How AI Automatically Labels Images and Checks Its Own Work
A method for training AI to label images by calculating how uncertain it is about its own predictions, allowing it to verify its accuracy without human help.
Original patent title: “Methods for training auto labeling device and performing auto labeling related to object detection while performing automatic verification by using uncertainty scores and devices using the same”
A method for training AI to label images by calculating how uncertain it is about its own predictions, allowing it to verify its accuracy without human help. Granted to Superb AI Co Ltd in 2021 with 28 claims and 1 forward citation.
Key facts
Coverage
What does this patent actually cover?
This patent describes a system that trains an AI to label objects in photos (like drawing boxes around cars or pedestrians) and then double-check its own accuracy. It uses two different 'classifiers'—essentially two different ways of looking at the image data—to generate scores for the objects it finds. Crucially, the system calculates an 'uncertainty score' for these predictions. By comparing the results from these two classifiers, the system can estimate how confident it is in its labels, allowing it to automatically flag or refine labels that it is unsure about, rather than just guessing.
The gap
What does this patent NOT cover?
- Does not cover manual image labeling performed by human workers.
- Does not cover object detection systems that lack an uncertainty-based verification mechanism.
- Does not cover the specific hardware used to run the neural networks.
- Does not cover non-image data types like raw audio or text streams.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
The system uses 'randomly-zeroed' copies of feature maps—a form of dropout—to force the AI to generate uncertainty scores, effectively turning the AI's internal confusion into a measurable metric for quality control.
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Autonomous vehicle sensor data annotation
Automated quality control in manufacturing vision systems
Large-scale medical imaging analysis pipelines
Why it matters
The bigger picture
Training AI for computer vision usually requires massive amounts of human-labeled data, which is expensive and slow. This patent provides a path to 'auto-labeling,' where the AI does the heavy lifting while maintaining a self-verification loop. It is a key building block for companies trying to scale computer vision models for autonomous vehicles or robotics without needing an army of human data annotators.
Filed
December 1, 2020
Granted
June 1, 2021
Market context
Who's building on this
Companies in this space
Superb AI, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is actively commercializing this through their data labeling platform. Other major players in the MLOps and data-centric AI space, such as Scale AI or Labelbox, are building similar automated verification pipelines to reduce human-in-the-loop dependencies.
Market impact
This technology supports the shift toward 'data-centric AI,' where the focus is on improving the quality of training data rather than just tweaking model architecture. It helps companies reduce the cost of creating high-quality datasets, which is a primary bottleneck in deploying computer vision in real-world environments like self-driving cars.
Claim 1 — Plain English
What this patent covers
This patent describes a system that trains an AI to label objects in photos (like drawing boxes around cars or pedestrians) and then double-check its own accuracy. It uses two different 'classifiers'—essentially two different ways of looking at the image data—to generate scores for the objects it finds. Crucially, the system calculates an 'uncertainty score' for these predictions. By comparing the results from these two classifiers, the system can estimate how confident it is in its labels, allowing it to automatically flag or refine labels that it is unsure about, rather than just guessing.
The clever bit
The system uses 'randomly-zeroed' copies of feature maps—a form of dropout—to force the AI to generate uncertainty scores, effectively turning the AI's internal confusion into a measurable metric for quality control.
What it does not cover
- Does not cover manual image labeling performed by human workers.
- Does not cover object detection systems that lack an uncertainty-based verification mechanism.
- Does not cover the specific hardware used to run the neural networks.
- Does not cover non-image data types like raw audio or text streams.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
PatentBrief Score
Impact Score
Early stage
Citation count
6/40
Early citations
Claim breadth
19/20
Very broad protection
Recency
10/20
Granted 5–10 years ago
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$94K – $300K
Midpoint $187K · 14.5 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
The original legal language
Original claims
28 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
KIM, K. (2021). How AI Automatically Labels Images and Checks Its Own Work (U.S. Patent No. 11,023,780). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/11023780/siri-on-device-speech-recognition
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does How AI Automatically Labels Images and Checks Its Own Work cover?
A method for training AI to label images by calculating how uncertain it is about its own predictions, allowing it to verify its accuracy without human help.
Who owns patent US 11023780?
Superb AI Co Ltd owns this patent, granted in 2021.
When does this patent expire?
This patent is expected to expire on June 1, 2041, when the invention enters the public domain.
What is patent US 11023780 cited by?
This patent has been cited by 1 later patents that build on its ideas.
What problem does this patent solve?
Training AI for computer vision usually requires massive amounts of human-labeled data, which is expensive and slow. This patent provides a path to 'auto-labeling,' where the AI does the heavy lifting while maintaining a self-verification loop. It is a key building block for companies trying to scale computer vision models for autonomous vehicles or robotics without needing an army of human data annotators.
What does this patent NOT cover?
Does not cover manual image labeling performed by human workers.
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