[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128796-en":3,"doc-seo-128796-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128796,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","A Machine Learning-Based Predictive Framework for Patent Infringement Detection - Enhancing Intellectual Property Protection Through Hybrid Ensemble Models","Patent infringement threatens innovation and economic growth, while conventional IP protection is often reactive, costly, and inefficient for large-scale patent management. This study proposes a proactive machine learning framework to predict patent infringements using a curated dataset enriched with patent citations, legal status, and family size. Random Forest, SVM, and Logistic Regression are evaluated with SMOTE for class imbalance handling and RFE for feature selection. A hybrid ensemble combining Random Forest and SVM achieves strong results (75% precision, 95% recall, F1-score 84%), supporting scalable early detection and reduced litigation costs.","A Machine Learning-Based Predictive Framework for Patent Infringement Detection: Enhancing Intellectual Property Protection Through Hybrid Ensemble Models  \nKang Jin Ganga *, Ang Ling Weayb  \na,bMalaysia University of Science and Technology (MUST), Block B, Encorp Strand Garden Office, No. 12, Jalan PJU 5/5, Kota Damansara, 47810 Petaling Jaya, Selangor, Malaysia aEmail: [kang.jingang@phd.must.edu.my](kang.jingang@phd.must.edu.my)  \n[b](bEmail: dr.ang@must.edu.my)[Email: dr.ang@must.edu.my](bEmail: dr.ang@must.edu.my)  \nAbstract  \nPatent 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.  \nKeywords: Patent Infringement Prediction; Machine Learning; Hybrid Ensemble Algorithm; Intellectual Property Management; Data Balancing; Feature Selection.  \nReceived: 2/27/2025  \nAccepted: 4/25/2025  \nPublished: 5/5/2025  \n* Corresponding author.  \n1. Introduction  \nIntellectual property (IP) protection plays a critical role in driving technological innovation and sustaining economic growth. As patents form a central mechanism through which inventors and organizations protect their innovations, the integrity of patent systems directly influences research incentives and market competitiveness. However, the exponential rise in patent filings globally has introduced significant challenges. According to the World Intellectual Property Organization (WIPO), over 3.4 million patent applications were filed globally in 2021, with Asia accounting for more than 67% of this volume [1] (WIPO, 2022) . This rapid growth has been accompanied by a corresponding surge in patent infringement cases, making it increasingly difficult for institutions and enterprises to manage and protect their intellectual assets effectively.  \nTraditional methods of patent protection, which primarily involve manual patent analysis, post-violation enforcement, and prolonged legal disputes, are no longer adequate in the current digital and data-intensive environment. These reactive approaches are resource-intensive, time-consuming, and often fail to provide timely intervention against potential threats. Moreover, the legal complexities involved in patent litigation often lead to high costs, making enforcement unfeasible, especially for small and medium enterprises (SMEs) and academic institutions [2] (Zhao and his colleagues 2020) .  \nIn response to these limitations, machine learning (ML) has emerged as a transformative approach capable of revolutionizing intellectual property management. ML algorithms are designed to process vast datasets, uncover complex patterns, and generate predictive insights—capabilities that are well-suited to the dynamic and multifaceted nature of patent data [3] (Nguyen and his colleagues 2021) . Through the integration of predictive analytics, organizations can shift from reactive to proactive IP protection strategies, enabling them to detect potential infringement risks early and act accordingly.  \nRecent studies have demonstrated the effectiveness of ML models such as Random Forest, Support Vector Machi","cbCaiabqg9ASKpCM","https://ap.wps.com/l/cbCaiabqg9ASKpCM","pdf",625390,2,1,9,"English","en",105,"# Abstract\n# 1. Introduction\n# 2. Methodology","[{\"question\":\"What problem does the proposed framework address?\",\"answer\":\"It targets the challenge that patent infringement prediction is difficult to manage proactively under increasing patent volumes, where traditional methods are reactive, expensive, and inefficient.\"},{\"question\":\"Which machine learning models and techniques are used?\",\"answer\":\"The study evaluates Random Forest, SVM, and Logistic Regression, and applies SMOTE to handle class imbalance and RFE to select informative features.\"},{\"question\":\"What is the key contribution of the proposed model?\",\"answer\":\"It introduces a hybrid ensemble model that integrates Random Forest and SVM, improving performance for infringement prediction beyond baseline approaches.\"}]","A Machine Learning-Based Predictive Framework for Patent Infringement Detection - Enhancing Intellectual Property Protection Through Hybrid Ensemble Models | 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