{
  "patent_number": "US 12518214",
  "country": "US",
  "title": "Training AI on Private Data Without Seeing It",
  "original_title": "Distributed machine learning systems including generation of synthetic data",
  "summary": "This patent describes a way to train artificial intelligence models using private data stored on many separate computers, by generating fake data that mimics the real data's patterns, so the private data itself never leaves its original location.",
  "what_it_does": "This patent outlines a system for distributed machine learning where private data stays put. Imagine many computers, each holding sensitive information like patient health records. A central system sends a 'task' definition to these private computers. Each private computer's 'modeling agent' uses its local private data to create synthetic, or fake, data that mimics the real data's patterns. It then trains a 'proxy model' on this synthetic data. The system then collects this proxy model data from multiple private servers. If the data from different servers looks similar in shape or properties, it's combined into a 'global model.' If the data looks different, it might signal a problem with the original private data, like corruption or missing information.",
  "what_it_does_not_cover": [
    "Systems where private data is de-identified or exposed to unauthorized systems.",
    "Systems that directly transmit the original local private data to a non-private server.",
    "Training AI models solely on synthetic data that does not originate from private data distributions.",
    "Systems where the proxy model data is not compared between different private data servers.",
    "Aggregating models without first generating synthetic data based on private data distributions."
  ],
  "filed": "2023-04-21",
  "granted": "2026-01-06",
  "expires": "2043-04-21",
  "status": "active",
  "holder": "Nant Holdings IP",
  "holder_url": "https://patentbrief.org/company/nant-holdings-ip",
  "inventors": [
    {
      "name": "Christopher W. Szeto",
      "url": "https://patentbrief.org/inventor/christopher-w-szeto"
    },
    {
      "name": "Stephen Charles Benz",
      "url": "https://patentbrief.org/inventor/stephen-charles-benz"
    },
    {
      "name": "Nicholas J. Witchey",
      "url": "https://patentbrief.org/inventor/nicholas-j-witchey"
    }
  ],
  "times_cited": 0,
  "tags": [
    "software",
    "ai_ml",
    "telecommunications",
    "finance",
    "biotech",
    "healthcare"
  ],
  "abstract": "A distributed, online machine learning system is presented. Contemplated systems include many private data servers, each having local private data. Researchers can request that relevant private data servers train implementations of machine learning algorithms on their local private data without requiring de-identification of the private data or without exposing the private data to unauthorized computing systems. The private data servers also generate synthetic or proxy data according to the data distributions of the actual data. The servers then use the proxy data to train proxy models. When the proxy models are sufficiently similar to the trained actual models, the proxy data, proxy model parameters, or other learned knowledge can be transmitted to one or more non-private computing devices. The learned knowledge from many private data servers can then be aggregated into one or more trained global models without exposing private data.",
  "url": "https://patentbrief.org/patent/us/12518214/distributed-machine-learning-systems-including-generation-of-synthetic-data",
  "markdown_url": "https://patentbrief.org/patent/us/12518214/distributed-machine-learning-systems-including-generation-of-synthetic-data/md",
  "google_patents_url": "https://patents.google.com/patent/US12518214",
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