Abstract
The aim of this study is to propose a clustering-based approach based on patent information for the evaluation of candidate emerging technologies. The proposed approach uses patent analysis and clustering approaches in data mining. Patent analysis is a widely used method for the evaluation of candidate emerging technologies in the literature. The clustering algorithms used in this study are self-organizing maps, expected maximization and density-based clustering. A real-life application on dental implant technology is presented to show how the proposed approach works in practice. The contributions of this study are twofold. This study contributes to the literature by taking into account claims, forward citations, backward citations, technology cycle times, and technology scores for the evaluation of candidate emerging technologies. Second, the evaluation of dental implant technology with respect to claims, forward citations, backward citations, technology cycle times, and technology scores has not been conducted so far. The results obtained from the application shows that dental implant technology is an candidate emerging technology and the proposed approach can be easily conducted in real life case studies.
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References
Abraham, B. P., & Moitra, S. D. (2001). Innovation assessment through patent analysis. Technovation,21(4), 245–252.
Albiero, A. M., Benato, R., Momic, S., & Degidi, M. (2017). Implementation of computer-guided implant planning using digital scanning technology for restorations supported by conical abutments: A dental technique. The Journal of Prosthetic Dentistry,119(5), 720–726.
Altuntas, S., Dereli, T., & Kusiak, A. (2015a). Analysis of patent documents with weighted association rules. Technological Forecasting and Social Change,92, 249–262.
Altuntas, S., Dereli, T., & Kusiak, A. (2015b). Forecasting technology success based on patent data. Technological Forecasting and Social Change,96, 202–214.
Altuntas, F., & Karaman Akgul, A. (2019). Technologies evaluation with data mining: An application on radio frequency identification-(RFID) technologies. Verimlilik Dergisi,4, 65–86.
Altuntas, F., & Yilmaz, M. K. (2017). Using patent analysis to construct technology networks. Journal of Entrepreneurship and Innovation Management,6(2), 97–129.
An, H. J., & Ahn, S. J. (2016). Emerging technologies—beyond the chasm: Assessing technological forecasting and its implication for innovation management in Korea. Technological Forecasting and Social Change,102, 132–142.
Angelopoulos, C., & Aghaloo, T. (2011). Cone Beam Computed Tomography for the Implant Patient. Dental clinics of North America,55(1), 141–158.
Babík, O., Czán, A., Holubjak, J., Kameník, R., & Pilc, J. (2017). Identification of surface characteristics created by miniature machining of dental implants made of titanium based materials. Procedia Engineering,192, 1016–1021.
Bacchiocchi, E., & Montobbio, F. (2009). Symposium: innovation and intellectual property values: Knowledge diffusion from university and public research: a comparison between US, Japan and Europe using patent citations. Journal of Technology Transfer,34(2), 169–181.
Bengisu, M., & Nekhili, R. (2006). Forecasting emerging technologies with the aid of science and technology databases. Technological Forecasting and Social Change,73(7), 835–844.
Bhattacharya, S. (2004). Mapping inventive activity and technological change through patent analysis: A case study of India and China. Scientometrics,61(3), 361–381.
Block, M. S. (2018). Dental implants: The last 100 years. Journal of Oral and Maxillofacial Surgery,76(1), 11–26.
Boh, W. F., Evaristo, R., & Ouderkirk, A. (2014). Balancing breadth and depth of expertise for innovation: A 3M story. Research Policy,43(2), 349–366.
Breitzman, A., & Thomas, P. (2015). The emerging clusters model: A tool for identifying emerging technologies across multiple patent systems. Research Policy,44(1), 195–205.
Bruzzone, L., & Prieto, D. F. (2002). an adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing,11(4), 452–466.
Cao, G., Luo, P., Wang, L., & Yang, X. (2016). Key technologies for sustainable design based on patent knowledge mining. Procedia Cirp,39, 97–102.
Carminati, M., Caron, R., Maggi, F., Epifani, I., Zanero, S. (2014, June). BankSealer: An online banking fraud analysis and decision support system. In IFIP international information security conference (pp. 380–394). Springer, Berlin.
Carpenter, M., Narin, F., & Woolf, P. (1981). Citation related to technologically importantpatents. World Patent Information,4, 160–163.
Chang, S. B. (2012). Using patent analysis to establish technological position: Two different strategic approaches. Technological Forecasting and Social Change,79(1), 3–15.
