UnitedHealth Group was granted a patent for machine-learning data transformation.

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  "detected_at": "2026-09-30T17:17:46+00:00",
  "company": {
    "name": "UnitedHealth",
    "domain": "unitedhealthgroup.com"
  },
  "data": {
    "cpc": [
      {
        "code": "G06F 16/211",
        "label": "Electric digital data processing"
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        "label": "Electric digital data processing"
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    "tags": [
      "Intellectual Property",
      "Research & Development",
      "Artificial Intelligence",
      "Machine Learning",
      "Data Management"
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    "detail": "In September 2026 UnitedHealth Group patented technology for converting inconsistent third-party datasets into standardized formats used in healthcare data systems.",
    "summary": "UnitedHealth Group was granted a patent for machine-learning data transformation.",
    "event_at": "2026-09-29",
    "inventors": [
      {
        "name": "Sanjay Kumar Singh",
        "status": "unresolved",
        "is_primary": true
      },
      {
        "name": "Subhasis Jethy",
        "status": "resolved",
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      {
        "name": "Udit Saini",
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      {
        "name": "Ranju Das",
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      },
      {
        "name": "Vasant Manohar",
        "status": "unresolved",
        "is_primary": false
      }
    ],
    "tech_area": "software / computing",
    "event_kind": "grant_date",
    "grant_date": "2026-09-29",
    "source_url": "https://patents.google.com/patent/US12748734",
    "cpc_primary": {
      "code": "G06F 16/211",
      "label": "Electric digital data processing"
    },
    "patent_title": "Canonical transformations using machine learning language model",
    "patent_number": "US12748734",
    "uspto_assignee": "UNITEDHEALTH GROUP INCORPORATED",
    "patent_abstract": "Various embodiments of the present disclosure provide machine learning techniques for transforming disparate, third-party datasets to canonical representations. The techniques include generating, using a machine learning prediction model, a canonical representation for an input dataset. The machine learning prediction model is previously trained using permutative input embeddings for a training dataset based on canonical data entity features, such that each permutative input embedding corresponds to a different sequence of the canonical data entity features. The permutative input embeddings are leveraged to generate a latent representation for the training dataset. The latent representation is combined with a canonical data map to generate an alignment vector, which is refined to generate an output vector for the input dataset. The machine learning prediction model is trained using a model loss generated based on a comparison of the output vector with a corresponding labeled vector.",
    "precision_class": "structural",
    "invention_explanation": "A machine-learning model learns the different ways data can be arranged and converts incoming datasets into one consistent structure.",
    "technologies_mentioned": [
      "machine learning",
      "data normalization",
      "data integration"
    ]
  }
}

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