Context-Aware Task Thresholding for Multi-Action Electronic Devices

The patent applies dynamics by transforming the fixed threshold into a dynamic threshold that adapts to environmental conditions. The processor obtains context information (such as lighting conditions, location, time) and adjusts the threshold value accordingly. For example, in low-light conditions, the threshold for human recognition success is lowered to account for reduced image quality, while in bright conditions, a higher threshold is applied. This dynamic adjustment resolves the contradiction by maintaining high reliability across varying environments without requiring overly complex predetermined rule systems.

The patent changes the parameter of the threshold value based on context information. Instead of using a single fixed parameter, the system modifies the threshold parameter dynamically according to environmental factors. The processor calculates adjusted threshold values by combining base thresholds with context-dependent adjustments, such as adding a penalty term for low-light conditions or modifying the threshold based on historical success rates in similar environments. This parameter change approach maintains reliability while keeping the system relatively simple.

The patent applies partial action by selectively obtaining and processing only the most relevant context information rather than all possible data. The processor prioritizes obtaining context information that has the greatest impact on threshold determination, such as lighting conditions for vision tasks or location data for navigation tasks. Less critical context information is either obtained with lower priority or skipped entirely, reducing energy consumption while maintaining sufficient task completion accuracy.

The system uses self-service by leveraging context information that is already available from the device's existing sensors and systems. Rather than adding dedicated high-energy sensors for context detection, the patent repurposes data from existing components (camera metadata for lighting information, GPS for location, clock for time) to inform threshold adjustments. This approach minimizes additional energy consumption while still achieving improved task completion accuracy through context-aware thresholding.