2-gram-based Phonetic Feature Generation for Convolutional Neural Network in Assessment of Trademark Similarity

TL;DR

This paper introduces a 2-gram-based phonetic feature generation method for CNNs to assess trademark similarity, achieving around 92% accuracy in distinguishing phonetic similarities.

Contribution

It presents a novel 2D phonetic feature extraction technique specifically designed for CNNs in trademark similarity assessment.

Findings

Achieved approximately 92% accuracy in phonetic similarity judgment.

Validated method on over 46,000 trademark pairs from 2010-2016.

Demonstrated effectiveness of phonetic features in trademark analysis.

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.