# 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. - **Patent:** US 11023780 - **Original 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 - **Owner:** Superb AI Co Ltd - **Granted:** 2021 - **Status:** Active - **Times cited:** 1 - **Field:** ai_ml, consumer_electronics, automotive ## What it does 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. ## 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. ## 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. ## Real-world examples 1. Autonomous vehicle sensor data annotation 2. Automated quality control in manufacturing vision systems 3. Large-scale medical imaging analysis pipelines ## Why it matters 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. ## 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. **Full plain-English explainer:** https://patentbrief.org/patent/us/11023780/siri-on-device-speech-recognition **Original patent:** https://patents.google.com/patent/US11023780 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [Training AI Models Together with Unlabeled Data Using a Teacher](https://patentbrief.org/patent/us/20220012637/federated-teacher-student-machine-learning) — This patent describes a way for multiple AI systems to learn together from data that hasn't been manually labeled, using a 'teacher' AI to create temporary labels for a 'student' AI. - [Automated AI for Adapting to New Data Without Retraining](https://patentbrief.org/patent/us/20230162023/system-and-method-for-automated-transfer-learning-with-domain-disentanglement) — This patent describes an automated system that builds artificial intelligence models capable of adapting to new, different data without needing full retraining, by learning to ignore irrelevant changes. - [Making AI Smarter by Focusing on Unsure 'Nodes'](https://patentbrief.org/patent/us/12423586/training-nodes-of-a-neural-network-to-be-decisive) — This 2025 patent from D5AI LLC describes a way to train AI models more effectively by boosting the learning signal for 'nodes' that aren't making clear decisions on data. - [How to Force AI to Follow Logical Rules During Training](https://patentbrief.org/patent/us/11651227/muzero) — A system that uses a dual-headed neural network to ensure AI models obey specific logical rules by embedding those rules directly into the training process. - [How to Update AI on Small Devices with Slow Internet](https://patentbrief.org/patent/us/20250363357/systems-and-methods-for-deploying-and-updating-neural-networks-at-the-edge-of-a-) — This patent describes a method for efficiently updating artificial intelligence models on small, internet-connected devices, like smart cameras, by sending only the changes, or 'patches,' instead of the entire updated model, which saves bandwidth.