Quaternion Motion Prediction for Low-Latency Object Tracking

The system performs preliminary actions by storing historical quaternion data in a buffer and pre-calculating predictive models before they are needed. When real-time tracking is required, the system uses pre-stored historical data to quickly generate predictions, avoiding the need for complex real-time calculations and reducing latency.

The patent implements dynamic prediction by continuously updating the quaternion buffer with new sensor data and adjusting predictive models based on recent motion patterns. The system adapts its prediction algorithm dynamically, using more recent historical data points when the object is moving quickly and fewer points when motion is slow, optimizing both accuracy and response time.

The system applies partial action by using a limited buffer size (e.g., storing only the most recent N quaternions) rather than all historical data. This provides sufficient accuracy for prediction while keeping data processing manageable. The buffer size is optimized to provide the minimum necessary historical context for accurate prediction without overwhelming the processing system.

The patent changes parameters dynamically by adjusting the weight given to different historical quaternion entries based on their age. More recent quaternions are given higher weights in the prediction calculation, while older entries receive lower weights. This parameter change allows the system to focus computational resources on the most relevant data, improving accuracy without proportionally increasing processing complexity.

The system performs preliminary action by pre-processing sensor data into quaternion format and storing it in a buffer as it arrives, rather than waiting to collect all necessary data before processing. This preliminary organization of data enables faster prediction when needed, reducing response time without requiring excessive computational power at the moment of prediction.

The patent substitutes complex mechanical or hardware-based tracking solutions with computational prediction using quaternion mathematics. Instead of using more sophisticated sensors or faster hardware, the system uses mathematical prediction algorithms that run efficiently on standard processors, achieving high response speed with moderate computational energy requirements.