Chang, S. H., & Fan, C. Y. (2016). Identification of the technology life cycle of telematics: A patent-based analytical perspective. Technological Forecasting and Social Change,105, 1–10.
Chen, J., Zhang, Z., Chen, X., Zhang, C., Zhang, G., & Xu, Z. (2014). Design and manufacture of customized dental implants by using reverse engineering and selective laser melting technology. The Journal of prosthetic dentistry,112(5), 1088–1095.
Chen, X., Peng, Z., & Zeng, C. (2012, October). A co-training based method for Chinese patent semantic annotation. In Proceedings of the 21st ACM international conference on Information and knowledge management (pp. 2379–2382).
Choi, J., & Hwang, Y. S. (2014). Patent keyword network analysis for improving technology development efficiency. Technological Forecasting and Social Change,83, 170–182.
Choi, S., & Jun, S. (2014). Vacant technology forecasting using new Bayesian patent clustering. Technology Analysis & Strategic Management,26(3), 241–251.
Clancy, M. S. (2018). Inventing by combining pre-existing technologies: Patent evidence on learning and fishing out. Research Policy,47(1), 252–265.
Cordeiro, J. M., Beline, T., Ribeiro, A. L. R., Rangel, E. C., da Cruz, N. C., Landers, R., et al. (2017). Development of binary and ternary titanium alloys for dental implants. Dental Materials,33(11), 1244–1257.
Corradini, C., & De Propris, L. (2017). Beyond local search: Bridging platforms and inter-sectoral technological integration. Research Policy,46(1), 196–206.
Dalva, D., Guz, U., & Gurkan, H. (2018). Effective semi-supervised learning strategies for automatic sentence segmentation. Pattern Recognition Letters,105, 76–86.
De Marco, A., Scellato, G., Ughetto, E., & Caviggioli, F. (2017). Global markets for technology: Evidence from patent transactions. Research Policy,46(9), 1644–1654.
Debackere, K., Clarysse, B., & Rappa, M. A. (1996). Dismantling the ivory tower: The influence of networks on innovative output in emerging technologies. Technological Forecasting and Social Change,53(2), 139–154.
Demircioglu, P. (2014). Estimation of surface topography for dental implants using advanced metrological technology and digital image processing techniques. Measurement,48, 43–53.
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the roYal Statistical Society: Series B,39, 1–38.
Ester, M., Kriegel, H. P., Sander, J., Xu, X. (1996). A density-based algorithm for discovering cluster in large spatial databases with noise. In 2nd international conference on knowledge discovery and data mining (pp. 226–231).
Fischer, T., & Leidnger, J. (2014). Testing patent value indicators on directly observed patent value—an empirical analysis of Ocean Tomo patent auctions. Research Policy,43(3), 519–529.
Fischer, T., & Ringler, P. (2014). What patents are used as collateral? —An empirical analysis of patent reassignment data. Journal of Business Venturing,29(5), 633–650.
Gao, Q., Huang, Y., Gao, X., Shen, W., & Zhang, H. (2015). A novel semi-supervised learning for face recognition. Neurocomputing,152, 69–76.
Gao, L., Porter, A. L., Wang, J., Fang, S., Zhang, X., Ma, T., et al. (2013). Technology life cycle analysis method based on patent documents. Technological Forecasting and Social Change,80(3), 398–407.
Grimaldi, M., Cricelli, L., & Rogo, F. (2018). Valuating and analyzing the patent portfolio: the patent portfolio value index. European Journal of Innovation Management.
Guthikonda, S. M. (2005). Kohonen self-organizing maps. Wittenberg: Wittenberg University.
Harhoff, D., Narin, F., Scherer, F. M., & Vopel, K. (1999). Citation frequency and the value of patented inventions. Review of Economics and Statistics,81(3), 511–515.
Haupt, R., Kloyer, M., & Lange, M. (2007). Patent indicators for the technology life cycle development. Research Policy,36(3), 387–398.
Hsu, C. W., Chang, P. L., Hsiung, C. M., & Wu, C. C. (2015). Charting the evolution of biohydrogen production technology through a patent analysis. Biomass and Bioenergy,76, 1–10.
Hussain, A., & Cambria, E. (2018). Semi-supervised learning for big social data analysis. Neurocomputing,275, 1662–1673.
