2-gram-based Phonetic Feature Generation for Convolutional Neural Network in Assessment of Trademark Similarity
Abstract
A trademark is a mark used to identify various commodities. If same or similar trademark is registered for the same or similar commodity, the purchaser of the goods may be confused. Therefore, in the process of trademark registration examination, the examiner judges whether the trademark is the same or similar to the other applied or registered trademarks. The confusion in trademarks is based on the visual, phonetic or conceptual similarity of the marks. In this paper, we focus specifically on the phonetic similarity between trademarks. We propose a method to generate 2D phonetic feature for convolutional neural network in assessment of trademark similarity. This proposed algorithm is tested with 12,553 trademark phonetic similar pairs and 34,020 trademark phonetic non-similar pairs from 2010 to 2016. As a result, we have obtained approximately 92% judgment accuracy.
Keywords
Cite
@article{arxiv.1802.03581,
title = {2-gram-based Phonetic Feature Generation for Convolutional Neural Network in Assessment of Trademark Similarity},
author = {Kyung Pyo Ko and Kwang Hee Lee and Mi So Jang and Gun Hong Park},
journal= {arXiv preprint arXiv:1802.03581},
year = {2018}
}
Comments
10 pages, 6 figures, 10 tables