{
  "patent_number": "US 11915111",
  "country": "US",
  "title": "How Training Systems Share AI Knowledge Without Sharing Private Student Data",
  "original_title": "Federated machine learning in adaptive training systems",
  "summary": "This patent describes a system where different student training centers can improve their AI models by sharing general learning patterns, not private student data, to make training better for everyone.",
  "what_it_does": "This system uses federated machine learning to improve student training across multiple locations while protecting privacy. A first training center uses an AI module to adapt training for its students and develops a 'first learning model' based on their performance metrics (Claim 1). A second training center extracts 'statistical properties' from its students' performance metrics, then uses a 'data simulator module' to create fake, but realistic, performance data (Claim 1). This simulated data is used to build a 'second learning model'. A central 'federation computing device' then combines the 'model weights' from both the first and second learning models to create or refine a 'federated model'. For example, in a flight simulator (Claim 8), pilot performance data like control yoke movements (Claim 8) could be used to improve the AI's ability to adapt training for new pilots at different flight schools.",
  "what_it_does_not_cover": [
    "Does not cover systems that share raw student performance data directly between training centers.",
    "Does not cover machine learning systems that centralize all student performance data for model training.",
    "Does not cover federated learning approaches that exchange full local models instead of just model weights.",
    "Does not cover training systems where the AI does not adapt individualized training to each student.",
    "Does not cover systems that don't use simulated data generated from statistical properties for a local model's contribution."
  ],
  "filed": "2023-03-15",
  "granted": "2024-02-27",
  "expires": "2043-03-15",
  "status": "active",
  "holder": "CAE",
  "holder_url": "https://patentbrief.org/company/cae",
  "inventors": [
    {
      "name": "Jean-François DELISLE",
      "url": "https://patentbrief.org/inventor/jean-franois-delisle"
    },
    {
      "name": "Navpreet Singh",
      "url": "https://patentbrief.org/inventor/navpreet-singh"
    },
    {
      "name": "Ben Winokur",
      "url": "https://patentbrief.org/inventor/ben-winokur"
    }
  ],
  "times_cited": 1,
  "tags": [
    "software",
    "ai_ml",
    "telecommunications",
    "aerospace",
    "education",
    "simulation"
  ],
  "abstract": "A federated machine learning system for training students comprises a first adaptive training system having a first artificial intelligence module for adapting individualized training to a first group of students and for developing a first learning model based on a first set of learning performance metrics. A second adaptive training system provides individualized training to a second group of students and has a data property extraction module for extracting statistical properties from a second set of learning performance metrics for the second group of students. A data simulator module generates simulated performance metrics using extracted statistical properties from the second set of learning performance metrics to thereby generate a second learning model. A federation computing device receives first and second model weights for the first and second learning models and generates or refines a federated model based on the first and second model weights.",
  "url": "https://patentbrief.org/patent/us/11915111/federated-machine-learning-in-adaptive-training-systems",
  "markdown_url": "https://patentbrief.org/patent/us/11915111/federated-machine-learning-in-adaptive-training-systems/md",
  "google_patents_url": "https://patents.google.com/patent/US11915111",
  "relatedPatents": [
    {
      "patentNumber": "12518214",
      "countryCode": "US",
      "title": "Training AI on Private Data Without Seeing It",
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    },
    {
      "patentNumber": "20220012637",
      "countryCode": "US",
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      "url": "https://patentbrief.org/patent/us/20220012637/federated-teacher-student-machine-learning"
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    },
    {
      "patentNumber": "12039012",
      "countryCode": "US",
      "title": "Sharing AI Knowledge Between Private Datasets Using Synthetic Data",
      "url": "https://patentbrief.org/patent/us/12039012/systems-and-methods-for-heterogeneous-federated-transfer-learning"
    },
    {
      "patentNumber": "11429762",
      "countryCode": "US",
      "title": "How Computers Train AI Models Using Separate Virtual Simulations",
      "url": "https://patentbrief.org/patent/us/11429762/simulation-orchestration-for-training-reinforcement-learning-models"
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  ]
}