{
  "patent_number": "US 12154036",
  "country": "US",
  "title": "How an AI Learns to Recognize Objects by Generating Its Own Examples",
  "original_title": "Enhanced generative adversarial network and target sample recognition method",
  "summary": "This patent describes an enhanced artificial intelligence system that uses two competing networks, a generator and a discriminator, with special 'capsule layers' to create and recognize specific types of data, like images, even with limited initial examples.",
  "what_it_does": "This patent details an 'enhanced generative adversarial network' (GAN) and a method for recognizing specific 'target samples.' The system includes at least one 'enhanced generator' that creates new data from initial data, and at least one 'enhanced discriminator' that evaluates this generated data. The discriminator then provides feedback to the generator, helping it improve. A key feature of this enhanced discriminator, as described in claim 1, is its structure: a 'convolution layer,' a 'basic capsule layer,' a 'convolution capsule layer,' and a 'classification capsule layer,' all connected in sequence. The number and setup of these layers can be adjusted based on the specific features of the 'target sample' being recognized. For example, to recognize a rare type of medical image, the system could generate many synthetic versions of that image, and the discriminator, with its capsule layers, would learn to distinguish them from other images, even if only a few real examples exist.",
  "what_it_does_not_cover": [
    "Does not cover generative adversarial networks that do not include capsule layers within their discriminator component.",
    "Does not cover target sample recognition methods that do not use an enhanced generative adversarial network with the specified discriminator architecture.",
    "Does not cover GANs where the discriminator's convolution, basic capsule, convolution capsule, and classification capsule layers are not sequentially connected.",
    "Does not cover systems where the quantities and structure parameters of the discriminator's specific layers are not set according to the target sample's features."
  ],
  "filed": "2020-08-21",
  "granted": "2024-11-26",
  "expires": "2040-08-21",
  "status": "active",
  "holder": "Shenzhen Institutes of Advanced Technology",
  "holder_url": "https://patentbrief.org/company/shenzhen-institutes-of-advanced-technology",
  "inventors": [
    {
      "name": "Wenyong Zhang",
      "url": "https://patentbrief.org/inventor/wenyong-zhang"
    },
    {
      "name": "Yanyan SHEN",
      "url": "https://patentbrief.org/inventor/yanyan-shen"
    },
    {
      "name": "Shuqiang Wang",
      "url": "https://patentbrief.org/inventor/shuqiang-wang"
    }
  ],
  "times_cited": 1,
  "tags": [
    "ai_ml",
    "software",
    "telecommunications",
    "consumer_electronics"
  ],
  "abstract": "The present disclosure relates to an enhanced generative adversarial network and a target sample recognition method. The enhanced generative adversarial network in the present disclosure includes at least one enhanced generator and at least one enhanced discriminator, where the enhanced generator obtains generated data by processing initial data, and provides the generated data to the enhanced discriminator; the enhanced discriminator processes the generated data and feeds back a classification result to the enhanced generator; the enhanced discriminator includes: a convolution layer, a basic capsule layer, a convolution capsule layer, and a classification capsule layer, and the convolution layer, the basic capsule layer, the convolution capsule layer, and the classification capsule layer are sequentially connected to each other.",
  "url": "https://patentbrief.org/patent/us/12154036/enhanced-generative-adversarial-network-and-target-sample-recognition-method",
  "markdown_url": "https://patentbrief.org/patent/us/12154036/enhanced-generative-adversarial-network-and-target-sample-recognition-method/md",
  "google_patents_url": "https://patents.google.com/patent/US12154036",
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}