Intelligent Forecasting of Trademark Registration Appeal with TF-IDF and XGBoost

Against the backdrop of rapid advancements in information technology, predictive algorithms are increasingly being integrated into various industries and domains. Despite the global prominence of this trend, the application of such algorithms within the niche of trademark law, particularly in China, has not yet been explored or developed extensively. Given the escalating volume of trademark-related disputes and the strain on the existing administrative review systems tasked with managing such caseloads, the incorporation of predictive algorithms to enhance the efficiency of processing these non-litigious administrative actions is paramount. This study innovates by synthesizing the TF-IDF algorithm with the XGBoost model to develop a first predictive model for trademark rejection appeals. The model demonstrates remarkable performance with an accuracy rate of 68%, marking a significant academic contribution by filling a research void and proving its practical worth. From the perspective of trademark applicants, the model offers data-driven decision support that mitigates time and financial costs. For administrative and review bodies, it promises to reduce systemic costs associated with handling trademark rejection appeal cases, thereby optimizing efficiency. The model’s codebase is made available to the public, accessible at: https://github.com/ValeriaWong/Trademark_Appeals .