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This study analyzed the effectiveness of different Natural Language Processing (NLP) models in assessing the risk of collisions between word marks. Three common NLP models for text classification were analyzed—Word2Vec, BERT, and FastText—and applied to 304-word mark cases from the National Institute of Industrial Property (INPI) database that were denied registration due to collisions. The models’ Accuracy, Precision, Recall, and F1-score metrics were calculated to assess their performance in identifying collision risks. The results showed that although all the models achieved 100% Recall when flagging all collision cases, BERT performed best with 86.51% Accuracy and an F1-score of 92.77%, correctly classifying most cases. Word2Vec showed lower Accuracy and Precision compared to the other models. FastText also provided relatively accurate results. This study advances the understanding of applying NLP approaches such as Word2Vec, BERT, and FastText to analyze trademark similarity semantically. The findings offer practical guidance to practitioners on how to check collisions efficiently and quickly using NLP techniques. All searches were performed fully online, a powerful and growing trend in academia and other industries, given the evolution of technology.

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Nominative Trademark Collision Analysis: A Study on the Performance of Natural Language Processing Models

  • 会议论文

  • 发表日期：

    2026年

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  • Mateus Miranda Torres,
  • Rafael Angelo Santos Leite,
  • Igor Bezerra Reis,
  • Lucas Taylor de Sousa Aires,
  • Cicero Eduardo Walter,
  • Manuel Au-Yong-Oliveira

摘要

This study analyzed the effectiveness of different Natural Language Processing (NLP) models in assessing the risk of collisions between word marks. Three common NLP models for text classification were analyzed—Word2Vec, BERT, and FastText—and applied to 304-word mark cases from the National Institute of Industrial Property (INPI) database that were denied registration due to collisions. The models’ Accuracy, Precision, Recall, and F1-score metrics were calculated to assess their performance in identifying collision risks. The results showed that although all the models achieved 100% Recall when flagging all collision cases, BERT performed best with 86.51% Accuracy and an F1-score of 92.77%, correctly classifying most cases. Word2Vec showed lower Accuracy and Precision compared to the other models. FastText also provided relatively accurate results. This study advances the understanding of applying NLP approaches such as Word2Vec, BERT, and FastText to analyze trademark similarity semantically. The findings offer practical guidance to practitioners on how to check collisions efficiently and quickly using NLP techniques. All searches were performed fully online, a powerful and growing trend in academia and other industries, given the evolution of technology.