C3.ai was granted a patent for predictive energy-customer segmentation.

{
  "signal_id": "e24ecaf2-135d-59aa-85e4-e2d13904905f",
  "signal_type": "patents-company",
  "signal_subtype": "patentGrant",
  "detected_at": "2026-09-09T03:59:28+00:00",
  "company": {
    "name": "C3.ai",
    "domain": "c3.ai"
  },
  "data": {
    "cpc": [
      {
        "code": "G06Q 50/06",
        "label": "Data processing systems for administrative, commercial, financial, managerial or supervisory purposes"
      },
      {
        "code": "G06N 5/01",
        "label": "Computing arrangements based on specific computational models"
      },
      {
        "code": "G06N 5/022",
        "label": "Computing arrangements based on specific computational models"
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    "tags": [
      "Intellectual Property",
      "Research & Development",
      "Artificial Intelligence",
      "Machine Learning",
      "Marketing Analytics / Insights"
    ],
    "detail": "In September 2026 C3.ai patented a machine-learning approach for identifying energy customers likely to adopt efficiency programs, supporting utility marketing and outreach.",
    "summary": "C3.ai was granted a patent for predictive energy-customer segmentation.",
    "event_at": "2026-09-01",
    "inventors": [
      {
        "name": "Adrian Albert",
        "status": "unresolved",
        "is_primary": true
      },
      {
        "name": "Mehdi Maasoumy Haghighi",
        "status": "unresolved",
        "is_primary": false
      }
    ],
    "tech_area": "business methods / CRM / fintech",
    "event_kind": "grant_date",
    "grant_date": "2026-09-01",
    "source_url": "https://patents.google.com/patent/US12725211",
    "cpc_primary": {
      "code": "G06Q 50/06",
      "label": "Data processing systems for administrative, commercial, financial, managerial or supervisory purposes"
    },
    "patent_title": "Predictive segmentation of energy customers",
    "patent_number": "US12725211",
    "uspto_assignee": "C3.ai, Inc.",
    "patent_abstract": "A computer system receives customer records listing customer attributes and an adoption status of the customer, such as whether the customer has enrolled in a particular energy efficiency program. An initial set of patterns are identified among the customer records, such as according to a decision tree. The initial set is pruned to obtain a set of patterns that meet minimum support and effectiveness and maximum overlap requirements. The patterns are assigned to segments according to an optimization algorithm that seeks to maximize the minimum effectiveness of each segment, where the effectiveness indicates a number of customers matching the pattern of each segment that have positive adoption status. The optimization algorithm may be a bisection algorithm that evaluates a linear-fractional integer program (LFIP-F) to iteratively approach an optimal distribution of patterns.",
    "precision_class": "structural",
    "invention_explanation": "A system that discovers useful customer patterns, removes redundant ones, and groups people into segments based on likely participation in energy programs.",
    "technologies_mentioned": [
      "machine learning",
      "customer segmentation",
      "decision trees",
      "energy analytics"
    ]
  }
}

curl https://signals.autobound.ai/v1/signals/e24ecaf2-135d-59aa-85e4-e2d13904905f \
  -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":"c3.ai","limit":20}'