Functional Requirements and Object-Oriented System Modeling for Designing AI-Driven Intelligent Catering Systems

The food industry has undergone a significant transformation in recent decades due to globalization, technological advancements, and evolving customer expectations. Artificial Intelligence (AI) and the Internet of Things (IoT) are now playing a critical role in enhancing food production, marketing, and service delivery. This study proposes an AI-driven intelligent system to improve restaurant catering services through contactless service using Natural Language Processing(NLP) and Linear Discriminant Analysis(LDA), personalized food recommendations through a Convolutional Recurrent Neural Network(Conv-RNN) model, and customer satisfaction prediction using an optimized Convolutional Long Short Term Memory(Conv-LSTM) model. Real-world experiments demonstrate that the proposed system outperforms traditional rule-based methods, achieving 91.5% accuracy, 91% precision, 91.1% recall, and an F1 score of 89.7% with Word2Vec-LDA; 98.5% accuracy with a loss of 0.02 in the Conv-RNN model; and an RMSE of 0.1011 with an R² of 0.9812 in the Conv-LSTM system. These results highlight the transformative potential of AI in automating and enhancing customer service in the restaurant industry.