Intuit was granted a patent for checking AI-generated database queries.

{
  "signal_id": "05001166-8b52-5f7c-8e30-ce23f0f955e1",
  "signal_type": "patents-company",
  "signal_subtype": "patentGrant",
  "detected_at": "2026-10-30T00:31:45+00:00",
  "company": {
    "name": "Intuit",
    "domain": "intuit.com"
  },
  "data": {
    "cpc": [
      {
        "code": "G06F 16/24575",
        "label": "Electric digital data processing"
      },
      {
        "code": "G06F 16/211",
        "label": "Electric digital data processing"
      },
      {
        "code": "G06F 16/2246",
        "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",
      "Database"
    ],
    "detail": "In October 2026 Intuit patented a method for measuring confidence in machine-generated structured queries, supporting reliable data retrieval in software systems.",
    "summary": "Intuit was granted a patent for checking AI-generated database queries.",
    "event_at": "2026-10-27",
    "inventors": [
      {
        "name": "Hilaf Hasson",
        "status": "resolved",
        "is_primary": true,
        "linkedin_url": "https://www.linkedin.com/in/hilaf-hasson-715b2055"
      }
    ],
    "tech_area": "software / computing",
    "event_kind": "grant_date",
    "grant_date": "2026-10-27",
    "source_url": "https://patents.google.com/patent/US12775626",
    "cpc_primary": {
      "code": "G06F 16/24575",
      "label": "Electric digital data processing"
    },
    "patent_title": "Node-level uncertainty estimation for machine learning model generated structured queries",
    "patent_number": "US12775626",
    "uspto_assignee": "INTUIT INC.",
    "patent_abstract": "Aspects of the present disclosure relate to estimating uncertainty for machine learning model generated structured queries. Aspects include generating a structured query using a generative machine learning model based on an input request, obtaining a ground truth structured query corresponding to the input request, and parsing the generated structured query and the ground truth query into abstract syntax trees (ASTs). Aspects include aligning nodes between the ASTs to automatically generate labels for nodes in the AST for the generated structured query, generating features for each node in the generated ASTs, and training a machine learning model using the generated features and the generated labels to predict node-level error probabilities in generated structured queries.",
    "precision_class": "structural",
    "invention_explanation": "A way to compare an AI-written query with a correct version by matching their syntax-tree parts and identifying where the model may be wrong.",
    "technologies_mentioned": [
      "machine learning",
      "structured queries",
      "database systems",
      "uncertainty estimation"
    ]
  }
}

curl https://signals.autobound.ai/v1/signals/05001166-8b52-5f7c-8e30-ce23f0f955e1 \
  -H "X-API-KEY: $AUTOBOUND_API_KEY"

curl -X POST https://signals.autobound.ai/v1/companies/enrich \
  -H "X-API-KEY: $AUTOBOUND_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"domain":"intuit.com","limit":20}'