MACHINE LEARNING APPROACHES FOR INTELLECTUAL PROPERTY PROTECTION: A TECHNICAL FRAMEWORK FOR CLOUD ENVIRONMENTS

This article presents a comprehensive technical framework for implementing machine learning approaches in intellectual property protection within cloud environments.The article explores various methodologies, from foundational search techniques to advanced ML models, examining their effectiveness in safeguarding intellectual property in the digital age.The article investigates keyword-based systems, embedding-based similarity search, and hybrid solutions, alongside computer vision and natural language processing applications.The article addresses critical aspects of cloud implementation, including scalability factors, resource optimization, and performance considerations.