Smartphone Usage Data Cleaning Using LLM-Based Processing

The proliferation of smartphone technology has generated unprecedented volumes of data, creating challenges in under-standing digital behavior patterns. We present a new computational system that integrates Large Language Models (LLMs) with conventional data processing techniques using a novel three-levelOur system employs LLMs for zero-shot learning capabilities to classify usage patterns, achieving 95% accuracy in task classification through automated pattern identification. The system implements pattern verification reaching 98% validation accuracy and utilizes automated validation that reduces data loss by 75%. This hierarchical approach demonstrates consistent performance across diverse device types and usage scenarios while maintaining processing efficiency through automated prompt engineering and code generation.