18189962. IDENTIFYING QUALITY OF LABELED DATA simplified abstract (GM Cruise Holdings LLC)
Contents
IDENTIFYING QUALITY OF LABELED DATA
Organization Name
Inventor(s)
Russell Brennan of Kirkland WA (US)
Wenjie Zhou of San Jose CA (US)
IDENTIFYING QUALITY OF LABELED DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 18189962 titled 'IDENTIFYING QUALITY OF LABELED DATA
Simplified Explanation: The patent application discusses a technology that identifies the quality of labeled data by comparing it to ground truth labeled data.
Key Features and Innovation:
- Accessing labeled data at a specific granularity level
- Sampling data at a lower granularity level to generate ground truth labeled data
- Comparing labeled data to ground truth data to determine labeling quality metric
- Performing relabeling based on the quality metric
Potential Applications: This technology can be applied in various fields such as machine learning, data analysis, and quality control processes.
Problems Solved: The technology addresses the challenge of assessing the quality of labeled data accurately and efficiently.
Benefits:
- Improved accuracy in labeling data
- Enhanced quality control processes
- Increased efficiency in data analysis tasks
Commercial Applications: The technology can be utilized in industries such as healthcare, finance, and e-commerce for data labeling and quality assessment purposes.
Prior Art: Readers can explore prior research on data labeling, quality assessment, and machine learning algorithms to understand the background of this technology.
Frequently Updated Research: Stay updated on advancements in data labeling techniques, quality assessment methods, and machine learning algorithms to enhance the application of this technology.
Questions about the Technology: 1. How does this technology improve the accuracy of labeled data? 2. What are the potential implications of using this technology in data analysis processes?
Original Abstract Submitted
Aspects of the subject technology relate to systems, methods, and computer-readable media for identifying a quality of labeled data. Labeled data of a data set that exists at a specific granularity level can be accessed. The labeled data can be sampled on a lower granularity level relative to the specific granularity level of the data set to generate sampled data of the data set. The sampled data can be labeled to generate ground truth labeled data of the data set. The labeled data can be compared to the ground truth labeled data to identify a labeling quality metric of the labeled data. Relabeling of the data set can be performed based on the labeling quality metric.
