TY - GEN
T1 - TradeMarker - Artificial Intelligence Based Trademarks Similarity Search Engine
AU - Mosseri, Idan
AU - Rusanovsky, Matan
AU - Oren, Gal
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - A trademark is a mark used by a company or a private human for the purpose of marking products or services that they manufacture or trade in. A restriction on the use of the trademark is necessary to enable sellers and manufacturers to build a reputation for themselves, to differentiate themselves from their competitors and thereby promote their businesses. In addition, the restriction also serves consumers and prevents their misuse by a name similar to another product. This restriction is done through the formal examination and approval of the trademarks. This process entails trademark examination against other approved trademarks which is currently a long manual process performed by experienced examiners. Current state-of-the-art trademark similarity search systems attempt to provide a single metric to quantify trademark similarities to a given mark [6–11]. In this work we introduce a new way to carry out this process, by simultaneously conducting several independent searches on different similarity aspects - Automated content similarity, Image/pixel similarity, Text similarity, and Manual content similarity. This separation enables us to benefit from the advantages of each aspect, as opposed to combining them into one similarity aspect and diminishing the significance of each one of them.
AB - A trademark is a mark used by a company or a private human for the purpose of marking products or services that they manufacture or trade in. A restriction on the use of the trademark is necessary to enable sellers and manufacturers to build a reputation for themselves, to differentiate themselves from their competitors and thereby promote their businesses. In addition, the restriction also serves consumers and prevents their misuse by a name similar to another product. This restriction is done through the formal examination and approval of the trademarks. This process entails trademark examination against other approved trademarks which is currently a long manual process performed by experienced examiners. Current state-of-the-art trademark similarity search systems attempt to provide a single metric to quantify trademark similarities to a given mark [6–11]. In this work we introduce a new way to carry out this process, by simultaneously conducting several independent searches on different similarity aspects - Automated content similarity, Image/pixel similarity, Text similarity, and Manual content similarity. This separation enables us to benefit from the advantages of each aspect, as opposed to combining them into one similarity aspect and diminishing the significance of each one of them.
KW - Artificial intelligence
KW - Computer vision
KW - Deep learning
KW - Image search
KW - Trademark
UR - http://www.scopus.com/inward/record.url?scp=85069720855&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-23525-3_13
DO - 10.1007/978-3-030-23525-3_13
M3 - Conference contribution
AN - SCOPUS:85069720855
SN - 9783030235246
T3 - Communications in Computer and Information Science
SP - 97
EP - 105
BT - HCI International 2019 - Posters - 21st International Conference, HCII 2019, Proceedings
A2 - Stephanidis, Constantine
PB - Springer Verlag
T2 - 21st International Conference on Human-Computer Interaction, HCI International 2019
Y2 - 26 July 2019 through 31 July 2019
ER -