{
  "patent_number": "US 10410117",
  "country": "US",
  "title": "How to Save and Reuse Skills Learned by Artificial Intelligence Hardware",
  "original_title": "Method and a system for creating dynamic neural function libraries",
  "summary": "A method for capturing the internal settings of a neuromorphic AI chip after it learns a task, allowing that 'skill' to be exported and loaded onto another AI chip.",
  "what_it_does": "This patent describes a way to extract the specific configuration of an AI hardware device—specifically a neuromorphic chip—after it has learned a task. Instead of just saving software code, the system captures the 'control values' stored in the chip's synaptic registers, which include parameters like neurotransmitter levels, dendrite delays, and axonal delays. These values effectively act as a snapshot of the device's learned behavior. This snapshot can then be stored in a library and transferred to a second, similar AI device, allowing it to perform the same task without needing to undergo the original training process.",
  "what_it_does_not_cover": [
    "Does not cover software-based neural networks running on standard CPUs or GPUs.",
    "Does not cover the specific algorithms used to train the initial neural network.",
    "Does not cover the transfer of raw data or training datasets, only the resulting synaptic control values.",
    "Does not cover cloud-based model weight sharing (e.g., standard TensorFlow or PyTorch model exports)."
  ],
  "filed": "2015-05-13",
  "granted": "2019-09-10",
  "expires": "2035-05-13",
  "status": "active",
  "holder": "BrainChip Inc",
  "holder_url": "https://patentbrief.org/company/brainchip-inc",
  "inventors": [
    {
      "name": "Peter A J van der Made",
      "url": "https://patentbrief.org/inventor/peter-a-j-van-der-made"
    }
  ],
  "times_cited": 1,
  "tags": [
    "semiconductors",
    "ai_ml",
    "consumer_electronics"
  ],
  "abstract": "A method for creating a dynamic neural function library that relates to Artificial Intelligence systems and devices is provided. Within a dynamic neural network (artificial intelligent device), a plurality of control values are autonomously generated during a learning process and thus stored in synaptic registers of the artificial intelligent device that represent a training model of a task or a function learned by the artificial intelligent device. Control Values include, but are not limited to, values that indicate the neurotransmitter level that is present in the synapse, the neurotransmitter type, the connectome, the neuromodulator sensitivity, and other synaptic, dendric delay and axonal delay parameters. These values form collectively a training model. Training models are stored in the dynamic neural function library of the artificial intelligent device. The artificial intelligent device copies the function library to an electronic data processing device memory that is reusable to train another artificial intelligent device.",
  "url": "https://patentbrief.org/patent/us/10410117/method-and-a-system-for-creating-dynamic-neural-function-libraries",
  "markdown_url": "https://patentbrief.org/patent/us/10410117/method-and-a-system-for-creating-dynamic-neural-function-libraries/md",
  "google_patents_url": "https://patents.google.com/patent/US10410117",
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}