# How to Save and Reuse Skills Learned by Artificial Intelligence Hardware > 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. - **Patent:** US 10410117 - **Original title:** Method and a system for creating dynamic neural function libraries - **Owner:** BrainChip Inc - **Granted:** 2019 - **Status:** Active - **Times cited:** 1 - **Field:** semiconductors, ai_ml, consumer_electronics ## 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). ## The clever bit It treats the physical state of a silicon-based neural network (like synaptic delays and neurotransmitter levels) as a portable data object, effectively creating a 'file format' for hardware-level intelligence. ## Real-world examples 1. BrainChip Akida neuromorphic processor 2. Edge AI devices that require pre-trained behaviors 3. Hardware-based pattern recognition systems ## Why it matters This patent addresses a major bottleneck in neuromorphic computing: how to move learned 'intelligence' between physical hardware chips. By treating learned neural states as portable files, it enables a modular approach to AI hardware, where specialized skills can be developed on one device and deployed across a fleet of edge devices without retraining. ## Frequently asked questions ### What does How to Save and Reuse Skills Learned by Artificial Intelligence Hardware cover? 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. ### Who owns patent US 10410117? BrainChip Inc owns this patent, granted in 2019. ### When does this patent expire? This patent is expected to expire on May 13, 2035, when the invention enters the public domain. ### What is patent US 10410117 cited by? This patent has been cited by 1 later patents that build on its ideas. ### What problem does this patent solve? This patent addresses a major bottleneck in neuromorphic computing: how to move learned 'intelligence' between physical hardware chips. By treating learned neural states as portable files, it enables a modular approach to AI hardware, where specialized skills can be developed on one device and deployed across a fleet of edge devices without retraining. ### What does this patent NOT cover? Does not cover software-based neural networks running on standard CPUs or GPUs. **Full plain-English explainer:** https://patentbrief.org/patent/us/10410117/method-and-a-system-for-creating-dynamic-neural-function-libraries **Original patent:** https://patents.google.com/patent/US10410117 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [Efficiently Updating Connections in AI Brains](https://patentbrief.org/patent/us/9256823/apparatus-and-methods-for-efficient-updates-in-spiking-neuron-network) — This patent describes a method for updating the connections in artificial "spiking neuron networks" more efficiently by only making changes when needed, saving computational power. - [How to Automatically Expand Neural Networks by Adding New Nodes](https://patentbrief.org/patent/us/10832138/gpt-language-model-pre-training) — A method for growing artificial intelligence models by identifying underperforming parts of a network and adding new nodes based on the behavior of existing ones. - [How Nanoparticles Form Adaptive Neural Network Connections](https://patentbrief.org/patent/us/8156057/adaptive-neural-network-utilizing-nanotechnology-based-components) — This patent describes how to build and strengthen a physical neural network using tiny nanoparticles suspended in a liquid, where electric fields make the connections learn and adapt. - [How Artificial Neurons Learn from Spikes and Internal States](https://patentbrief.org/patent/us/9256215/apparatus-and-methods-for-generalized-state-dependent-learning-in-spiking-neuron) — This patent describes a method for artificial neurons to learn by adjusting their connections based on a history of incoming electrical 'spikes' and the neuron's own internal state, aiming to improve its performance. - [How to Fix Faulty Memory Cells in AI Chips](https://patentbrief.org/patent/us/10956815/killing-asymmetric-resistive-processing-units-for-neural-network-training) — This patent describes a system that tests individual memory cells in AI chips for uneven behavior and then permanently disables the faulty ones before the chip starts learning, making AI training more efficient.