Intelligent prediction of trademark registration appeal outcomes based on natural language processing and CatBoost algorithm

With the rapid development of artificial intelligence technology, its application in the field of trademark registration applications can save a significant amount of time and human resources. However, research on predicting trademark opposition and review is still relatively limited. This paper proposes a trademark appeal outcome prediction model based on natural language processing and machine learning. The model extracts text features using the TF-IDF algorithm, calculates the similarity between trademark names using the Levenshtein distance, and models the comprehensive features based on the CatBoost algorithm to predict the results of trademark registration appeals. Experiments show that the model achieves an accuracy rate of 69% in predicting trademark opposition and review outcomes, effectively evaluating the similarity between trademark texts, providing auxiliary decision-making for trademark applicants, reducing application time costs, and at the same time, increasing the efficiency of relevant institutions.