{
  "patent_number": "US 12738026",
  "country": "US",
  "title": "AI That Finds Partially Hidden Objects in Images",
  "original_title": "Systems, methods, and computer programs for using a network of machine learning models to detect an image depicting an object of interest which can be partially occluded by another object",
  "summary": "This patent describes an AI system that uses multiple machine learning models to accurately identify objects in an image, even when parts of those objects are blocked by something else.",
  "what_it_does": "This patent details a system for detecting objects in images, even when they are partially hidden. It starts by taking an input image and extracting 'object-based features' and 'context-based features'. These features are then processed by a deep convolutional neural network (DCNN) model. The DCNN's output is sent to two specialized models: an 'occlusion model' and a 'generative compositional model'. The occlusion model specifically identifies areas where an object is blocked by another object. The generative compositional model looks for different parts or components of objects that might be present. By combining the information from both these models, the system creates a 'likelihood map' to show if a particular object is depicted in the image, even with parts missing. For example, if a car is mostly hidden behind a tree, this system could still identify it by recognizing visible parts and understanding the context of the occlusion.",
  "what_it_does_not_cover": [
    "Does not cover detecting objects that are completely hidden and have no visible parts or context clues.",
    "Does not cover object detection methods that do not use a deep convolutional neural network (DCNN).",
    "Does not cover systems that only use a single model for object detection without separate occlusion and compositional analysis.",
    "Does not cover general image classification where the goal is to label the entire image, rather than locate specific objects.",
    "Does not cover object detection that relies solely on traditional computer vision techniques without machine learning models."
  ],
  "filed": "2022-06-14",
  "granted": "2026-09-15",
  "expires": null,
  "status": "active",
  "holder": null,
  "holder_url": null,
  "inventors": [],
  "times_cited": 0,
  "tags": [
    "ai_ml",
    "software",
    "automotive",
    "telecommunications",
    "consumer_electronics"
  ],
  "abstract": "Methods, systems, and apparatus, including computer programs encoded on computer-storage media, used for object detection in an input image that can include at least partial object occlusion. In some implementations, input data representing the image depicting an object can include object-based features and context-based features used for object detection. The feature is processed by a deep convolutional neural network (DCNN) model. First feature data generated by the DCNN is provided to an occlusion model and a generative compositional model. The occlusion model can detect locations where an object depicted in the image is occluded by an object of any other type. The generative compositional model detects the presence of different classes of objects that represent parts or partial components of object depicted in the image. The output of the compositional model and occlusion model is a likelihood map that shows if an object is depicted in the input image.",
  "url": "https://patentbrief.org/patent/us/12738026/systems-methods-and-computer-programs-for-using-a-network-of-machine-learning",
  "markdown_url": "https://patentbrief.org/patent/us/12738026/systems-methods-and-computer-programs-for-using-a-network-of-machine-learning/md",
  "google_patents_url": "https://patents.google.com/patent/US12738026",
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}