{
  "patent_number": "US 20260065072",
  "country": "US",
  "title": "How AI Models Learn from Both Local and Central AI Systems",
  "original_title": "Knowledge acquisition for distributed learning models",
  "summary": "This patent describes a system for training specialized AI models at local sites, like factories, by combining insights from small local AI systems and large central AI systems, especially when data patterns change.",
  "what_it_does": "The patent describes a \"distributed learning knowledge acquirer\" which is a system designed to improve how AI models learn. This acquirer sits at a \"distributed edge site,\" like a factory (Claim 2), and gathers data from three places: a \"local small foundation system\" (a smaller AI model at the edge), a \"distributed endpoint management system\" (which manages devices at the edge), and a \"centralized large foundation system\" (a powerful AI model in a central location). The acquirer checks if this incoming data shows a \"conceptual drift\" (a change in data patterns). If it does, this new data is used as \"local training data\" to update a \"local discriminative model.\" This local model then learns from both the local small AI's classifications and the central large AI's classifications (Claim 1), making it better at quickly classifying new input data at the edge site (Claim 3).",
  "what_it_does_not_cover": [
    "Does not cover training discriminative AI models without using data from both a local small foundation system and a centralized large foundation system.",
    "Does not cover training AI models that do not perform a \"drift evaluation\" to identify data causing \"conceptual drift\" before using it for training.",
    "Does not cover systems where the local small foundation system has more than 10 billion parameters (Claim 5).",
    "Does not cover training discriminative AI models solely on human-labeled data, rather than on output data from other AI models.",
    "Does not cover systems that lack a \"distributed learning knowledge acquirer\" to coordinate data reception and model training across edge and central systems."
  ],
  "filed": "2024-11-29",
  "granted": null,
  "expires": "2044-11-29",
  "status": "active",
  "holder": "Dell Products LP",
  "holder_url": "https://patentbrief.org/company/dell-products-lp",
  "inventors": [
    {
      "name": "William Jeffery White",
      "url": "https://patentbrief.org/inventor/william-jeffery-white"
    }
  ],
  "times_cited": 0,
  "tags": [
    "ai_ml",
    "software",
    "telecommunications",
    "consumer_electronics",
    "manufacturing",
    "industrial_automation"
  ],
  "abstract": "The disclosure includes a distributed learning knowledge acquirer. The distributed learning knowledge acquirer allows for the training of discriminative AI models on the output data from generative AI models in a local small foundation system and a centralized large foundation system. This increases the effectiveness of the discriminative AI models which are used in an artificial intelligence hybrid distributed system to make first classifications on input data. Classifying the input data completely at the discriminative AI model level is cheaper, easier, and more time efficient making updates of the discriminative AI model critical. The distributed learning knowledge acquirer is connected to a centralized system allowing for data sharing between distributed learning knowledge acquirers in different edge sites up to and including federated learning.",
  "url": "https://patentbrief.org/patent/us/20260065072/knowledge-acquisition-for-distributed-learning-models",
  "markdown_url": "https://patentbrief.org/patent/us/20260065072/knowledge-acquisition-for-distributed-learning-models/md",
  "google_patents_url": "https://patents.google.com/patent/US20260065072",
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}