Intelligent Electronic Device Disturbance Mode Control

The system automatically detects surrounding situations using sensors (illumination, sound, motion) and autonomously determines and applies appropriate disturbance interruption modes without requiring user intervention. The processor analyzes sensor data to identify situations such as meetings, driving, or nighttime scenarios and automatically configures notification settings, volume, screen brightness, and vibration patterns accordingly.

The patent replaces manual mechanical adjustment of notification settings with an automated electronic system that uses sensor data and machine learning algorithms. Instead of users physically navigating through settings menus, the system uses illumination sensors, sound sensors, and motion sensors combined with neural network processing to automatically determine and apply the appropriate disturbance interruption mode.

The system performs preliminary detection and analysis of surrounding situations using sensors before user actions are needed. By continuously monitoring illumination levels, sound patterns, and motion data, the system is already prepared with situation identification when a disturbance interruption mode change is required, enabling immediate automatic application of appropriate settings without user delay.

The system continuously receives feedback from sensors (illumination sensor values, sound sensor values, motion sensor values) and uses this feedback to dynamically adjust disturbance interruption modes. The processor analyzes the feedback data in real-time and automatically modifies notification settings based on the detected situation, creating a closed-loop system that adapts to changing environmental conditions without user intervention.

The patent employs a multi-functional approach where a single processor handles multiple tasks: it processes data from various sensors (illumination, sound, motion), performs situation classification using machine learning models, determines appropriate disturbance interruption modes, and controls multiple output parameters (notification settings, volume, screen brightness, vibration). This universal processing core reduces the need for separate dedicated components for each function.

The system manages complexity by dynamically changing operational parameters rather than adding permanent hardware components. The processor adjusts software-based parameters such as sensor thresholds, classification model configurations, and mode priority levels based on learned patterns from sensor data. This allows the system to adapt to different situations through parameter modification rather than physical reconfiguration.