Identifying Technological Trends: A Patent Analysis Method for Technology Forecasting
Resumo
Patents are extensive and reliable sources of data on technological inventions, serving as the basis for patent analysis tasks. Among these tasks, technology forecasting is essential for research, development and decision-making in organizations. This paper proposes a decision-making support method capable of identifying technological trends. To achieve this, we explore the learning of network representations by applying link prediction algorithms to identify potential trends in the links between technologies. To demonstrate the effectiveness of the proposed method, we conducted experiments in the field of carbon technology. Our link prediction model reached a mean performance of 0.91, considering the ROC-AUC metric.
Palavras-chave:
Patent, Forecasting, Technology
Referências
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Altuntas, S., Dereli, T. and Kusiak, A. (2015) “Forecasting technology success based on patent data”. In Technological Forecasting and Social Change, n. 96, pages 202–214.
Amara, A., Taieb, M. A. H. and Aouicha, M. B. (2021) “Network representation learning systematic review: Ancestors and current development state”. In Machine Learning with Applications, n. 6, pages 100-130.
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Cho, Y. and Daim, T. (2013) “Technology forecasting methods”. In: Daim T, Oliver T, Kim J (eds) Research and Technology Management in the Electricity Industry: Methods, Tools and Case Studies. Springer London, London, pages 67-112.
Choi, D. and Song, B. (2018) ‘Exploring technological trends in logistics: Topic modeling-based patent analysis”. In Sustainability, vol 10, n. 8., pages 1-26.
Dadu, A., Kumar, A., Shakya, H., et al. (2019) “A study of link prediction using deep learning”. In Communications in Computer and Information Science, vol 955, pages 377–385.
Gupta, A., Matta, P. and Pant, B. (2021) “Graph neural network: Current state of art, challenges and applications”. In Materials Today: Proceedings, vol. 46, n. 20, pages 10927–10932.
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Kumar, A., Singh, S. S., Singh, K., et al. (2020) “Link prediction techniques, applications, and performance: A survey”. In Physica A: Statistical Mechanics and its Applications, vol. 553, p. 124289.
Lee, C., Kwon, O., Kim, M., et al. (2018) “Early identification of emerging technologies: A machine learning approach using multiple patent indicators”. In Technological Forecasting and Social Change, vol. 127, pages 291–303.
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Liben-Nowell, D., and Kleinberg, J. (2003) “The link prediction problem for social networks. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management”. Association for Computing Machinery, New York, NY, USA, CIKM’03, pages 556–559.
Lupu, M., Fujii, A., Oard, D. W., et al. (2017) “Patent-related tasks at NTCIR”. In: Current Challenges in Patent Information Retrieval. Edited by Lupu, M., Mayer, K., Kando, N., et al. Springer Berlin Heidelberg, Berlin, Heidelberg, pages 77–111.
Ma, J., Pan, Y., and Su, C. Y. (2022) “Organization-oriented technology opportunities analysis based on predicting patent networks: a case of Alzheimer's disease”. In: Scientometrics, vol. 127, n. 9, pages 5497–5517.
Malek, M., Chehreghani, M. H., Nazerfard, E., et al. (2021) “Shallow node representation learning using centrality indices”. In: 2021 IEEE International Conference on Big Data (Big Data), pages 5209–5214.
Ottonicar, S., Valentim, M., Mosconi, E. (2018) “A competitive intelligence model based on information literacy: Organizational competitiveness in the context of the 4th industrial revolution”. In Journal of Intelligence Studies in Business, vol. 8, pages 55–65.
Park I, Yoon, B (2018) “Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network”. In Journal of Informetrics. vol. 12, n. 4, pages 1199–1222.
Wang, W., Wu, L., Huang, Y., et al. (2019) ‘Link prediction based on deep convolutional neural network”. In Information, vol. 10, n. 5. pages 1-17.
Wu, W., Li, B., Luo, C., et al. (2021) “Hashing-accelerated graph neural networks for link prediction”. In: Proceedings of the Web Conference 2021. Association for Computing Machinery, New York, NY, USA, WWW’21, pages 2910–2920.
Yoon, B. and Lee, S. (2008) “Patent analysis for technology forecasting: Sector-specific applications”. In: 2008 IEEE International Engineering Management Conference, pages 1–5.
Zhang, L. and Liu, Z. (2020) “Research on technology prospect risk of high-tech projects based on patent analysis”. In Plos One, vol. 15, n. 10, pages 1–19.
Zhou, Y., Dong, F., Liu, Y., et al. (2020) “Forecasting emerging technologies using data augmentation and deep learning”. In Scientometrics, vol. 123, n. 1, pages 1–29.
Altuntas, S., Dereli, T. and Kusiak, A. (2015) “Forecasting technology success based on patent data”. In Technological Forecasting and Social Change, n. 96, pages 202–214.
Amara, A., Taieb, M. A. H. and Aouicha, M. B. (2021) “Network representation learning systematic review: Ancestors and current development state”. In Machine Learning with Applications, n. 6, pages 100-130.
Barabási, A. L. and Pósfai, M. (2016) “Network science”. Cambridge University Press, Cambridge, URL: [link]
Chen, Q., Wang, C. H. and Huang, S. Z. (2020) “Effects of organizational innovation and technological innovation capabilities on firm performance: evidence from firms in China’s pearl river delta”. In Asia Pacific Business Review vol. 26, n. 1, pages 72–96.
