دانلود رایگان مقاله ارزیابی چند بعدی شباهت علامت تجاری

abstract

Trademarks are intellectual property assets with potentially high reputational value. Their infringement may lead to lost revenue, lower profits and damages to brand reputation. A test normally conducted to check whether a trademark is highly likely to infringe other existing, already registered, trademarks is called a likelihood of confusion test. One of the most influential factors in this test is establishing similarity in appearance, meaning or sound. However, even though the trademark registration process suggests a multi-faceted similarity assessment, relevant research in expert systems mainly focuses on computing individual aspects of similarity between trademarks. Therefore, this paper contributes to the knowledge in this field by proposing a method, which, similar to the way people perceive trademarks, blends together the three fundamental aspects of trademark similarity and produces an aggregated score based on the individual visual, semantic and phonetic assessments. In particular, semantic similarity is a new aspect, which has not been considered by other researchers in approaches aimed at providing decision support in trademark similarity assessment. Another specific scientific contribution of this paper is the innovative integration, using a fuzzy engine, of three independent assessments, which collectively provide a more balanced and human-centered view on potential infringement problems. In addition, the paper introduces the concept of degree of similarity since the line between similar and dissimilar trademarks is not always easy to define especially when dealing with blending three very different assessments. The work described in the paper is evaluated using a database comprising 1400 trademarks compiled from a collection of real legal cases of trademark disputes. The evaluation involved two experiments. The first experiment employed information retrieval measures to test the classification accuracy of the proposed method while the second used human collective opinion to examine correlations between the trademark scoring/rating and the ranking of the proposed method, and human judgment. In the first experiment, the proposed method improved the F-score, precision and accuracy of classification by 12.5%, 35% and 8.3%, respectively, against the best score computed using individual similarity. In the second experiment, the proposed method produced a perfect positive Spearman rank correlation score of 1.00 in the ranking task and a pairwise Pearson correlation score of 0.92 in the rating task. The test of significance conducted on both scores rejected the null hypotheses of the experiment and showed that both scores correlated well with collective human judgment. The combined overall assessment could add value to existing support systems and be beneficial for both trademark examiners and trademark applicants. The method could be further used in addressing recent cyberspace phenomena related to trademark infringement such as customer hijacking and cybersquatting.

6. Conclusions

A support system to assess the overall degree of similarity between trademarks is essential for trademark protection so the work presented in this paper was motivated by the need to help prevent trademark infringement by identifying existing similarities between trademarks.

This paper contributes to the body of knowledge in this area by the development of a method that measures the degree of similarity between trademarks on the basis of all three aspects of similarity: visual, semantic and phonetic. The method uses fuzzy logic to aggregate the overall assessment, which provides a more balanced and human-centered view on potential infringement problems. In addition, the paper introduces the concept of degree of similarity since the line between similar and dissimilar trademarks is not always easy to define especially when dealing with blending three very different assessments.

One of the strengths of the proposed method is its rigorous evaluation using a large, purpose-built collection of real legal cases of trademark disputes. Moreover, the experiments performed in this study examined the performance of the proposed method from two points of view. First, the relative performance of the method was investigated from an information retrieval perspective in terms of classification performance. Using a crowdsourcing platform, the second experiment investigated the performance of the method relative to human judgment. The results of the experiments confirmed that there is a significant improvement in trademark similarity assessment when all similarity aspects are carefully considered. The results also showed that the proposed method demonstrates a statistically significant correlation against human collective judgment. Therefore, the experiments convincingly validated both original hypotheses outlined in this study.

In conclusion, the proposed system can provide a support mechanism in the trademark similarity analysis performed by trademark examiners during trademark registration. Moreover, the method for assessing the trademark similarity could be extended to address recent cyberspace phenomena such as consumer hijacking and cybersquatting. A particular limitation of the proposed work is its focus on only one aspect of the concept of likelihood of confusion, i.e. computing the similarity between trademarks. In reality, there are several other factors influencing the perceptions of the consumers. Such factors include strength of the registered trademarks, proximity of the channels of trade, product relatedness and consumer traits (sophistication and care). Such a study, which is currently underway, requires a multi-disciplinary approach, which involves experts from business studies, marketing, psychology and engineering.