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(사)한국여성발명협회
- The Inventors News
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- no.27
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- pp.3-4
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- 2004
한국특허정보원 `대한민국경영품질대상` 품질경영부문 최우수상 수상 - 웰빙 붐과 함께 건강양말이 뜨고 있다 - 중기육성자금 1조2천억원으로 확대 - 한국과학재단 `올해의 여성과학기술자상` 공모 - (주)우리식품 `해초록 아이스 찰떡` 선보여 - 네팔의 어린 소녀에게 전달된 장비 상자 - 정지용의 `향수` 상표권 되찾기 위한 취소 심판 열려 - 박세준 이앤테크 대표 발명 노하우 공개 - 특허청, 세계 최초 WIPO와 온라인 문서교환 시스템 구축
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Song, Soo-Ryun;Lim, Sung-Chul
- Korea Trade Review
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- v.41 no.3
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- pp.157-176
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- 2016
This study is to investigate Trademark Prior Use Defence of prior use party under Trademark Law of the People's Republic of China. Chinese Trademark Law was amended for the third time and this Law shall enter into force on 1 May 2014. This third amendment introduced Prior Use Defence Right of Trademark for the first time. Article 59(3) gives the right to the prior use party for the continuous use of such trademark under the condition that first, an identical or similar trademark has been used in connection with the same goods or similar goods by others before the registrant's application, second, such trademark should have a certain influence in certain market, and third, such aforesaid trademark should be used within the original scope continuously. Then the exclusive right holder of said registered trademark shall have no right to prohibit others from continuous use of such trademark. Korean companies should be aware that it is almost impossible to search prior use trademark before a dispute arises, since the prior use trademark has never been registered. The best way to control the prior use trademark is to superintend aforesaid trademark for the use within the original scope.
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Gu, Bongen
- Journal of Platform Technology
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- v.8 no.1
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- pp.16-23
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- 2020
For protecting copyright for trademark, convolutional neural network can be used to confirm genuine of trademark image. For this, repeated training one trademark image degrades the performance of machine learning because of overfitting problem. Therefore, this type of machine learning application generates training data in various way. But if genuine trademark image is rotated, this image is classified as not genuine trademark. In this paper, we propose the way for extending training data to confirm genuine of trademark image which is rotated. Our proposed way generates rotated image from genuine trademark image as training data. To show effectiveness of our proposed way, we use CNN machine learning model, and evaluate the accuracy with test image. From evaluation result, our way can be used to generate training data for machine learning application which confirms genuine of rotated trademark image.
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