Screening ideas in the early stages of technology development: A word2vec and convolutional neural network approach

Technovation - Tập 112 - Trang 102407 - 2022
Suckwon Hong1, Juram Kim2, Han-Gyun Woo1, Young-Choon Kim1, Changyong Lee3
1School of Business Administration, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulsan, 44919, Republic of Korea
2Center for R&D Investment and Strategy Research, Korea Institute of Science and Technology Information, 66 Hoegi-ro, Dongdaemun-gu, Seoul, 02456, Republic of Korea
3Graduate School of Management of Technology, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul, 04107, Republic of Korea

Tài liệu tham khảo

Arts, 2018, Text matching to measure patent similarity, Strat. Manag. J., 39, 62, 10.1002/smj.2699 Arts, 2021, Natural language processing to identify the creation and impact of new technologies in patent text: code, data, and new measures, Res. Policy, 50, 104144, 10.1016/j.respol.2020.104144 Arts, 2015, Technology familiarity, recombinant novelty, and breakthrough invention, Ind. Corp. Change, 24, 1215, 10.1093/icc/dtu029 Barirani, 2015, Distant recombination and the creation of basic inventions: an analysis of the diffusion of public and private sector nanotechnology patents in Canada, Technovation, 36–37, 39, 10.1016/j.technovation.2014.10.002 Bojanowski, 2017, Enriching word vectors with subword information, Trans. Assoc. Comput. Linguist., 5, 135, 10.1162/tacl_a_00051 Briggs, 2015, Co-owner relationships conducive to high quality joint patents, Res. Policy, 44, 1566, 10.1016/j.respol.2015.05.011 Calantone, 1999, Using the analytic hierarchy process in new product screening, J. Prod. Innovat. Manag., 16, 65, 10.1111/1540-5885.1610065 Devlin, 2018 DiMasi, 2003, The price of innovation: new estimates of drug development costs, J. Health Econ., 22, 151, 10.1016/S0167-6296(02)00126-1 Dziallas, 2020, How to evaluate innovative ideas and concepts at the front-end?: a front-end perspective of the automotive innovation process, J. Bus. Res., 110, 502, 10.1016/j.jbusres.2018.05.008 Fleming, 2007, Breakthroughs and the "long tail" of innovation, MIT Sloan Manag. Rev., 49, 69 Goldberg, 2014 Hall, 2005, Market value and patent citations, Rand J. Econ., 36, 16 Harhoff, 1999, Citation frequency and the value of patented inventions, Rev. Econ. Stat., 81, 511, 10.1162/003465399558265 Huang, 2020, A model for supporting the ideas screening during front end of the innovation process based on combination of methods of EcaTRIZ, AHP, and SWOT, Concur. Eng., 28, 89, 10.1177/1063293X20911165 Jang, 2017, Hawkes process-based technology impact analysis, J. Informetr., 11, 511, 10.1016/j.joi.2017.03.007 Johnson, 2015, Semi-supervised convolutional neural networks for text categorization via region embedding, Adv. Neural Inf. Process. Syst., 28, 919 Jolly, 1997 Kim, 2021, Valuation of university-originated technologies: a predictive analytics approach, IEEE T. Eng. Manag., 68, 1813, 10.1109/TEM.2019.2938182 Kim, 2019, Anticipating technological convergence: link prediction using Wikipedia hyperlinks, Technovation, 79, 25, 10.1016/j.technovation.2018.06.008 Kiros, 2015 Kudrowitz, 2013, Assessing the quality of ideas from prolific, early-stage product ideation, J. Eng. Des., 24, 120, 10.1080/09544828.2012.676633 Lavecchia, 2019, Deep learning in drug discovery: opportunities, challenges, and future prospects, Drug Discov. Today, 24, 2017, 10.1016/j.drudis.2019.07.006 LeCun, 1998, Gradient-based learning applied to document recognition, P. IEEE, 86, 2278, 10.1109/5.726791 Lee, 2021, A review of data analytics in technological forecasting, Technol. Forecast. Soc., 166, 120646, 10.1016/j.techfore.2021.120646 Lee, 2012, A stochastic patent citation analysis approach to assessing future technological impacts, Technol. Forecast. Soc., 79, 16, 10.1016/j.techfore.2011.06.009 Lee, 2020, Navigating a product landscape for technology opportunity analysis: a word2vec approach using an integrated patent-product database, Technovation, 96, 102140, 10.1016/j.technovation.2020.102140 Lee, 2011, Monitoring trends of technological changes based on the dynamic patent lattice: a modified formal concept analysis approach, Technol. Forecast. Soc., 78, 690, 10.1016/j.techfore.2010.11.010 Lee, 2015, Novelty-focused patent mapping for technology opportunity analysis, Technol. Forecast. Soc., 90, 355, 10.1016/j.techfore.2014.05.010 Lee, 2018, Early identification of emerging technologies: a machine learning approach using multiple patent indicators, Technol. Forecast. Soc., 127, 291, 10.1016/j.techfore.2017.10.002 Lee, 2009, An approach to discovering new technology opportunities: keyword-based patent map approach, Technovation, 29, 481, 10.1016/j.technovation.2008.10.006 Mikolov, 2013 Murphy, 1997, The front end of new product development: a Canadian survey, R D Manag., 27, 5, 10.1111/1467-9310.00038 Noh, 2015, Keyword selection and preprocessing strategy for applying text mining to patent analysis, Expert Syst. Appl., 42, 4348, 10.1016/j.eswa.2015.01.050 Peters, 2018 Porter, 1980, An algorithm for suffix stripping, Program-electron. Lib., 14, 130 Robbes, 2019, Leveraging small software engineering data sets with pre-trained neural networks, IEEE/ACM Int. Conf. Softw. Eng., 29 Rong, 2014 Salerno, 2015, Innovation processes: which process for which project?, Technovation, 35, 59, 10.1016/j.technovation.2014.07.012 Shin, 2013, Robust future‐oriented technology portfolios: black–Litterman approach, R D Manag., 43, 409, 10.1111/radm.12022 Trajtenberg, 1990, A penny for your quotes: patent citations and the value of innovations, Rand J. Econ., 21, 172, 10.2307/2555502 Woo, 2019, Screening early stage ideas in technology development processes: a text mining and k-nearest neighbours approach using patent information, Technol. Anal. Strateg., 31, 532, 10.1080/09537325.2018.1523386 Young, 2018, Recent trends in deep learning based natural language processing, IEEE Comput. Intell. M., 13, 55, 10.1109/MCI.2018.2840738 Yun, 2020, Automated classification of patents: a topic modeling approach, Comput. Ind. Eng., 147, 106636, 10.1016/j.cie.2020.106636 Zhang, 2018, Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU, J. Comput. Sci., 28, 1, 10.1016/j.jocs.2018.07.003