{
  "patent_number": "US 20260197424",
  "country": "US",
  "title": "How Distributed AI Models Learn Privately on Edge Devices",
  "original_title": "Distributed deep learning using a distributed deep neural network",
  "summary": "This patent describes how a central computer can train a basic artificial intelligence model, send it to a remote device, and let that device customize the model using its own private data, then use it to filter information before sending it back.",
  "what_it_does": "This patent details a system where a central computer, called a \"first computing system,\" first trains a neural network using general data collected from many remote devices (claim 1). Once this initial training is done, the central system sends this partially trained neural network to a specific remote device, a \"second computing system.\" This remote device then takes the neural network and further trains it using its own \"private data\" that isn't shared with the central system (claim 1). For example, a security camera (the second computing system) could receive a general object detection model, then train it specifically on its own footage of common local events, like a delivery person, without sending that private footage back to the central server. The customized neural network then processes \"live event data\" directly on the remote device, filtering out less important events before any data is sent back to the central system (claim 1, claim 4).",
  "what_it_does_not_cover": [
    "Does not cover a system where all neural network training, including customization, happens only on a single central server.",
    "Does not cover scenarios where the remote device sends all its raw private data back to the central system for training.",
    "Does not cover a system where the customized neural network is not applied to filter live event data at the remote device.",
    "Does not cover a system where the central system does not initially train a neural network before sending it to the remote system.",
    "Does not cover a system where the remote device's private data is not used to further train and customize the neural network."
  ],
  "filed": "2026-03-05",
  "granted": null,
  "expires": "2046-03-05",
  "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": [
    "ai_ml",
    "telecommunications",
    "consumer_electronics",
    "software",
    "cybersecurity"
  ],
  "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/20260197424/distributed-deep-learning-using-a-distributed-deep-neural-network-20260197424",
  "markdown_url": "https://patentbrief.org/patent/us/20260197424/distributed-deep-learning-using-a-distributed-deep-neural-network-20260197424/md",
  "google_patents_url": "https://patents.google.com/patent/US20260197424",
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  ]
}