A Machine Learning-Based Predictive Framework for Patent Infringement Detection: Enhancing Intellectual Property Protection Through Hybrid Ensemble Models
Keywords:
Patent Infringement Prediction, Machine Learning, Hybrid Ensemble Algorithm, Intellectual Property Management, Feature Selection, Data BalancingAbstract
Patent infringement poses significant risks to innovation and economic growth. Traditional intellectual property (IP) protection methods are often reactive, expensive, and inefficient for large-scale patent management. This study introduces an optimized machine learning framework designed to predict patent infringements proactively. The research evaluates the performance of Random Forest, Support Vector Machines (SVM), and Logistic Regression on a curated dataset enriched with patent citations, legal status, and family size. The study employs Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance and Recursive Feature Elimination (RFE) for feature selection. A novel hybrid ensemble model integrating Random Forest and SVM is developed, achieving 75% precision, 95% recall, and an F1-score of 84%, outperforming baseline models. The findings contribute to IP management by offering a scalable predictive framework that minimizes litigation costs and enhances proactive infringement detection.
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