{
  "patent_number": "US 12574477",
  "country": "US",
  "title": "Training AI Models Across Different Computers",
  "original_title": "Distributed deep learning using a distributed deep neural network",
  "summary": "This 2026 patent describes a way to train AI models on one computer, send a version to another computer for further training with private data, and then update the original model with the improvements.",
  "what_it_does": "This patent details a method for distributed AI training. A central computer (first host system) trains an initial neural network using data that has already been filtered from multiple remote sources. This initial network is then sent to a remote computer (second host system). The remote computer further trains this network using its own private data, creating a customized version. This customized network is then installed and used on the remote computer to process its live data stream. Crucially, the remote computer sends updated coefficients back to the central computer, allowing the original network to be improved based on the private data insights gained remotely. For example, a central server could train a general security camera AI, send it to individual homes, where each home's camera further trains it on local activity, and then sends back anonymized updates to improve the central AI.",
  "what_it_does_not_cover": [
    "Training that only occurs on a single computer system.",
    "Using a neural network that is not further trained on private, local data at the second host system.",
    "Sending raw, unfiltered event data from the remote systems to the first host system.",
    "Updating the central neural network without receiving updated coefficients from a remote system.",
    "Training that does not involve evaluating the neural network at both the first and second host systems."
  ],
  "filed": "2017-04-19",
  "granted": "2026-03-10",
  "expires": "2037-04-19",
  "status": "active",
  "holder": "Deep Sentinel",
  "holder_url": "https://patentbrief.org/company/deep-sentinel",
  "inventors": [
    {
      "name": "Chaoying Chen",
      "url": "https://patentbrief.org/inventor/chaoying-chen"
    },
    {
      "name": "Ching-Wa Yip",
      "url": "https://patentbrief.org/inventor/ching-wa-yip"
    },
    {
      "name": "David Lee Selinger",
      "url": "https://patentbrief.org/inventor/david-lee-selinger"
    }
  ],
  "times_cited": 0,
  "tags": [
    "consumer_electronics",
    "software",
    "telecommunications",
    "ai_ml"
  ],
  "abstract": "Apparatus and associated methods relate to training a neural network on a first host system, sending the neural network to a second host system, training the neural network by the second host system based on data private to the second host system, and employing the neural network to filter events sent to the first host system. In an illustrative example, the first host system may be a server having a central repository including trained neural networks and historical data, and the second host system may be a remote server having a data source private to the remote server. The private remote data source may be a camera. In some examples, events may be filtered as a function of a prediction of error in the neural network. Various examples may advantageously provide remote intelligent filtering. For example, remote data may remain private while adaptively filtering events to the central server.",
  "url": "https://patentbrief.org/patent/us/12574477/distributed-deep-learning-using-a-distributed-deep-neural-network",
  "markdown_url": "https://patentbrief.org/patent/us/12574477/distributed-deep-learning-using-a-distributed-deep-neural-network/md",
  "google_patents_url": "https://patents.google.com/patent/US12574477",
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}