Conflicting Marks Archive Dataset: A Dataset of Conflicting Marks from the Brazilian Intellectual Property Office

Author:

Reis Igor Bezerra1ORCID,Leite Rafael Ângelo Santos2ORCID,Torres Mateus Miranda2ORCID,Neto Alcides Gonçalves da Silva2ORCID,Silva Francisco José da Silva e1ORCID,Teles Ariel Soares13ORCID

Affiliation:

1. Postgraduate Program in Computer Science, Federal University of Maranhão, São Luís 65085-580, Brazil

2. Federal Institute of Piauí, Floriano 64808-475, Brazil

3. Federal Institute of Maranhão, Araioses 65570-000, Brazil

Abstract

A registered trademark represents one of a company’s most valuable intellectual assets, acting as a safeguard against possible reputational damage and financial losses resulting from infringements of this intellectual property. To be registered, a mark must be unique and distinctive in relation to other trademarks which are already registered. In this paper, we describe the CMAD, an acronym for Conflicting Marks Archive Dataset. This dataset has been meticulously organized into pairs of marks (Number of pairs = 18,355) involved in copyright infringement across word, figurative and mixed marks. Organizations sought to register these marks with the National Institute of Industrial Property (INPI) in Brazil, and had their applications denied after analysis by intellectual property specialists. The robustness of this dataset is ensured by the intrinsic similarity of the conflicting marks, since the decisions were made by INPI specialists. This characteristic provides a reliable basis for the development and testing of tools designed to analyze similarity between marks, thus contributing to the evolution of practices and computer-based solutions in the field of intellectual property.

Funder

Coordenação de Aperfeicoamento de Pessoal de Nível Superior

National Council for Scientific and Technological Development

Publisher

MDPI AG

Reference47 articles.

1. Vesnin, D., Levshun, D., and Chechulin, A. (2023). Trademark Similarity Evaluation Using a Combination of ViT and Local Features. Information, 14.

2. Intelligent trademark similarity analysis of image, spelling, and phonetic features using machine learning methodologies;Trappey;Adv. Eng. Inform.,2020

3. Trademark dilution and its practical effect on purchase decision;Span. J. Mark. Esic,2017

4. National Institute of Industrial Property (2023, October 24). Manual de Marcas, Available online: http://manualdemarcas.inpi.gov.br/projects/manual/wiki/02_O_que_%C3%A9_marca#2-O-que-%C3%A9-marca.

5. World Intellectual Property Organization (2023, October 24). Trademarks. Available online: https://www.wipo.int/trademarks/en/.

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