2-gram-based Phonetic Feature Generation for Convolutional Neural Network in Assessment of Trademark Similarity
This work addresses trademark registration examination by automating phonetic similarity assessment, but it is incremental as it applies existing neural network techniques to a specific domain.
The paper tackled the problem of assessing phonetic similarity between trademarks to prevent consumer confusion, achieving approximately 92% judgment accuracy using a 2-gram-based phonetic feature generation method for convolutional neural networks.
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.