{
  "patent_number": "US 20250292091",
  "country": "US",
  "title": "How AI at the Edge Learns and Adapts Its Brain",
  "original_title": "Systems and Methods for Dynamic Neural Network Enhancement and Adaptive Edge Computing",
  "summary": "This patent describes how an artificial intelligence system running on a small device can automatically improve its accuracy and efficiency by changing its internal structure and learning methods in real-time.",
  "what_it_does": "The patent describes a computing device that runs a neural network, a type of AI, and makes it smarter and more efficient on the fly. It \"monitors real-time model accuracy\" using a feedback loop to catch changes in how well the AI is guessing (Claim 1). If accuracy drops or computational limits are hit, the system can \"dynamically adjust a structure of the neural network by adding or removing hidden layers\" (Claim 1). This is like adding or removing sections of the AI's \"brain\" to make it work better. It uses a special calculation called \"Kalman gain\" to figure out how much to tweak the AI's internal \"weights\" (Claim 1), which are like the strength of connections between its \"neurons.\" These small \"incremental weight adjustments\" are stored to allow for \"low-bandwidth model updates\" (Claim 1), meaning the AI can be updated without needing a huge internet connection. Finally, it applies \"predictive holistic inference logic (PHIL)\" to its stored results to make its guesses even more accurate (Claim 1). For example, a smart camera on a factory floor could use this to automatically improve its ability to spot defects as lighting changes, without needing constant manual updates or a powerful cloud connection.",
  "what_it_does_not_cover": [
    "Does not cover neural networks that only adjust weights without dynamically changing their hidden layer structure.",
    "Does not cover AI systems that update their models using high-bandwidth, full model replacements rather than incremental weight adjustments.",
    "Does not cover systems that lack a real-time feedback loop for monitoring inference accuracy.",
    "Does not cover AI systems that do not use Kalman gain to determine weight adjustments.",
    "Does not cover AI systems that do not apply Predictive Holistic Inference Logic (PHIL) to enhance inference accuracy.",
    "Does not cover AI models that are only trained once and not periodically updated based on stored incremental adjustments."
  ],
  "filed": "2025-03-10",
  "granted": null,
  "expires": "2045-03-10",
  "status": "active",
  "holder": "Veea",
  "holder_url": "https://patentbrief.org/company/veea",
  "inventors": [
    {
      "name": "Clint Smith",
      "url": "https://patentbrief.org/inventor/clint-smith"
    }
  ],
  "times_cited": 18,
  "tags": [
    "ai_ml",
    "consumer_electronics",
    "telecommunications",
    "software",
    "robotics",
    "automotive"
  ],
  "abstract": "Systems and methods for adaptive edge computing using artificial intelligence (AI) include monitoring real-time accuracy of a neural network by using a feedback loop configured to detect changes in inference accuracy and dynamically adjusting the structure of the neural network by adding or removing hidden layers based on monitored error rates and predetermined computational constraints. A Kalman gain computation determines neural network weight adjustments based on monitored error rates. Weight matrices undergo incremental updates derived from these adjustments. Incremental weight adjustments remain stored in memory to enable low-bandwidth model updates. The neural network stores inference results and refined weights in an inference result database. Pre-trained models periodically receive incremental updates based on stored adjustments. Predictive holistic inference logic (PHIL) applied to stored inference results improves the accuracy of the inference results.",
  "url": "https://patentbrief.org/patent/us/20250292091/systems-and-methods-for-dynamic-neural-network-enhancement-and-adaptive-edge-com",
  "markdown_url": "https://patentbrief.org/patent/us/20250292091/systems-and-methods-for-dynamic-neural-network-enhancement-and-adaptive-edge-com/md",
  "google_patents_url": "https://patents.google.com/patent/US20250292091",
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}