Tunable Lens Focus Control Using LiDAR and Gaze Prediction
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Solution Overview
Problem
Existing tunable lenses in optometry devices suffer from limitations in depth resolution and accuracy in distance estimation, trailing the user's fixation point, and do not consider scene context for tuning.
Innovation Solution
A computer-implemented method using a LiDAR sensor for precise distance measurement and an RGB camera for scene evaluation, combined with machine learning algorithms to identify and predict user gaze, enables accurate and natural tuning of tunable lenses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods for distance estimation are used, then the system can determine distance, but the depth resolution and accuracy are insufficient
Solution Approach 1:
The patent combines multiple sensing modalities (optical sensors, depth sensors, eye tracking) to achieve accurate distance estimation and depth resolution. By merging data from different sensor types and processing methods (pupil tracking, vergence angle calculation, scene context analysis), the system overcomes the limitations of individual methods and achieves both high accuracy and reliability in distance measurement.
2Speed
If traditional tunable lens tuning is used, then the lens can be adjusted, but it trails the user's fixation point
Solution Approach 1:
The patent implements predictive tuning by analyzing eye movement patterns, gaze direction, and scene context to anticipate the user's next fixation point before it occurs. The system calculates predicted gaze positions and pre-adjusts the tunable lens accordingly, eliminating the trailing effect and ensuring the focus is ready before the user actually looks at the target.
Solution Approach 2:
The system continuously monitors eye tracking data, pupil positions, and gaze patterns to provide real-time feedback for lens adjustment. This closed-loop feedback mechanism allows the system to dynamically track and respond to user fixation points with minimal delay, maintaining synchronous focus adjustment with user intent.
3Adaptability or versatility
If basic distance measurement is implemented, then the system can focus, but it does not consider scene context
Solution Approach 1:
The patent implements a multi-functional system where the same sensor array and processing unit serve multiple purposes: distance measurement, depth estimation, eye tracking, gaze prediction, and scene context analysis. By making the system universal in its capabilities, it achieves high adaptability to different scenes and user behaviors without proportionally increasing hardware complexity.
Solution Approach 2:
The patent introduces scene context as an intermediary layer that mediates between raw sensor data and lens control decisions. The system analyzes contextual information about the environment, object distances, and user interaction patterns to inform tuning decisions, creating a intelligent intermediary processing layer that enhances adaptability without requiring complex hardware modifications.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy and speed of focus adjustment, providing improved comfort and natural focus adaptation to changing scenes.
Implementation Method 1
An active rangefinder may emit optical radiation from a transmitter directed at the object. The optical radiation may then be reflected off the object. The reflected optical radiation may then be received with an appropriate receiver.
Implementation Method 2
The received optical radiation may then be processed by appropriate circuitry to determine a distance to the object.
Data Source
AI summary
A computer-implemented method for operating an optometry device includes the following steps: generating a first data set including information about a distance of a user of the optometry device to an object and tuning a tunable lens of the optometry device based on the distance of the user of the optometry device to the object based on the first data set. The first data set is generated by using a LiDAR sensor, which measures the distance of the user of the optometry device to the object by evaluating a scene represented by the first data set.