AI Apparatus for Automated Training Data Labeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The generation of training data for artificial intelligence models requires significant human resources and is inefficient, as it relies on manual labeling of data, which is time-consuming and labor-intensive.
Innovation Solution
An AI apparatus and server system that automatically generates training data by analyzing sensor data from real environments, determining relevance and usefulness, calculating uncertainty, and extracting labels with high confidence levels, thereby reducing the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to generate training data, then labeling accuracy can be ensured, but human resources and time consumption increase significantly
Solution Approach 1:
The system enables automatic self-labeling of sensor data through AI models. The labeling process is performed autonomously by the system itself using pre-trained models to generate labels for training data, eliminating the need for manual human intervention while maintaining efficiency.
Solution Approach 2:
The patent replaces the mechanical manual labeling process with an automated AI-based labeling system. Pre-trained AI models are used to automatically generate labels from sensor data, substituting human manual work with computational processes that are faster and more scalable.
2Measurement precision
If manual labeling is used to generate training data, then label quality can be controlled, but human resources requirements increase
Solution Approach 1:
The system performs automatic self-labeling using AI models, enabling the system to generate training data labels autonomously without requiring human resources. This dramatically improves productivity by eliminating manual labeling work while maintaining label quality through model-based validation.
Solution Approach 2:
Manual human labeling operations are replaced with automated AI model-based labeling. The system uses pre-trained models to generate labels automatically, substituting human labor with computational processes that can handle large volumes of data efficiently.
3Quantity of substance
If all sensor data is stored for training, then comprehensive training data is available, but storage capacity requirements increase
Solution Approach 1:
The system extracts only the essential and useful components from sensor data for storage. By identifying and extracting relevant features and labels that are most valuable for training, the system avoids storing redundant or irrelevant data, thereby reducing storage requirements while maintaining training data quality.
Solution Approach 2:
The system applies different quality standards to different portions of data. High-quality labeled data that is most useful for training is stored in detail, while less critical data is either summarized, compressed, or discarded. This selective approach optimizes storage efficiency while preserving training effectiveness.
Data Source
AI summary
An artificial intelligence apparatus for generating training data includes a memory configured to store a target artificial intelligence model, and a processor configured to receive sensor data, determine whether the received sensor data is irrelevant to a learning of the target artificial intelligence model, determine whether the received sensor data is useful for the learning if the received sensor data is determined to be relevant to the learning, extract a label from the received sensor data by using a label extractor if the received sensor data is determined to be useful for the learning, determine a confidence level of the extracted label, and generate training data including the received sensor data and the extracted label if the determined confidence level exceeds a first reference value.