{
  "patent_number": "US 11062228",
  "country": "US",
  "title": "How AI Learns New Tasks Using Old Data Labels",
  "original_title": "Transfer learning techniques for disparate label sets",
  "summary": "A method for helping AI models understand new topics by grouping similar labels from different datasets into a shared, broader category.",
  "what_it_does": "This patent describes a way to teach an AI model about a new subject by using knowledge from a subject it already knows. It works by turning labels (like 'weather' or 'flight status') into mathematical vectors, which are lists of numbers that represent their meaning. The system then uses clustering algorithms to find common ground between these different labels, creating a 'coarse label' that acts as a bridge. For example, if the AI knows 'book flight' and 'reserve seat,' it can use this shared 'coarse' category to better understand a new, related task like 'check-in for flight' even if it has very little training data for that specific task.",
  "what_it_does_not_cover": [
    "Does not cover systems that rely solely on manually defined rules rather than vector-based clustering.",
    "Does not cover models that perform transfer learning without first creating a coarse label set as an abstraction layer.",
    "Does not cover the specific hardware architecture, only the software-based method of label mapping."
  ],
  "filed": "2015-07-06",
  "granted": "2021-07-13",
  "expires": null,
  "status": "active",
  "holder": "Microsoft Technology Licensing LLC",
  "holder_url": "https://patentbrief.org/company/microsoft-technology-licensing-llc",
  "inventors": [
    {
      "name": "Young-Bum Kim",
      "url": "https://patentbrief.org/inventor/young-bum-kim"
    },
    {
      "name": "Ruhi Sarikaya",
      "url": "https://patentbrief.org/inventor/ruhi-sarikaya"
    }
  ],
  "times_cited": 4,
  "tags": [
    "ai_ml",
    "software",
    "telecommunications"
  ],
  "abstract": "Examples of the present disclosure describe systems and methods of transfer learning techniques for disparate label sets. In aspects, a data set may be accessed on a server device. The data set may comprise labels and word sets associated with the labels. The server device may induce label embedding within the data set. The embedded labels may be represented by multi-dimensional vectors that correspond to particular labels. The vectors may be used to construct label mappings for the data set. The label mappings may be used to train a model to perform domain adaptation or transfer learning techniques. The model may be used to provide results to a statement/query or to train a different model.",
  "url": "https://patentbrief.org/patent/us/11062228/gpt-3-few-shot-learning",
  "markdown_url": "https://patentbrief.org/patent/us/11062228/gpt-3-few-shot-learning/md",
  "google_patents_url": "https://patents.google.com/patent/US11062228",
  "relatedPatents": []
}