{
  "patent_number": "US 12245052",
  "country": "US",
  "title": "Using AI to Manage Wireless Network Resources Smarter",
  "original_title": "Reinforcement learning (RL) and graph neural network (GNN)-based resource management for wireless access networks",
  "summary": "Intel's 2025 patent on using a hierarchy of AI models to manage wireless network resources, making them more efficient by learning from network measurements and rewards.",
  "what_it_does": "This patent describes a computer system, called a 'computing node,' designed to help manage wireless networks like the 5G and beyond (Next Generation or NG). It uses a smart approach with multiple Artificial Intelligence (AI) models, specifically machine learning models, organized in layers like a pyramid. The system takes measurements from the network and uses these AI models to make decisions. A key part is how these models communicate: a model at a lower level gets instructions from a model at a higher level, and it also uses its own measurements to send signals. This allows the system to train the AI models using 'reward functions,' which are like scores that tell the AI if it's doing a good job. For example, a model might decide how to best allocate bandwidth based on traffic data and instructions from a higher-level model overseeing network stability.",
  "what_it_does_not_cover": [
    "Does not cover AI models that are not organized in a multi-level hierarchy.",
    "Does not cover systems that do not generate reward functions for training AI models.",
    "Does not cover wireless networks that are not 'Next Generation' (NG) networks.",
    "Does not cover systems where control signaling only comes from a single AI model.",
    "Does not cover network management that doesn't use network measurements."
  ],
  "filed": "2021-09-23",
  "granted": "2025-03-04",
  "expires": "2041-09-23",
  "status": "active",
  "holder": "Intel",
  "holder_url": "https://patentbrief.org/company/intel",
  "inventors": [
    {
      "name": "Shilpa Talwar",
      "url": "https://patentbrief.org/inventor/shilpa-talwar"
    },
    {
      "name": "Oner Orhan",
      "url": "https://patentbrief.org/inventor/oner-orhan"
    },
    {
      "name": "Vasuki Narasimha Swamy",
      "url": "https://patentbrief.org/inventor/vasuki-narasimha-swamy"
    },
    {
      "name": "Hosein Nikopour",
      "url": "https://patentbrief.org/inventor/hosein-nikopour"
    }
  ],
  "times_cited": 3,
  "tags": [
    "telecommunications",
    "ai_ml",
    "software",
    "consumer_electronics"
  ],
  "abstract": "A computing node to implement an RL management entity in an NG wireless network includes a NIC and processing circuitry coupled to the NIC. The processing circuitry is configured to generate a plurality of network measurements for a corresponding plurality of network functions. The functions are configured as a plurality of ML models forming a multi-level hierarchy. Control signaling from an ML model of the plurality is decoded, the ML model being at a predetermined level (e.g., a lowest level) in the hierarchy. The control signaling is responsive to a corresponding network measurement and at least second control signaling from a second ML model at a level that is higher than the predetermined level. A plurality of reward functions is generated for training the ML models, based on the control signaling from the MLO model at the predetermined level in the multi-level hierarchy.",
  "url": "https://patentbrief.org/patent/us/12245052/reinforcement-learning-rl-and-graph-neural-network-gnn-based-resource-management",
  "markdown_url": "https://patentbrief.org/patent/us/12245052/reinforcement-learning-rl-and-graph-neural-network-gnn-based-resource-management/md",
  "google_patents_url": "https://patents.google.com/patent/US12245052",
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}