This paper introduces Aviath AI, a cutting-edge virtual assistant that combines state-of-the-art natural language processing (NLP), federated learning, and context-aware dialogue management. Aviath AI is designed to provide a more precise, efficient, and privacy-respecting alternative to current virtual assistants. The system was tested against several criteria, such as task completion accuracy, response time, resource usage, and user satisfaction. Outcomes indicate that Aviath AI surpasses industry competitors in task correctness, registering a 92% success rate for simple tasks and 75% for intricate, multi-turn interactions. Data privacy is maintained by the federated learning process of the system, which upholds high levels of user confidence.Although certain difficulties arise in the case of vague queries and resource usage in intensive tasks, Aviath AI reflects enormous potential in boosting virtual assistant technology. The work concludes with future prospectives for improvements on query handling and performance.
Virtual Assistant, Natural Language Processing, Federated Learning, Privacy, Task Completion, User Satisfaction, Context- Aware Dialogue Management, AI, Machine Learning, System Evaluation
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