Autodesk was granted a patent for AI-controlled water networks

{
  "signal_id": "6552c406-b776-57e6-ab41-d85691c6d887",
  "signal_type": "patents-company",
  "signal_subtype": "patentGrant",
  "detected_at": "2026-10-30T00:25:06+00:00",
  "company": {
    "name": "Autodesk",
    "domain": "autodesk.com"
  },
  "data": {
    "cpc": [
      {
        "code": "G05B 13/0265"
      },
      {
        "code": "G05B 13/04"
      }
    ],
    "tags": [
      "Intellectual Property",
      "Research & Development",
      "Artificial Intelligence",
      "Machine Learning",
      "Utility Operations"
    ],
    "detail": "In October 2026 Autodesk patented reinforcement-learning technology for managing municipal water distribution, balancing tank levels, energy costs, and system changes.",
    "summary": "Autodesk was granted a patent for AI-controlled water networks",
    "event_at": "2026-10-27",
    "inventors": [
      {
        "name": "Yuan Zhou",
        "status": "unresolved",
        "is_primary": true
      },
      {
        "name": "Shu Wang",
        "status": "unresolved",
        "is_primary": false
      },
      {
        "name": "Harsh Bakulchandra Patel",
        "status": "resolved",
        "is_primary": false,
        "linkedin_url": "https://www.linkedin.com/in/harsh-patel-25526476"
      },
      {
        "name": "Michael Pennell",
        "status": "unresolved",
        "is_primary": false
      },
      {
        "name": "Jieliang Luo",
        "status": "unresolved",
        "is_primary": false
      },
      {
        "name": "Alexander Lamb",
        "status": "resolved",
        "is_primary": false,
        "linkedin_url": "https://www.linkedin.com/in/lambalexander"
      }
    ],
    "event_kind": "grant_date",
    "grant_date": "2026-10-27",
    "source_url": "https://patents.google.com/patent/US12775248",
    "cpc_primary": {
      "code": "G05B 13/0265"
    },
    "patent_title": "Hybrid reinforcement learning (RL) to control a water distribution network",
    "patent_number": "US12775248",
    "uspto_assignee": "AUTODESK, INC.",
    "patent_abstract": "A method and system control a water distribution network. A database is maintained of prior states based on a residential water demand, a tank level, and an energy tariff. A current state of the water distribution network is determined. Rewards are determined and include a tank level constraint, an energy cost, and a toggle count. A query based model is used to determine a set of control points used to control a first prior state. An RL agent is trained based on the prior states and rewards. The RL agent determines a control setpoint (that changes the pump speed) that maintains the tank level, minimizes the energy cost, and complies with the toggle count. The RL agent determines time slots and selects one of the time slots. Hybrid setpoints are generated to control the water distribution network within the selected time slot.",
    "precision_class": "structural",
    "invention_explanation": "A control system learns from past water demand and tank conditions to choose network settings that maintain supply while reducing energy use and unnecessary adjustments.",
    "technologies_mentioned": [
      "reinforcement learning",
      "water distribution",
      "predictive control"
    ]
  }
}

curl https://signals.autobound.ai/v1/signals/6552c406-b776-57e6-ab41-d85691c6d887 \
  -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":"autodesk.com","limit":20}'