Supermicro was granted a patent for AI-powered data center resource management.

{
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  "signal_type": "patents-company",
  "signal_subtype": "patentGrant",
  "detected_at": "2026-10-14T15:45:22+00:00",
  "company": {
    "name": "Supermicro",
    "domain": "supermicro.com"
  },
  "data": {
    "cpc": [
      {
        "code": "G06F 1/3246",
        "label": "Electric digital data processing"
      },
      {
        "code": "G06N 20/00",
        "label": "Computing arrangements based on specific computational models"
      }
    ],
    "tags": [
      "Intellectual Property",
      "Research & Development",
      "Artificial Intelligence",
      "Machine Learning",
      "Data Center"
    ],
    "detail": "In October 2026 Supermicro patented power-aware server provisioning for data centers, helping operators choose which machines to start or shut down.",
    "summary": "Supermicro was granted a patent for AI-powered data center resource management.",
    "event_at": "2026-10-13",
    "inventors": [
      {
        "name": "Cheng-Yi Fang",
        "status": "unresolved",
        "is_primary": true
      },
      {
        "name": "Chih Chia Huang",
        "status": "unresolved",
        "is_primary": false
      }
    ],
    "tech_area": "software / computing",
    "event_kind": "grant_date",
    "grant_date": "2026-10-13",
    "source_url": "https://patents.google.com/patent/US12761718",
    "cpc_primary": {
      "code": "G06F 1/3246",
      "label": "Electric digital data processing"
    },
    "patent_title": "Management system for provisioning server resources of a data center",
    "patent_number": "US12761718",
    "uspto_assignee": "Super Micro Computer, Inc.",
    "patent_abstract": "A data center has a management system for selecting a server computer to start or shut down. The management system has a machine learning model that is trained to predict power consumption of the data center using a large training dataset of many, different data centers. The machine learning model is fine-tuned using data of server computers of the data center. Input data that include temperature information of a server computer and position of the server computer are input to the machine learning model to obtain a predicted difference in power consumption of the data center. Predicted differences in power consumption of the data center are compared to select a server computer to start or shut down.",
    "precision_class": "structural",
    "invention_explanation": "A system that predicts facility power use from data such as server temperature and location, then selects servers to run or turn off.",
    "technologies_mentioned": [
      "data center management",
      "machine learning",
      "server provisioning",
      "power optimization"
    ]
  }
}

curl https://signals.autobound.ai/v1/signals/8caef1d5-7e09-5fb1-8273-bfbf3f89391c \
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curl -X POST https://signals.autobound.ai/v1/companies/enrich \
  -H "X-API-KEY: $AUTOBOUND_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"domain":"supermicro.com","limit":20}'