{
  "patent_number": "US 11853891",
  "country": "US",
  "title": "How AI Models Learn from Your Health Data Without Sharing It",
  "original_title": "System and method with federated learning model for medical research applications",
  "summary": "This patent describes a system where personal health data, like photos or audio, stays on your device while helping train a shared AI model for medical research, improving predictions without sending your raw data.",
  "what_it_does": "This system uses a method called federated learning to train artificial intelligence models. It involves multiple 'edge devices' (like your smartphone) that each have a program called a 'federated learner' running a 'deep neural network' (DNN). This DNN predicts characteristics about you, such as your mood or age, from your recorded data like pictures or audio, as mentioned in claims 1, 2, 5, and 7. If the prediction is wrong, you can correct it. Your device then uses your private data and corrections to update its part of the DNN model. Instead of sending your raw data, only a 'modified part of the modified tensor' (a small, updated piece of the AI model) is sent to a central 'FL aggregator'. The aggregator combines these updates from many devices to create a better, shared model, which it then sends back to all the devices. For example, claim 1 specifies that if the data is a picture, the system only performs this update training if a face is detected in the image.",
  "what_it_does_not_cover": [
    "It does not cover systems where raw user data, like pictures or audio recordings, is directly uploaded to a central server for training.",
    "It does not cover AI training systems that do not perform 'update training' of the deep neural network directly on the end user's device.",
    "It does not cover federated learning systems that send the entire model parameters from the edge device to the aggregator, rather than just a 'modified part'.",
    "It does not cover systems that use picture data for training without first detecting a face in the picture, as specified in claim 1.",
    "It does not cover federated learning without an 'FL aggregator' that combines updates from multiple edge devices to produce a new base model."
  ],
  "filed": "2020-03-11",
  "granted": "2023-12-26",
  "expires": "2040-03-11",
  "status": "active",
  "holder": "Sharecare AI",
  "holder_url": "https://patentbrief.org/company/sharecare-ai",
  "inventors": [
    {
      "name": "Walter Adolf DE BROUWER",
      "url": "https://patentbrief.org/inventor/walter-adolf-de-brouwer"
    },
    {
      "name": "Kartik THAKORE",
      "url": "https://patentbrief.org/inventor/kartik-thakore"
    },
    {
      "name": "Philip Joseph DOW",
      "url": "https://patentbrief.org/inventor/philip-joseph-dow"
    },
    {
      "name": "Srivatsa Akshay SHARMA",
      "url": "https://patentbrief.org/inventor/srivatsa-akshay-sharma"
    },
    {
      "name": "Neerajshyam Rangan KASHYAP",
      "url": "https://patentbrief.org/inventor/neerajshyam-rangan-kashyap"
    }
  ],
  "times_cited": 1,
  "tags": [
    "consumer_electronics",
    "software",
    "ai_ml",
    "telecommunications",
    "healthcare"
  ],
  "abstract": "Method and system with federated learning model for health care applications are disclosed. The system for federated learning comprises multiple edge devices of end users, one or more federated learner update repository, and one or more cloud. Each edge device comprises a federated learner model, configured to send tensors to federated learner update repository. Cloud comprises a federated learner model, configured to send tensors to federated learner update repository. Federated learner update repository comprises a back-end configuration, configured to send model updates to edge devices and cloud.",
  "url": "https://patentbrief.org/patent/us/11853891/system-and-method-with-federated-learning-model-for-medical-research-application",
  "markdown_url": "https://patentbrief.org/patent/us/11853891/system-and-method-with-federated-learning-model-for-medical-research-application/md",
  "google_patents_url": "https://patents.google.com/patent/US11853891",
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}