Cho, Y. and Daim, T. (2013) “Technology forecasting methods”. In: Daim T, Oliver T, Kim J (eds) Research and Technology Management in the Electricity Industry: Methods, Tools and Case Studies. Springer London, London, pages 67-112.
Choi, D. and Song, B. (2018) ‘Exploring technological trends in logistics: Topic modeling-based patent analysis”. In Sustainability, vol 10, n. 8., pages 1-26.
Dadu, A., Kumar, A., Shakya, H., et al. (2019) “A study of link prediction using deep learning”. In Communications in Computer and Information Science, vol 955, pages 377–385.
Gupta, A., Matta, P. and Pant, B. (2021) “Graph neural network: Current state of art, challenges and applications”. In Materials Today: Proceedings, vol. 46, n. 20, pages 10927–10932.
Haleem, A., Mannan, B., Luthra, S., et al. (2019) “Technology forecasting (TF) and technology assessment (TA) methodologies: a conceptual review”. In Benchmarking: An International Journal, vol. 26, n. 1, pages 48–72.
Jeong, B., Ko, N., Son, C., et al. (2021) “Trademark-based framework to uncover business diversification opportunities: Application of deep link prediction and competitive intelligence analysis”. In Computers in Industry, vol. 124, p. 103356.
Kim, G. and Bae, J. (2017) “A novel approach to forecast promising technology through patent analysis”. In Technological Forecasting and Social Change, vol. 117, pages 228–237.
Kim, K. H., Han, Y. J., Lee, S., et al. (2019) “Text mining for patent analysis to forecast emerging technologies in wireless power transfer”. In Sustainability, vol. 11, n. 22., p. 6240.
Krestel, R., Chikkamath, R., Hewel, C., et al. (2021) “A survey on deep learning for patent analysis”. In World Patent Information, vol. 65, p. 102035.
Kumar, A., Singh, S. S., Singh, K., et al. (2020) “Link prediction techniques, applications, and performance: A survey”. In Physica A: Statistical Mechanics and its Applications, vol. 553, p. 124289.
Lee, C., Kwon, O., Kim, M., et al. (2018) “Early identification of emerging technologies: A machine learning approach using multiple patent indicators”. In Technological Forecasting and Social Change, vol. 127, pages 291–303.
Lee, J., Ko, N., Yoon, J., et al. (2021) “An approach for discovering firm-specific technology opportunities: Application of link prediction to F-term networks”. In Technological Forecasting and Social Change, vol. 168, p. 120746.
Lenz, R. C. (1962) “Technological forecasting”. Report ASDTDR 62-414. Aeronautical Systems Divisions, Wright-Patterson Air Force Base, Ohio.
Li, S., Hu, J., Cui, Y., et al. (2018) “Deep patent: patent classification with convolutional neural networks and word embedding”. In Scientometrics, vol. 117, n. 2, pages 721–744.
Liben-Nowell, D., and Kleinberg, J. (2003) “The link prediction problem for social networks. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management”. Association for Computing Machinery, New York, NY, USA, CIKM’03, pages 556–559.
Lupu, M., Fujii, A., Oard, D. W., et al. (2017) “Patent-related tasks at NTCIR”. In: Current Challenges in Patent Information Retrieval. Edited by Lupu, M., Mayer, K., Kando, N., et al. Springer Berlin Heidelberg, Berlin, Heidelberg, pages 77–111.
Ma, J., Pan, Y., and Su, C. Y. (2022) “Organization-oriented technology opportunities analysis based on predicting patent networks: a case of Alzheimer's disease”. In: Scientometrics, vol. 127, n. 9, pages 5497–5517.
Malek, M., Chehreghani, M. H., Nazerfard, E., et al. (2021) “Shallow node representation learning using centrality indices”. In: 2021 IEEE International Conference on Big Data (Big Data), pages 5209–5214.
Ottonicar, S., Valentim, M., Mosconi, E. (2018) “A competitive intelligence model based on information literacy: Organizational competitiveness in the context of the 4th industrial revolution”. In Journal of Intelligence Studies in Business, vol. 8, pages 55–65.
Park I, Yoon, B (2018) “Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network”. In Journal of Informetrics. vol. 12, n. 4, pages 1199–1222.
Wang, W., Wu, L., Huang, Y., et al. (2019) ‘Link prediction based on deep convolutional neural network”. In Information, vol. 10, n. 5. pages 1-17.
Wu, W., Li, B., Luo, C., et al. (2021) “Hashing-accelerated graph neural networks for link prediction”. In: Proceedings of the Web Conference 2021. Association for Computing Machinery, New York, NY, USA, WWW’21, pages 2910–2920.
Yoon, B. and Lee, S. (2008) “Patent analysis for technology forecasting: Sector-specific applications”. In: 2008 IEEE International Engineering Management Conference, pages 1–5.
Zhang, L. and Liu, Z. (2020) “Research on technology prospect risk of high-tech projects based on patent analysis”. In Plos One, vol. 15, n. 10, pages 1–19.
Zhou, Y., Dong, F., Liu, Y., et al. (2020) “Forecasting emerging technologies using data augmentation and deep learning”. In Scientometrics, vol. 123, n. 1, pages 1–29.
Publicado
14/10/2024
Como Citar
KOCHAN, Patrick D.; BARCELOS, Bartholomeo O.; GONÇALVES, Alexandre L..
Identifying Technological Trends: A Patent Analysis Method for Technology Forecasting. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 39. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 327-340.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2024.240268.
