{
  "patent_number": "US 11023780",
  "country": "US",
  "title": "How AI Automatically Labels Images and Checks Its Own Work",
  "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",
  "summary": "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.",
  "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."
  ],
  "filed": "2020-12-01",
  "granted": "2021-06-01",
  "expires": null,
  "status": "active",
  "holder": "Superb AI Co Ltd",
  "holder_url": "https://patentbrief.org/company/superb-ai-co-ltd",
  "inventors": [
    {
      "name": "Kye-hyeon KIM",
      "url": "https://patentbrief.org/inventor/kye-hyeon-kim"
    }
  ],
  "times_cited": 1,
  "tags": [
    "ai_ml",
    "consumer_electronics",
    "automotive"
  ],
  "abstract": "A method for training an auto labeling device capable of performing automatic verification by using uncertainty scores of labels is provided. The method includes steps of: a learning device (a) inputting unlabeled training images into a trained object detection network and a trained convolution network to generate bounding boxes for training and feature maps for training; and (b) (i) instructing an ROI pooling layer to generate pooled feature maps for training, (ii) at least one of (ii-1) inputting the pooled feature maps for training into a first classifier to generate first class scores for training and first box uncertainty scores for training, and (ii-2) inputting the pooled feature maps for training into a second classifier to generate second class scores for training and second box uncertainty scores for training, and (iii) training one of the first classifier using first class losses and the second classifier using second class losses.",
  "url": "https://patentbrief.org/patent/us/11023780/siri-on-device-speech-recognition",
  "markdown_url": "https://patentbrief.org/patent/us/11023780/siri-on-device-speech-recognition/md",
  "google_patents_url": "https://patents.google.com/patent/US11023780",
  "relatedPatents": []
}