Hájek, P., & Olej, V. (2011). Credit rating modelling by kernel-based approaches with supervised and semi-supervised learning. Neural Computing and Applications,20(6), 761–773.
Jang, H. J., Woo, H. G., & Lee, C. (2017). Hawkes process-based technology impact analysis. Journal of Informetrics,11(2), 511–529.
Jeong, C., & Kim, K. (2014). Creating patents on the new technology using analogy-based patent mining. Expert Systems with Applications,41(8), 3605–3614.
Johnson, W. H., & Liu, Q. (2011). Patenting and the role of technology markets in regional innovation in China: An empirical analysis. The Journal of High Technology Management Research,22(1), 14–25.
Kim, G., & Bae, J. (2017). A novel approach to forecast promising technology through patent analysis. Technological Forecasting and Social Change,117, 228–237.
Kim, A., & Cho, S. B. (2019). An ensemble semi-supervised learning method for predicting defaults in social lending. Engineering Applications of Artificial Intelligence,81, 193–199.
Kohonen, T. (1982). Analysis of a simple self-organizing process. Biological Cybernetics,44(2), 135–140.
Kyebambe, M. N., Cheng, G., Huang, Y., He, C., & Zhang, Z. (2017). Forecasting emerging technologies: A supervised learning approach through patent analysis. Technological Forecasting and Social Change,125, 236–244.
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.
Lee, W. S., Han, E. J., & Sohn, S. Y. (2015). Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents. Technological Forecasting and Social Change,100, 317–329.
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.
Lerner, J. (1994). The importance of patent scope: an empirical analysis. The RAND Journal of Economics,25(2), 319–333.
Lesmes, D., & Laster, Z. (2011). Innovations in dental implant design for current therapy. Oral and Maxillofacial Surgery Clinics,23(2), 193–200.
Liu, R., Verbič, G., & Ma, J. (2019). A new dynamic security assessment framework based on semi-supervised learning and data editing. Electric Power Systems Research,172, 221–229.
Liu, C. Y., & Wang, J. C. (2010). Forecasting the development of the biped robot walking technique in Japan through S-curve model analysis. Scientometrics,82(1), 21–36.
Ma, T., & Zhang, A. (2018). Affinity network fusion and semi-supervised learning for cancer patient clustering. Methods,145, 16–24.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the fifth Berkeley symposium on mathematical statistics and probability,1(14), 281–297.
Madani, F., & Weber, C. (2016). The evolution of patent mining: Applying bibliometrics analysis and keyword network analysis. World Patent Information,46, 32–48.
Mendonça, G., Mendonça, D. B., Aragao, F. J., & Cooper, L. F. (2008). Advancing dental implant surface technology–from micron-to nanotopography. Biomaterials,29(28), 3822–3835.
Mueller, S. C., Sandner, P. G., & Welpe, I. M. (2015). Monitoring innovation in electrochemical energy storage technologies: A patent-based approach. Applied Energy,137, 537–544.
No, H. J., An, Y., & Park, Y. (2015). A structured approach to explore knowledge flows through technology based business methods by integrating patent citation analysis and text mining. Technological Forecasting and Social Change,97, 181–192.
Novelli, E. (2015). An examination of the antecedents and implications of patent scope. Research Policy,44(2), 493–507.
Nuray, R., & Can, F. (2006). Automatic ranking of information retrieval systems using data fusion. Information Processing & Management,42(3), 595–614.
Ozdemir, M., Akbulak, C., & Yıldırım, H. H. (2010). Analysis of the spatial changes of forest areas in the Gelibolu peninsula historical national park through image difference method and expectation–maximization algorithm. Fırat Univ. J. Soc. Sci.,20(1), 115–138.
Park, S., Kim, J., Lee, H., Jang, D., & Jun, S. (2016). Methodology of technological evolution for three-dimensional printing. Industrial Management & Data Systems,116(1), 122–146.
Park, J., Lee, H., & Park, Y. (2009). Disembodied knowledge flows among industrial clusters: A patent analysis of the Korean manufacturing sector. Technology in Society,31(1), 73–84.
Rajeswar, A. R. (1996). Indian patent statistics—an analysis. Scientometrics,36(1), 109–130.
