{
  "$type": "site.standard.document",
  "description": "An “aggregator” controls the allocation of scarce resources among competing demands within a target machine-control environment. Multiple machine-learning agents are initiated, each with its own initial resource-utilization-optimization model based on a pre-trained model. The machine-learning…",
  "path": "/patents/1378379",
  "publishedAt": "2026-02-05T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "B60W50/0097",
    "GM GLOBAL TECHNOLOGY OPERATIONS LLC"
  ],
  "textContent": "An “aggregator” controls the allocation of scarce resources among competing demands within a target machine-control environment. Multiple machine-learning agents are initiated, each with its own initial resource-utilization-optimization model based on a pre-trained model. The machine-learning agents receive resource-utilization information from within the target environment. They then use the received information to modify their models in order to more optimally utilize the scarce resources. Each agent sends a prediction, based on the agent's modified model, to the aggregator. The aggregator uses the predictions it receives to update its own model and uses that updated aggregator model to control, at least to some extent, the allocation of the scarce resources within the target environment.",
  "title": "USING MACHINE LEARNING TO CONTROL RESOURCE UTILIZATION"
}