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.
Original patent title: “Method and a system for creating dynamic neural function libraries”
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. Granted to BrainChip Inc in 2019 with 18 claims and 1 forward citation, and it is expected to expire in 2035.
Coverage
What does this patent actually cover?
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.
The gap
What does this patent 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).
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
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.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
BrainChip Akida neuromorphic processor
Edge AI devices that require pre-trained behaviors
Hardware-based pattern recognition systems
Why it matters
The bigger picture
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.
Filed
May 13, 2015
Granted
September 10, 2019
Market context
Who's building on this
Companies in this space
BrainChip Inc. is the primary developer of this technology, specifically through their Akida neuromorphic processor line. Other companies in the neuromorphic space, such as Intel with their Loihi chip or various startups developing spiking neural network hardware, are exploring similar concepts of state portability.
Market impact
This patent supports the transition toward 'Edge AI,' where intelligence resides on the device rather than in the cloud. By enabling the reuse of learned functions, it reduces the energy and time costs associated with training AI locally on power-constrained hardware, which is essential for the growth of autonomous sensors and IoT devices.
Claim 1 — Plain English
What this patent covers
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.
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.
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).
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Early stage
Citation count
6/40
Early citations
Claim breadth
12/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
10/20
Granted 5–10 years ago
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$78K – $250K
Midpoint $156K · 8.8 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Patent Claims
0 independent claims · 1 dependent
Claims are the legal boundaries of the patent. An independent claim stands alone. A dependent claim adds limitations to its parent, narrowing — but not broadening — the scope.
The original legal language
Original claims
18 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Made, P. A. J. V. D. (2019). How to Save and Reuse Skills Learned by Artificial Intelligence Hardware (U.S. Patent No. 10,410,117). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/10410117/method-and-a-system-for-creating-dynamic-neural-function-libraries
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
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.
Patent monitoring