Ramakrishnaiah, R., Mohammad, A., Divakar, D. D., Kotha, S. B., Celur, S. L., Hashem, M. I., et al. (2017). Preliminary fabrication and characterization of electron beam melted Ti–6Al–4V customized dental implant. Saudi Journal of Biological Sciences,24(4), 787–796.
Rao, Y. V. D., Parimi, A. M., Rahul, D. S. P., Patel, D., & Mythreya, Y. N. (2017). Robotics in dental implantation. Materials Today: Proceedings,4(8), 9327–9332.
Revathi, A., Borrás, A. D., Muñoz, A. I., Richard, C., & Manivasagam, G. (2017). Degradation mechanisms and future challenges of titanium and its alloys for dental implant applications in oral environment. Materials Science and Engineering: C,76, 1354–1368.
Shen, Y. C., Chang, S. H., Lin, G. T., & Yu, H. C. (2010). A hybrid selection model for emerging technology. Technological Forecasting and Social Change,77(1), 151–166.
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.
Starczewski, A., & Krzyżak, A. (2015). Performance evaluation of the silhouette index. International Conference on Artificial Intelligence and Soft Computing (pp. 49–58). Cham: Springer.
Sun, T. M., Lan, T. H., Pan, C. Y., & Lee, H. E. (2018). Dental implant navigation system guide the surgery future. The Kaohsiung journal of medical sciences,34(1), 56–64.
Takahashi, D., Suzuki, H., & Komori, T. (2018). A clinical study of 103 dental implants in oral cancer patients after jaw resection. Journal of Oral and Maxillofacial Surgery, Medicine, and Pathology,30(3), 206–211.
Tong, X., & Frame, J. D. (1994). Measuring national technological performance with patent claims data. Research Policy,23, 133–141.
Trappey, C. V., Trappey, A. J., Peng, H. Y., Lin, L. D., & Wang, T. M. (2014). A knowledge centric methodology for dental implant technology assessment using ontology based patent analysis and clinical meta-analysis. Advanced Engineering Informatics,28(2), 153–165.
Trappey, C. V., Wang, T. M., Hoang, S., & Trappey, A. J. (2013). Constructing a dental implant ontology for domain specific clustering and life span analysis. Advanced Engineering Informatics,27(3), 346–357.
Van der Valk, T., Chappin, M. M., & Gijsbers, G. W. (2011). Evaluating innovation networks in emerging technologies. Technological Forecasting and Social Change,78(1), 25–39.
Verhaegen, P. A., D’hondt, J., Vertommen, J., Dewulf, S., & Duflou, J. R. (2011). Searching for similar products through patent analysis. Procedia Engineering,9, 431–441.
Vesselinov, V. V., Alexandrov, B. S., & O’Malley, D. (2018). Contaminant source identification using semi-supervised machine learning. Journal of Contaminant Hydrology,212, 134–142.
Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining practical machine learning tools and techniques third edition. Burlington: Morgan Kaufmann.
Xuexi, Z., Zhiwen, H., Heyuan, Z., Jinsong, C., & Zhao, L. (2017). Research on the Construction of Search Database Patent Platform for Intelligent Industrial Robots. Procedia Computer Science,107(C), 218–224.
Yoon, J., & Kim, K. (2012). An analysis of property-function based patent networks for strategic R&D planning in fast-moving industries: The case of silicon-based thin film solar cells. Expert System Application.,39(9), 7709–7717.
Yoshikane, F. (2014). Comparative analysis of patent citations of different fields. In consideration of the data size dependency of statistical measures. Procedia-Social and Behavioral Sciences,147, 153–159.
Yu, D., Chen, N., Jiang, F., Fu, B., & Qin, A. (2017). Constrained NMF-based semi-supervised learning for social media spammer detection. Knowledge-Based Systems,125, 64–73.
Zhu, X., & Goldberg, A. B. (2009). Introduction to semi-supervised learning. Synthesis lectures on artificial intelligence and machine learning,3(1), 1–130.
Zohrabian, V. M., Sonick, M., Hwang, D., & Abrahams, J. J. (2015). Dental implants. Seminars in Ultrasound,36(5), 415–426.
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Altuntas, S., Erdogan, Z. & Dereli, T. A clustering-based approach for the evaluation of candidate emerging technologies. Scientometrics 124, 1157–1177 (2020). https://doi.org/10.1007/s11192-020-03535-0
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DOI: https://doi.org/10.1007/s11192-020-03535-0