Abstract
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 [6,7,8,9,10,11]. 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.
Keywords
This work was supported by Google, Israel Ministry of Justice, National Digital Israel Initiative and the Lynn and William Frankel Center for Computer Science. Patent Pending, “Similarity Search Engine for a Digital Visual Object”, IL/18-ORP/38222, Request No. 262220.
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Mosseri, I., Rusanovsky, M., Oren, G. (2019). TradeMarker - Artificial Intelligence Based Trademarks Similarity Search Engine. In: Stephanidis, C. (eds) HCI International 2019 - Posters. HCII 2019. Communications in Computer and Information Science, vol 1034. Springer, Cham. https://doi.org/10.1007/978-3-030-23525-3_13
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