Cloud Learning for Smart Windows

The patent introduces a cloud-based server as an intermediary between the electrochromic device and users/environmental conditions. The server receives data from multiple sources including sensors, weather services, and user preferences, processes this information, and generates adaptive control algorithms that are downloaded to the device. This intermediary approach enables sophisticated adaptability without increasing the complexity of the local control system hardware.

The patent transitions control from a local two-dimensional space (device-level sensors and processors) to a cloud-based multi-dimensional space that incorporates environmental data, user preferences, historical patterns, and real-time conditions. This dimensional expansion allows the system to process vastly more variables and generate sophisticated adaptive control strategies without complicating the physical device architecture.

The patent extracts the complex data processing and algorithm generation functions from the local device and places them in the cloud environment. The electrochromic device itself remains relatively simple, while the energy-optimizing adaptive control algorithms are developed and maintained remotely on servers that have access to vast computational resources and multiple data sources.

The system implements continuous feedback loops where the server collects performance data from the electrochromic device, analyzes energy consumption patterns along with environmental and user preference data, and iteratively refines control algorithms. This feedback mechanism enables progressive optimization of energy efficiency while the system adapts to changing conditions and user behaviors over time.