Systematic Review: Advances in Machine Learning Frameworks for Predicting Patent Infringements
Keywords:
Machine Learning (ML), Patent Infringement Prediction, Intellectual Property (IP), Random Forest Algorithm, Hybrid Machine Learning ModelsAbstract
The rise of patent infringement cases has spurred the demand for innovative solutions in intellectual property (IP) management. This systematic review explores advancements in machine learning (ML) frameworks for predicting patent infringements, focusing on algorithm performance, data balancing, and feature selection. By evaluating Random Forest, Support Vector Machines (SVM), Logistic Regression, and hybrid ensemble models, we provide insights into their strengths and limitations. Key findings highlight the critical role of data preprocessing techniques, such as Synthetic Minority Oversampling Technique (SMOTE) and Recursive Feature Elimination (RFE), in improving model accuracy. Furthermore, ethical and practical considerations, including scalability and bias mitigation, are discussed. The review concludes by proposing a roadmap for integrating advanced ML techniques into proactive IP protection strategies.
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