Personalized Golf Recommendation System Using ML Sensor Fusion

The system segments the complex data processing task into distinct modules: sensor data acquisition module, machine learning algorithm module, and recommendation generation module. Each module handles specific aspects of data processing independently, reducing overall system complexity while maintaining high accuracy through specialized processing at each stage.

The patent introduces intermediary components including data processing servers and communication networks that mediate between the sensors and the recommendation engine. These intermediaries handle raw data filtering, aggregation, and preliminary analysis, reducing the computational burden on the core machine learning algorithms and simplifying the overall system architecture.

The system implements periodic action by processing data at strategically determined intervals rather than continuously. The recommendation engine analyzes sensor data at key moments in the golf round (e.g., after each hole or significant shot), providing timely feedback while allowing the system to enter low-power states during intervals between processing cycles, thus reducing overall energy consumption.

The patent maintains continuity of useful action by implementing background data collection and preliminary processing that occurs continuously at low power consumption, with intensive analysis triggered only when needed. This ensures data readiness for immediate analysis when processing is required, maintaining timeliness without sustained high energy consumption.