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Technology identification from patent texts: A novel named entity recognition method

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  • Puccetti, Giovanni
  • Giordano, Vito
  • Spada, Irene
  • Chiarello, Filippo
  • Fantoni, Gualtiero

Abstract

Identifying technologies is a key element for mapping a domain and its evolution. It allows managers and decision makers to anticipate trends for an accurate forecast and effective foresight. Researchers and practitioners are taking advantage of the rapid growth of the publicly accessible sources to map technological domains. Among these sources, patents are the widest technical open access database used in the literature and in practice.

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  • Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
  • Handle: RePEc:eee:tefoso:v:186:y:2023:i:pb:s0040162522006813
    DOI: 10.1016/j.techfore.2022.122160
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    1. Vicente-Gomila, J.M. & Artacho-Ramírez, M.A. & Ting, Ma & Porter, A.L., 2021. "Combining tech mining and semantic TRIZ for technology assessment: Dye-sensitized solar cell as a case," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    2. Robinson, Douglas K.R. & Huang, Lu & Guo, Ying & Porter, Alan L., 2013. "Forecasting Innovation Pathways (FIP) for new and emerging science and technologies," Technological Forecasting and Social Change, Elsevier, vol. 80(2), pages 267-285.
    3. Kyebambe, Moses Ntanda & Cheng, Ge & Huang, Yunqing & He, Chunhui & Zhang, Zhenyu, 2017. "Forecasting emerging technologies: A supervised learning approach through patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 236-244.
    4. Suominen, Arho & Toivanen, Hannes & Seppänen, Marko, 2017. "Firms' knowledge profiles: Mapping patent data with unsupervised learning," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 131-142.
    5. Jose M. Vicente-Gomila & Anna Palli & Begoña Calle & Miguel A. Artacho & Sara Jimenez, 2017. "Discovering shifts in competitive strategies in probiotics, accelerated with TechMining," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1907-1923, June.
    6. Karvonen, Matti & Kässi, Tuomo, 2013. "Patent citations as a tool for analysing the early stages of convergence," Technological Forecasting and Social Change, Elsevier, vol. 80(6), pages 1094-1107.
    7. Magee, C.L. & Basnet, S. & Funk, J.L. & Benson, C.L., 2016. "Quantitative empirical trends in technical performance," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 237-246.
    8. de Rassenfosse, Gaétan & Dernis, Hélène & Guellec, Dominique & Picci, Lucio & van Pottelsberghe de la Potterie, Bruno, 2013. "The worldwide count of priority patents: A new indicator of inventive activity," Research Policy, Elsevier, vol. 42(3), pages 720-737.
    9. Arts, Sam & Hou, Jianan & Gomez, Juan Carlos, 2021. "Natural language processing to identify the creation and impact of new technologies in patent text: Code, data, and new measures," Research Policy, Elsevier, vol. 50(2).
    10. Joung, Junegak & Kim, Kwangsoo, 2017. "Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 281-292.
    11. Li, Xin & Xie, Qianqian & Jiang, Jiaojiao & Zhou, Yuan & Huang, Lucheng, 2019. "Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 687-705.
    12. Gustafsson, Robin & Kuusi, Osmo & Meyer, Martin, 2015. "Examining open-endedness of expectations in emerging technological fields: The case of cellulosic ethanol," Technological Forecasting and Social Change, Elsevier, vol. 91(C), pages 179-193.
    13. Porter, Alan L. & Garner, Jon & Carley, Stephen F. & Newman, Nils C., 2019. "Emergence scoring to identify frontier R&D topics and key players," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 628-643.
    14. Luciano Kay & Nils Newman & Jan Youtie & Alan L. Porter & Ismael Rafols, 2014. "Patent overlay mapping: Visualizing technological distance," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(12), pages 2432-2443, December.
    15. Eun Han & So Sohn, 2015. "Patent valuation based on text mining and survival analysis," The Journal of Technology Transfer, Springer, vol. 40(5), pages 821-839, October.
    16. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    17. Janghyeok Yoon & Kwangsoo Kim, 2011. "Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 213-228, July.
    18. Tom Magerman & Bart Looy & Xiaoyan Song, 2010. "Exploring the feasibility and accuracy of Latent Semantic Analysis based text mining techniques to detect similarity between patent documents and scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 289-306, February.
    19. Xu, Jianguo & Guo, Lixiang & Jiang, Jiang & Ge, Bingfeng & Li, Mengjun, 2019. "A deep learning methodology for automatic extraction and discovery of technical intelligence," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 339-351.
