MACHINE LEARNING APPROACHES FOR INTELLECTUAL PROPERTY PROTECTION: A TECHNICAL FRAMEWORK FOR CLOUD ENVIRONMENTS
Keywords:
Machine Learning, Intellectual Property Protection, Cloud Computing, Computer Vision, Natural Language ProcessingAbstract
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. Through detailed analysis of different implementation approaches, the article evaluates the trade-offs between speed, accuracy, and resource utilization while providing insights into cost-effectiveness and return on investment. The article also examines emerging technologies and integration strategies, offering a forward-looking perspective on the evolution of IP protection systems. This comprehensive approach provides organizations with a structured methodology for implementing and optimizing ML-based IP protection solutions while maintaining efficiency and effectiveness in cloud environments.
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