3 research outputs found
Semantic retrieval of trademarks based on conceptual similarity
Trademarks are signs of high reputational value. Thus, they require protection. This paper studies conceptual similarities between trademarks, which occurs when two or more trademarks evoke identical or analogous semantic content. This paper advances the state-of-the-art by proposing a computational approach based on semantics that can be used to compare trademarks for conceptual similarity. A trademark retrieval algorithm is developed that employs natural language processing techniques and an external knowledge source in the form of a lexical ontology. The search and indexing technique developed uses similarity distance, which is derived using Tversky's theory of similarity. The proposed retrieval algorithm is validated using two resources: a trademark database of 1400 disputed cases and a database of 378,943 company names. The accuracy of the algorithm is estimated using measures from two different domains: the R-precision score, which is commonly used in information retrieval and human judgment/collective human opinion, which is used in human-machine systems
Multi-faceted Assessment of Trademark Similarity
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 1,400 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.
Keywords—Trademark assessment, trademark infringement, trademark retrieval, degree of similarity, fuzzy aggregation, semantic similarity, phonetic similarity, visual similarity
Trade mark similarity assessment support system
Trade marks are valuable intangible intellectual property (IP) assets with potentially
high reputational value that can be protected. Similarity between trade marks may
potentially lead to infringement. That similarity is normally assessed based on the
visual, conceptual and phonetic aspects of the trade marks in question. Hence, this
thesis addresses this issue by proposing a trade mark similarity assessment support
system that uses the three main aspects of trade mark similarity as a mechanism to
avoid future infringement.
A conceptual model of the proposed trade mark similarity assessment support
system is first proposed and developed based on the similarity assessment criteria
outlined in a trade mark manual. The proposed model is the first contribution of this
study, and it consists of visual, conceptual, phonetic and inference engine modules.
The second contribution of this work is an algorithm that compares trade
marks based on their visual similarity. The algorithm performs a similarity
assessment using content-based image retrieval (CBIR) technology and an
integrated visual descriptor derived using the low-level image feature, i.e. the shape
feature. The performance of the algorithm is then assessed using information
retrieval based measures. The obtained result demonstrates better retrieval
performance in comparison to the state of the art algorithm.
The conceptual aspect of trade mark similarity is then examined and analysed
using a proposed algorithm that employs semantic technology in the conceptual
module. This contribution enables the computation of the conceptual similarity
between trade marks, with the utilisation of an external knowledge source in the
form of a lexical ontology, together with natural language processing and set
similarity theory. The proposed algorithm is evaluated using both information
VI
retrieval and human collective opinion measures. The retrieval result produced by
the proposed algorithm outperforms the traditional string similarity comparison
algorithm in both measures.
The phonetic module examines the phonetic similarity of trade marks using
another proposed algorithm that utilises phoneme analysis. This algorithm employs
phonological features, which are extracted based on human speech articulation. In
addition, the algorithm also provides a mechanism to compare the phonetic aspect
of trade marks with typographic characters. The proposed algorithm is the fourth
contribution of this study. It is evaluated using an information retrieval based
measure. The result shows better retrieval performance in comparison to the
traditional string similarity algorithm.
The final contribution of this study is a methodology to aggregate the overall
similarity score between trade marks. It is motivated by the understanding that trade
mark similarity should be assessed holistically; that is, the visual, conceptual and
phonetic aspects should be considered together. The proposed method is
developed in the inference engine module; it utilises fuzzy logic for the inference
process. A set of fuzzy rules, which consists of several membership functions, is
also derived in this study based on the trade mark manual and a collection of trade
mark disputed cases is analysed. The method is then evaluated using both
information retrieval and human collective opinion. The proposed method improves
the retrieval accuracy and the experiment also proves that the aggregated similarity
score correlates well with the score produced from human collective opinion.
The evaluations performed in the course of this study employ the following
datasets: the MPEG-7 shape dataset, the MPEG-7 trade marks dataset, a collection
of 1400 trade marks from real trade mark dispute cases, and a collection of 378,943
company names