Porter, A. L., Robinson, D. K. R., et al. (2014a). Nano-enabled drug delivery: A research profile. Nanomedicine: Nanotechnology, Biology and Medicine, 10(5), 889–896.

    Article  Google Scholar 

  • Zhou, X., Zhang, Y., Porter, A. L., et al. (2014b). A patent analysis method to trace technology evolutionary pathways. Scientometrics, 100(3), 705–721.

    Article  Google Scholar 

  • Zhu, D., Hassan, S. U., & Haddawy, P. (2013). Small-world phenomenon of keywords network based on complex network. Scientometrics, 97(2), 435–442.

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Science Foundation of China Funds (Grant No. 71774013 and 71673024), the National Science Foundation of China Yong Funds (Grant No. 71103015 and 71704139), the Basic Research Foundation of the Beijing Institute of Technology (Grant No. 20152142010), and the Special Items Fund of the Beijing Municipal Commission of Education. We also acknowledge Dr. Lu Yang for his contribution as an AI expert for this study.

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  1. School of Economics and Management, Xidian University, 266 Xinglong Section of Xifeng Road, Xi’an, 710126, Shaanxi, People’s Republic of China

    Xiao Zhou

  2. School of Management and Economics, Beijing Institute of Technology, No. 5 South Zhong Guan Cun Street, Haidian, Beijing, 100081, People’s Republic of China

    Lu Huang & Miaomiao Yu

  3. Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, Ultimo, NSW, 2007, Australia

    Yi Zhang

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Correspondence to Lu Huang.

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Zhou, X., Huang, L., Zhang, Y. et al. A hybrid approach to detecting technological recombination based on text mining and patent network analysis. Scientometrics 121, 699–737 (2019). https://doi.org/10.1007/s11192-019-03218-5

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