Multimodal Deep Learning for Patent Classification

Juhyun Lee; Junseok Lee; Jiho Kang; Youngho Kim; Dongsik Jang; Sangsung Park

doi:10.1007/978-981-16-2102-4_26

Multimodal Deep Learning for Patent Classification

Juhyun Lee, Junseok Lee, Jiho Kang, Youngho Kim, Dongsik Jang, Sangsung Park

School of Industrial Management Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

The global technology market is changing rapidly. Organizations need strategies to increase their competitiveness. Technology transfer is an effective tool for them to plan for intellectual property research and development. Prediction of technology transfer leads to efficient results. For this, we propose multimodal learning. The model utilizes the quantitative information and text of the patent. Unlike the previous studies, it uses the architecture of deep learning. The model was tested for practical applicability through patent data. As a result, it showed higher performance than other models.

Original language	English
Title of host publication	Proceedings of 6th International Congress on Information and Communication Technology, ICICT 2021
Editors	Xin-She Yang, Simon Sherratt, Nilanjan Dey, Amit Joshi
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	281-289
Number of pages	9
ISBN (Print)	9789811621017
DOIs	https://doi.org/10.1007/978-981-16-2102-4_26
Publication status	Published - 2022
Event	6th International Congress on Information and Communication Technology, ICICT 2021 - Virtual, Online Duration: 2021 Feb 25 → 2021 Feb 26

Publication series

Name	Lecture Notes in Networks and Systems
Volume	217
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	6th International Congress on Information and Communication Technology, ICICT 2021
City	Virtual, Online
Period	21/2/25 → 21/2/26

Keywords

Multimodal learning
Patent classification
Technology transfer

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-16-2102-4_26

Cite this

Lee, J., Lee, J., Kang, J., Kim, Y., Jang, D., & Park, S. (2022). Multimodal Deep Learning for Patent Classification. In X-S. Yang, S. Sherratt, N. Dey, & A. Joshi (Eds.), Proceedings of 6th International Congress on Information and Communication Technology, ICICT 2021 (pp. 281-289). (Lecture Notes in Networks and Systems; Vol. 217). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-2102-4_26

Multimodal Deep Learning for Patent Classification. / Lee, Juhyun; Lee, Junseok; Kang, Jiho et al.

Proceedings of 6th International Congress on Information and Communication Technology, ICICT 2021. ed. / Xin-She Yang; Simon Sherratt; Nilanjan Dey; Amit Joshi. Springer Science and Business Media Deutschland GmbH, 2022. p. 281-289 (Lecture Notes in Networks and Systems; Vol. 217).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, J, Lee, J, Kang, J, Kim, Y, Jang, D & Park, S 2022, Multimodal Deep Learning for Patent Classification. in X-S Yang, S Sherratt, N Dey & A Joshi (eds), Proceedings of 6th International Congress on Information and Communication Technology, ICICT 2021. Lecture Notes in Networks and Systems, vol. 217, Springer Science and Business Media Deutschland GmbH, pp. 281-289, 6th International Congress on Information and Communication Technology, ICICT 2021, Virtual, Online, 21/2/25. https://doi.org/10.1007/978-981-16-2102-4_26

Lee J, Lee J, Kang J, Kim Y, Jang D, Park S. Multimodal Deep Learning for Patent Classification. In Yang X-S, Sherratt S, Dey N, Joshi A, editors, Proceedings of 6th International Congress on Information and Communication Technology, ICICT 2021. Springer Science and Business Media Deutschland GmbH. 2022. p. 281-289. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-16-2102-4_26

Lee, Juhyun ; Lee, Junseok ; Kang, Jiho et al. / Multimodal Deep Learning for Patent Classification. Proceedings of 6th International Congress on Information and Communication Technology, ICICT 2021. editor / Xin-She Yang ; Simon Sherratt ; Nilanjan Dey ; Amit Joshi. Springer Science and Business Media Deutschland GmbH, 2022. pp. 281-289 (Lecture Notes in Networks and Systems).

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abstract = "The global technology market is changing rapidly. Organizations need strategies to increase their competitiveness. Technology transfer is an effective tool for them to plan for intellectual property research and development. Prediction of technology transfer leads to efficient results. For this, we propose multimodal learning. The model utilizes the quantitative information and text of the patent. Unlike the previous studies, it uses the architecture of deep learning. The model was tested for practical applicability through patent data. As a result, it showed higher performance than other models.",

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author = "Juhyun Lee and Junseok Lee and Jiho Kang and Youngho Kim and Dongsik Jang and Sangsung Park",

note = "Funding Information: Acknowledgements This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Republic of Korea government (MSIT) (No. NRF– 2020R1A2C1005918). This research was supported by the MOTIE (Ministry of Trade, Industry, and Energy) in Korea, under the Fostering Global Talents for Innovative Growth Program (P0008749) supervised by the Korea Institute for Advancement of Technology (KIAT). Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 6th International Congress on Information and Communication Technology, ICICT 2021 ; Conference date: 25-02-2021 Through 26-02-2021",

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Multimodal Deep Learning for Patent Classification

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