ConocoPhillips was granted a patent for machine-learning fluid metering.

{
  "signal_id": "456a7015-2fc9-5496-a8df-e41c260e3668",
  "signal_type": "patents-company",
  "signal_subtype": "patentGrant",
  "detected_at": "2026-09-15T03:09:50+00:00",
  "company": {
    "name": "ConocoPhillips",
    "domain": "conocophillips.com"
  },
  "data": {
    "cpc": [
      {
        "code": "G01D 5/35361"
      },
      {
        "code": "E21B 47/07"
      },
      {
        "code": "G01F 1/661"
      }
    ],
    "tags": [
      "Intellectual Property",
      "Research & Development",
      "Machine Learning",
      "Oil & Gas"
    ],
    "detail": "In September 2026 ConocoPhillips patented a system for estimating oil, gas, and water proportions in pipelines using distributed acoustic sensing data.",
    "summary": "ConocoPhillips was granted a patent for machine-learning fluid metering.",
    "event_at": "2026-09-08",
    "inventors": [
      {
        "name": "Upendra K. Tiwari",
        "status": "unresolved",
        "is_primary": true
      },
      {
        "name": "Baishali Roy",
        "status": "unresolved",
        "is_primary": false
      },
      {
        "name": "Nan Ma",
        "status": "unresolved",
        "is_primary": false
      },
      {
        "name": "Ge Jin",
        "status": "unresolved",
        "is_primary": false
      }
    ],
    "event_kind": "grant_date",
    "grant_date": "2026-09-08",
    "source_url": "https://patents.google.com/patent/US12729985",
    "cpc_primary": {
      "code": "G01D 5/35361"
    },
    "patent_title": "Machine logic multi-phase metering using distributed acoustic sensing data",
    "patent_number": "US12729985",
    "uspto_assignee": "ConocoPhillips Company",
    "patent_abstract": "A method for predicting fluid fractions is provided. The method includes building, from pressure, temperature, a fluid speed parameter, speed of sound, and fluid fractions of a first fluid flow, a machine learning model programmed to estimate fluid fractions of a fluid flow as a function of at least one Distributed Acoustic Sensing (“DAS”) fluid flow parameter and at least one physical characteristic of the fluid flow; receiving at least one DAS fluid flow parameter and the at least one physical characteristic of a second fluid flow; and determining, using the machine learning model, fluid fractions of the second fluid flow from at least the at least one DAS fluid flow parameter for the second fluid flow and the at least one physical characteristic of the second fluid flow.",
    "precision_class": "structural",
    "invention_explanation": "A machine-learning method that combines sound-based pipeline measurements with pressure, temperature, and flow information to estimate the mixture of fluids moving through a pipe.",
    "technologies_mentioned": [
      "machine learning",
      "distributed acoustic sensing",
      "multiphase flow metering",
      "pipeline monitoring"
    ]
  }
}

curl https://signals.autobound.ai/v1/signals/456a7015-2fc9-5496-a8df-e41c260e3668 \
  -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":"conocophillips.com","limit":20}'