Automated Calibration Engine for Eye-Tracking Models
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Solution Overview
Problem
Software programs and machine-learning models often face compatibility issues due to variations in hardware and firmware, leading to inaccurate outputs and incompatibilities when deployed on different devices, especially those with eye-tracking capabilities in AR/VR/XR environments.
Innovation Solution
An automated conversion engine is used to calibrate assessment models by converting eye-tracking features data to a compatible sampling rate specific to the unique user device, facilitating integration and validation across various hardware specifications, enabling efficient deployment of machine-learning models on diverse devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine-learning model is trained on specific hardware and firmware, then the model achieves accurate output on that hardware, but the model becomes incompatible with different hardware and firmware configurations
Solution Approach 1:
The patent introduces a calibration layer as an intermediary component between the machine-learning model and the hardware device. This calibration layer contains device-specific parameters and conversion rules that adapt the model's input and output to match the characteristics of different hardware configurations, allowing the same model to operate accurately across multiple device types without retraining
Solution Approach 2:
The patent modifies operational parameters by maintaining a calibration database with device-specific parameters (such as sensor characteristics, processing capabilities, and firmware versions). The system dynamically adjusts model inputs and outputs by applying conversion rules based on these parameters, enabling the model to adapt to different hardware configurations while preserving output accuracy
2Adaptability or versatility
If hardware components and firmware are varied across devices, then device diversity and user choice increase, but the model becomes incompatible or produces inaccurate output
Solution Approach 1:
The patent creates a universal calibration framework that can handle multiple hardware configurations and firmware versions through a single unified system. The calibration database stores parameters for various device types, and the conversion rules apply universally across different hardware platforms, allowing one machine-learning model to function reliably on diverse devices
3Measurement precision
If the model is reconfigured for each unique device, then output accuracy is maintained, but the configuration process becomes time-consuming and complex
Solution Approach 1:
The patent performs calibration actions in advance by pre-storing device-specific parameters and conversion rules in a calibration database during device manufacturing or initial setup. When deploying the model to a new device, the system automatically retrieves the pre-prepared calibration data and applies it, eliminating the need for time-consuming real-time configuration while maintaining output accuracy
Data Source
AI summary
A method for automatically calibrating an assessment model for a user device may include accessing an automated conversion engine and a first assessment model trained to determine a target assessment result using a set of eye-tracking features data captured at a first sampling rate. Using the automated conversion engine, the set of eye-tracking features data may be input into a time series, the set of eye-tracking features data may be generated at a second sampling rate based on one or more components or processors of the user device, and a calibrated assessment model specific to the user device may be formed using the set of eye-tracking features data at the second sampling rate.