Multimodal Deep Learning for Patent Classification

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationProceedings of 6th International Congress on Information and Communication Technology, ICICT 2021
EditorsXin-She Yang, Simon Sherratt, Nilanjan Dey, Amit Joshi
PublisherSpringer Science and Business Media Deutschland GmbH
Pages281-289
Number of pages9
ISBN (Print)9789811621017
DOIs
Publication statusPublished - 2022
Event6th International Congress on Information and Communication Technology, ICICT 2021 - Virtual, Online
Duration: 2021 Feb 252021 Feb 26

Publication series

NameLecture Notes in Networks and Systems
Volume217
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference6th International Congress on Information and Communication Technology, ICICT 2021
CityVirtual, Online
Period21/2/2521/2/26

Keywords

  • Multimodal learning
  • Patent classification
  • Technology transfer

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

Fingerprint

Dive into the research topics of 'Multimodal Deep Learning for Patent Classification'. Together they form a unique fingerprint.

Cite this