    20. Choi, Seokkyu & Lee, Hyeonju & Park, Eunjeong & Choi, Sungchul, 2022. "Deep learning for patent landscaping using transformer and graph embedding," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    21. Douglas K. R. Robinson & Lu Huang & Yan Guo & Alan L. Porter, 2013. "Forecasting Innovation Pathways (FIP) for new and emerging science and technologies," Post-Print hal-01070417, HAL.
    22. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    23. Samira Ranaei & Arho Suominen & Alan Porter & Stephen Carley, 2020. "Evaluating technological emergence using text analytics: two case technologies and three approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 215-247, January.
    24. Breitzman, Anthony & Thomas, Patrick, 2015. "The Emerging Clusters Model: A tool for identifying emerging technologies across multiple patent systems," Research Policy, Elsevier, vol. 44(1), pages 195-205.
    25. Chang, Shu-Hao & Fan, Chin-Yuan, 2016. "Identification of the technology life cycle of telematics: A patent-based analytical perspective," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 1-10.
    26. Sercan Ozcan & Nazrul Islam, 2017. "Patent information retrieval: approaching a method and analysing nanotechnology patent collaborations," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 941-970, May.
    27. Morteza Maghrebi & Ali Abbasi & Saeid Amiri & Reza Monsefi & Ahad Harati, 2011. "A collective and abridged lexical query for delineation of nanotechnology publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(1), pages 15-25, January.
    28. Yuan Zhou & Fang Dong & Yufei Liu & Zhaofu Li & JunFei Du & Li Zhang, 2020. "Forecasting emerging technologies using data augmentation and deep learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 1-29, April.
    29. Hofmann, Peter & Keller, Robert & Urbach, Nils, 2019. "Inter-technology relationship networks: Arranging technologies through text mining," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 202-213.
    30. Frey, Carl Benedikt & Osborne, Michael A., 2017. "The future of employment: How susceptible are jobs to computerisation?," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 254-280.
    31. Shaobo Li & Jie Hu & Yuxin Cui & Jianjun Hu, 2018. "DeepPatent: patent classification with convolutional neural networks and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 721-744, November.
    32. D.K. Robinson & Lu Huang & Ying Guo & Alan L. Porter, 2013. "Forecasting Innovation Pathways (FIP) for new and emerging science and technologies," Post-Print hal-01071140, HAL.
    33. Niemann, Helen & Moehrle, Martin G. & Frischkorn, Jonas, 2017. "Use of a new patent text-mining and visualization method for identifying patenting patterns over time: Concept, method and test application," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 210-220.
    34. Song, Chie Hoon & Elvers, David & Leker, Jens, 2017. "Anticipation of converging technology areas — A refined approach for the identification of attractive fields of innovation," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 98-115.
    35. Yu, Xiang & Zhang, Ben, 2019. "Obtaining advantages from technology revolution: A patent roadmap for competition analysis and strategy planning," Technological Forecasting and Social Change, Elsevier, vol. 145(C), pages 273-283.
    36. Jeffrey Kuhn & Kenneth Younge & Alan Marco, 2020. "Patent citations reexamined," RAND Journal of Economics, RAND Corporation, vol. 51(1), pages 109-132, March.
    37. Sternitzke, Christian, 2010. "Knowledge sources, patent protection, and commercialization of pharmaceutical innovations," Research Policy, Elsevier, vol. 39(6), pages 810-821, July.
    38. Ernst, Holger, 2003. "Patent information for strategic technology management," World Patent Information, Elsevier, vol. 25(3), pages 233-242, September.
    39. Hain, Daniel S. & Jurowetzki, Roman & Buchmann, Tobias & Wolf, Patrick, 2022. "A text-embedding-based approach to measuring patent-to-patent technological similarity," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    40. Righi, Cesare & Simcoe, Timothy, 2019. "Patent examiner specialization," Research Policy, Elsevier, vol. 48(1), pages 137-148.
    41. Song, Bomi & Suh, Yongyoon, 2019. "Identifying convergence fields and technologies for industrial safety: LDA-based network analysis," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 115-126.
    42. Lee, Changyong & Jeon, Daeseong & Ahn, Joon Mo & Kwon, Ohjin, 2020. "Navigating a product landscape for technology opportunity analysis: A word2vec approach using an integrated patent-product database," Technovation, Elsevier, vol. 96.
    43. D. Thorleuchter & D. Van Den Poel & A. Prinzie & -, 2010. "A compared R&D-based and patent-based cross impact analysis for identifying relationships between technologies," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/632, Ghent University, Faculty of Economics and Business Administration.
    44. Stephen F. Carley & Nils C. Newman & Alan L. Porter & Jon G. Garner, 2018. "An indicator of technical emergence," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 35-49, April.
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