An Original On-Device iOS Intelligence Framework for Adaptive User Interaction Using Core ML and Behavioral Modeling

On-device intelligence has emerged as a critical requirement for modern mobile systems due to increasing concerns around latency, privacy, reliability, and regulatory compliance. While prior work has demonstrated the feasibility of executing isolated machine learning models on mobile hardware, there remains a lack of system-level frameworks that integrate behavioral modeling, temporal analysis, and adaptive decision-making in a manner suitable for large-scale iOS deployment. This paper presents an original, end-to-end on-device intelligence framework for iOS applications that models user interaction behavior using structured feature engineering and lightweight machine learning pipelines optimized through Apple Core ML and the Neural Engine. The system captures fine-grained interaction signals, performs deterministic preprocessing and behavioral feature extraction, and applies on-device inference to generate adaptive application responses in real time. Unlike cloud-centric approaches, the proposed framework eliminates network dependency and preserves user privacy by design. Experimental evaluation demonstrates that the framework achieves consistently low latency, reduced energy consumption, and stable performance across devices and usage contexts. The technical contributions of this work lie in its system architecture, modeling methodology, and deployment strategy, offering a practical and scalable blueprint for privacy-preserving mobile intelligence. This work constitutes an independent and original contribution to the field of mobile computing and on-device machine learning.