Rebellions was granted a patent for environment-aware neural-network quantization.

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  "signal_type": "patents-company",
  "signal_subtype": "patentGrant",
  "detected_at": "2026-09-20T18:04:07+00:00",
  "company": {
    "name": "Rebellions",
    "domain": "rebellions.ai"
  },
  "data": {
    "cpc": [
      {
        "code": "G06V 10/82",
        "label": "Image or video recognition or understanding"
      },
      {
        "code": "G06N 3/0495",
        "label": "Computing arrangements based on specific computational models"
      }
    ],
    "tags": [
      "Intellectual Property",
      "Research & Development",
      "Artificial Intelligence",
      "Machine Learning"
    ],
    "detail": "In September 2026 Rebellions patented adaptive AI processing for computer-vision systems operating across changing lighting, scenes, or other conditions.",
    "summary": "Rebellions was granted a patent for environment-aware neural-network quantization.",
    "event_at": "2026-09-15",
    "inventors": [
      {
        "name": "Won Jae Lee",
        "status": "unresolved",
        "is_primary": true
      },
      {
        "name": "Ji Eun Lim",
        "status": "unresolved",
        "is_primary": false
      }
    ],
    "tech_area": "computer vision",
    "event_kind": "grant_date",
    "grant_date": "2026-09-15",
    "source_url": "https://patents.google.com/patent/US12738047",
    "cpc_primary": {
      "code": "G06V 10/82",
      "label": "Image or video recognition or understanding"
    },
    "patent_title": "Method and device for quantizing deep learning neural network model by considering change in external environment",
    "patent_number": "US12738047",
    "uspto_assignee": "REBELLIONS INC.",
    "patent_abstract": "A quantization method of a deep learning neural network model is disclosed. An embodiment of the disclosure provides a quantization method comprising: detecting a feature change of input data caused by a change in an external environment, from input image data of a quantized deep learning neural network model based on a plurality of preset quantization parameters; performing quantization calibration for the deep learning neural network model to determine a new quantization parameter corresponding to the feature change of input data caused by the change in the external environment; and updating at least one of the plurality of preset quantization parameters based on the new quantization parameter.",
    "precision_class": "structural",
    "invention_explanation": "A method that detects when image inputs change because the surroundings changed, then recalibrates the model’s numerical settings to keep recognition accurate.",
    "technologies_mentioned": [
      "deep learning",
      "neural-network quantization",
      "computer vision",
      "image recognition"
    ]
  }
}

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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":"rebellions.ai","limit":20}'