Synthetic Ground Truth Regeneration With Threshold Feedback
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Solution Overview
Problem
Conventional methods for creating synthetic ground truth data face limitations in quality and scalability, and AI-generated data lacks quality assurance, making manual control impractical.
Innovation Solution
A method and apparatus that utilize a data generator to evaluate synthetic ground truth data against a performance threshold, generate anew if the threshold is not met, and replace substandard data, ensuring only high-quality data is used for training, with automated processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI models are used to create ground truth data from existing ground truth target data, then data diversity and scale are improved, but data quality assurance deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the performance of the data generator is evaluated by comparing generated ground truth data samples against performance threshold values. When the threshold is not met, the system generates new samples and replaces substandard ones, creating a closed-loop quality control system that continuously improves data quality while maintaining AI-generated diversity
Solution Approach 2:
The system changes parameters by introducing performance threshold values and iteratively adjusting the data generation process. By evaluating and regenerating data samples based on these thresholds, the system transforms the quality assurance approach from static to dynamic, maintaining reliability while preserving the scalability benefits of AI generation
2Reliability
If manual quality control is applied to AI-generated data, then data quality is improved, but processing efficiency deteriorates
Solution Approach 1:
The system implements self-service quality control where the data generator automatically evaluates its own output against performance thresholds and performs self-correction by regenerating and replacing substandard samples. This automated self-service mechanism eliminates the need for manual quality control while maintaining high data quality standards
Solution Approach 2:
The patent replaces manual mechanical quality control processes with an automated computational system that uses performance threshold comparisons and algorithmic regeneration. This substitution maintains data quality while dramatically improving processing efficiency by eliminating human intervention in the quality control loop
3Manufacturing precision
If traditional ray tracing or rasterizing methods are used to generate synthetic data, then data quality is improved, but scalability deteriorates
Solution Approach 1:
The patent uses AI models to copy and generalize from existing ground truth target data to create new synthetic ground truth data samples. This copying approach with performance threshold evaluation maintains quality characteristics of traditional methods while achieving the scalability needed for large-volume data generation required by modern AI development
Data Source
AI summary
Method and apparatus for improving synthetic ground truth data by means of a data generator and for training a target machine learning model. The method includes: providing ground truth data samples which relate to ground truth source data and are synthetically generated by the data generator; comparing a performance of the data generator for the provided ground truth data samples with a performance threshold value; generating anew ground truth data samples for the same ground truth source data by means of the data generator if the performance threshold value for the provided ground truth data samples is not achieved; replacing the ground truth data samples for which the performance threshold value is not achieved with the newly generated ground truth data samples; and training the target machine learning model on the basis of the replaced and provided ground truth data samples.