{
  "patent_number": "US 12141700",
  "country": "US",
  "title": "How AI Generates Images Based on Style and Content Cues",
  "original_title": "Generative adversarial network for generating images",
  "summary": "This patent describes an artificial intelligence system that creates new images by understanding both what the image should show (like a cat) and how it should look (like a high-quality photo), using a special internal layer to blend these instructions.",
  "what_it_does": "This patent details a generative adversarial network (GAN) that creates synthetic images. It uses two main parts: a generator neural network and a discriminator neural network. The generator takes a random 'noise vector' and a pair of 'conditioning variables' as input (Claim 1). One variable describes 'semantic information,' like what objects are in the image, and the other describes 'aesthetic information,' such as its quality or style (Claim 1). A key part of the generator is a 'mixed-conditional batch normalization layer' which normalizes the data flow within the network (Abstract, Claim 1). This layer uses the semantic and aesthetic conditioning variables to adjust its internal parameters, allowing it to precisely control how the generated image reflects both content and style (Abstract, Claim 1). For example, you could ask it to generate a 'dog' (semantic) that looks 'beautiful' (aesthetic).",
  "what_it_does_not_cover": [
    "Does not cover image generation systems that only use semantic information without also incorporating aesthetic information as a distinct conditioning variable.",
    "Does not cover GANs that generate images without using a 'mixed-conditional batch normalization layer' that specifically adjusts its parameters based on both semantic and aesthetic inputs.",
    "Does not cover image generation where the conditioning variables are not applied via an 'affine transformation' to compute the normalization layer's parameters.",
    "Does not cover image generation where the aesthetic information is not provided as 'continuous information in the form of histogram score distributions' (Claim 1)."
  ],
  "filed": "2020-09-17",
  "granted": "2024-11-12",
  "expires": "2040-09-17",
  "status": "active",
  "holder": "Naver",
  "holder_url": "https://patentbrief.org/company/naver",
  "inventors": [
    {
      "name": "Naila Murray",
      "url": "https://patentbrief.org/inventor/naila-murray"
    }
  ],
  "times_cited": 3,
  "tags": [
    "ai_ml",
    "software",
    "consumer_electronics",
    "telecommunications"
  ],
  "abstract": "A generative adversarial network, a method of training a generator neural network, and a method of generating images using the generator network is provided. The generator neural network is configured to process an input comprising a noise vector and a pair of conditioning variables to generate an image according to the conditioning variables. The generator neural network includes a mixed-conditional batch normalization layer. The mixed-conditional batch normalization layer is configured to normalize a network layer output to generate a normalized network layer output, comprising transforming the network layer output in accordance with mixed-conditional batch normalization layer parameters to generate the normalized network layer output, wherein the mixed-conditional batch normalization layer parameters are computed by applying an affine transformation to the conditioning variables.",
  "url": "https://patentbrief.org/patent/us/12141700/generative-adversarial-network-for-generating-images",
  "markdown_url": "https://patentbrief.org/patent/us/12141700/generative-adversarial-network-for-generating-images/md",
  "google_patents_url": "https://patents.google.com/patent/US12141700",
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}