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.
|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|
|Number of pages||9|
|Publication status||Published - 2022|
|Event||6th International Congress on Information and Communication Technology, ICICT 2021 - Virtual, Online|
Duration: 2021 Feb 25 → 2021 Feb 26
|Name||Lecture Notes in Networks and Systems|
|Conference||6th International Congress on Information and Communication Technology, ICICT 2021|
|Period||21/2/25 → 21/2/26|
Bibliographical noteFunding 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).
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Multimodal learning
- Patent classification
- Technology transfer
ASJC Scopus subject areas
- Control and Systems Engineering
- Signal Processing
- Computer Networks and Communications