Automated Labeling via Biometric Feedback in Virtual Reality
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies face challenges in efficiently and accurately labeling large datasets for machine learning models, particularly in fields like medicine and engineering, where deep domain expertise is required and manual labeling is time-consuming and costly.
Innovation Solution
A computer-implemented method and system that utilize virtual or augmented reality environments to capture a user's reflexive biometric data, such as brain waves, to determine whether injected labels accurately describe images, with the data being used to train machine learning algorithms in a more efficient and accurate manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling by domain experts is used, then labeling accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The patent replaces manual mechanical labeling processes with an automated system that uses virtual reality environments and biometric data capture. The system automatically determines label accuracy by analyzing user responses and biometric signals, eliminating the need for time-consuming manual review by domain experts while maintaining high labeling accuracy.
Solution Approach 2:
The system enables users to participate in the labeling process through immersive virtual reality experiences where they naturally interact with content. The biometric data capture system automatically records user responses and physiological signals, allowing the system to self-evaluate label accuracy without requiring external expert verification, thus reducing time consumption while maintaining quality.
2Measurement precision
If manual labeling by domain experts is used, then labeling accuracy is improved, but cost increases
Solution Approach 1:
The patent replaces expensive manual labor by domain experts with an automated computational system. The system uses virtual reality platforms, biometric sensors, and machine learning algorithms to automatically evaluate label accuracy, significantly reducing operational costs while maintaining or improving labeling quality through objective biometric measurement.
Solution Approach 2:
The system changes the evaluation parameters from subjective expert judgment to objective biometric measurements. By capturing physiological responses such as eye movements, pupil dilation, and brain wave patterns, the system creates measurable, quantifiable metrics for label accuracy that eliminate the need for expensive expert time while maintaining consistent, repeatable evaluation standards.
3Productivity
If automated labeling systems are used, then time consumption is reduced, but labeling accuracy deteriorates
Solution Approach 1:
The patent introduces biometric data as an intermediary between the automated system and the labeling task. The system captures objective physiological responses from users interacting with virtual reality content, using these biometric signals as a mediator to evaluate label accuracy. This intermediary mechanism allows automated processing to achieve both high speed and high accuracy by relying on objective physiological measurements rather than subjective algorithms alone.
Solution Approach 2:
The system implements continuous feedback loops where user interactions with the virtual reality content and biometric responses are constantly monitored and fed back into the labeling system. This feedback mechanism allows the system to adjust and refine its labeling decisions in real-time, ensuring high accuracy while maintaining rapid processing speeds through iterative optimization based on actual user behavior data.
Data Source
AI summary
A computer-implemented method identifies, using a processor set, a time to inject an image and a label within a virtual reality environment or an augmented reality environment. The processor set may then inject the image and the label within the virtual reality environment or the augmented reality environment at the identified time. The processor set captures a user's response to the injected label and the injected image and determines whether the injected label accurately describes the injected image, based on the user's captured response to the injected label and the injected image. The processor set may then write the determination whether the label accurately describes the injected image to a memory, based on a degree of statistical significance of the user's response exceeding a threshold.