Smart Lamp Local State Modeling for Real-Time Brightness Learning

The patent extracts the AI learning function from the server/cloud environment and embeds it directly into the smart lamp device. The lamp now performs local model training and updates using its own processor and memory, eliminating dependency on network connectivity for model updates. This extraction resolves the contradiction by maintaining model update accuracy through local processing while achieving real-time updates without network delay or interruption.

The patent introduces a local model training module as an intermediary between the lamp's operational data and the AI model. This intermediary processes data locally, performing weighted mean calculations and model updates without requiring network transmission. The intermediary resolves the contradiction by enabling accurate, real-time model updates while avoiding network-related delays and interruptions.

The patent implements dynamic model updating where the learning time period is not fixed but adapts based on the actual time intervals between lamp operations. Instead of accumulating data over a predetermined fixed period, the system continuously learns from each operational event, dynamically adjusting the model based on the most recent user behavior patterns. This dynamic approach improves accuracy by capturing real-time usage patterns while reducing data storage requirements.

The system incorporates feedback mechanisms where each lamp operation provides immediate input for model updating. The weighted mean calculation uses the most recent operational data with higher weights, creating a feedback loop that continuously refines the model based on actual user behavior. This feedback-driven approach achieves high accuracy with minimal data storage by learning incrementally from each interaction rather than requiring extensive historical accumulation.

The patent implements self-service functionality where the lamp automatically determines and adjusts its brightness based on learned user preferences and environmental conditions. The AI model analyzes historical operation data, time of day, and usage patterns to autonomously select optimal brightness levels without requiring manual user input. This self-service capability resolves the contradiction by maintaining simple device operation while significantly improving user convenience through automatic adaptation to user needs.

The system performs preliminary learning and analysis of user preferences during periods when the lamp is not in use, preparing the optimal brightness settings in advance. When the user activates the lamp, the pre-computed brightness settings are immediately applied, eliminating the need for secondary adjustments. This preliminary action resolves the contradiction by keeping the device interface simple while providing personalized, convenient operation through advance preparation of optimal settings.