How Distributed AI Models Learn Privately on Edge Devices
This patent describes how a central computer can train a basic artificial intelligence model, send it to a remote device, and let that device customize the model using its own private data, then use it to filter information before sending it back.
Original patent title: “Distributed deep learning using a distributed deep neural network”
This patent describes how a central computer can train a basic artificial intelligence model, send it to a remote device, and let that device customize the model using its own private data, then use it to filter information before sending it back. Owned by Deep Sentinel with 23 claims, and it is expected to expire in 2046.
Coverage
What does this patent actually cover?
This patent details a system where a central computer, called a "first computing system," first trains a neural network using general data collected from many remote devices (claimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). Once this initial training is done, the central system sends this partially trained neural network to a specific remote device, a "second computing system." This remote device then takes the neural network and further trains it using its own "private data" that isn't shared with the central system (claim 1). For example, a security camera (the second computing system) could receive a general object detection model, then train it specifically on its own footage of common local events, like a delivery person, without sending that private footage back to the central server. The customized neural network then processes "live event data" directly on the remote device, filtering out less important events before any data is sent back to the central system (claim 1, claim 4).
The gap
What does this patent NOT cover?
- Does not cover a system where all neural network training, including customization, happens only on a single central server.
- Does not cover scenarios where the remote device sends all its raw private data back to the central system for training.
- Does not cover a system where the customized neural network is not applied to filter live event data at the remote device.
- Does not cover a system where the central system does not initially train a neural network before sending it to the remote system.
- Does not cover a system where the remote device's private data is not used to further train and customize the neural network.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The noveltynoveltyThe requirement that an invention be different from anything publicly known before its priority date.Read more → lies in the specific two-stage training process: a general model is trained centrally, then sent to an edge device for private, localized refinement. This allows for personalized AI performance while keeping sensitive data on the device where it originates, rather than sending it all to a central server.
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
Smart security cameras that learn to recognize specific local events without uploading all footage.
Industrial sensors that adapt anomaly detection models to unique machine behaviors on-site.
Personalized health monitoring devices that refine AI models using individual user data.
Federated learning systems where models are trained collaboratively without sharing raw data.
Why it matters
The bigger picture
This technology is important for applications where data privacy is critical, such as surveillance, healthcare, or industrial monitoring. By allowing AI models to be customized on local devices using private data, it reduces the need to transmit sensitive information to a central server. This approach can also make AI systems more efficient by filtering irrelevant data at the source, saving bandwidth and processing power for the central system.
Filed
March 5, 2026
Market context
Who's building on this
Companies in this space
Deep Sentinel Corp, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, focuses on AI-powered security solutions, which aligns directly with the patent's focus on remote intelligent filtering, potentially using cameras as the 'second computing system.' Major cloud providers like Google, Amazon, and Microsoft, along with companies specializing in edge AI hardware and software, are also actively developing and deploying similar distributed machine learning architectures to enable more private and efficient AI at the edge.
Market impact
This patent addresses a growing need for privacy-preserving and efficient AI at the edge, a significant trend in the AI market. It enables the deployment of smarter devices that can adapt to local conditions without compromising user data, potentially reducing bandwidth costs and improving response times for real-time applications. This approach helps overcome data privacy concerns that often hinder the broader adoption of AI in sensitive areas, fostering the development of more robust and trustworthy AI systems.
Claim 1 — Plain English
What this patent covers
This patent details a system where a central computer, called a "first computing system," first trains a neural network using general data collected from many remote devices (claim 1). Once this initial training is done, the central system sends this partially trained neural network to a specific remote device, a "second computing system." This remote device then takes the neural network and further trains it using its own "private data" that isn't shared with the central system (claim 1). For example, a security camera (the second computing system) could receive a general object detection model, then train it specifically on its own footage of common local events, like a delivery person, without sending that private footage back to the central server. The customized neural network then processes "live event data" directly on the remote device, filtering out less important events before any data is sent back to the central system (claim 1, claim 4).
The clever bit
The novelty lies in the specific two-stage training process: a general model is trained centrally, then sent to an edge device for private, localized refinement. This allows for personalized AI performance while keeping sensitive data on the device where it originates, rather than sending it all to a central server.
What it does not cover
- Does not cover a system where all neural network training, including customization, happens only on a single central server.
- Does not cover scenarios where the remote device sends all its raw private data back to the central system for training.
- Does not cover a system where the customized neural network is not applied to filter live event data at the remote device.
- Does not cover a system where the central system does not initially train a neural network before sending it to the remote system.
- Does not cover a system where the remote device's private data is not used to further train and customize the neural network.
Patent timeline
Application submitted to the patent office
Patent enters public domain
PatentBrief Score
Impact Score
Limited data
Citation count
0/40
No citations yet
Claim breadth
15/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
0/20
Older than 20 years
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
$31K – $100K
Midpoint $62K · 19.4 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.
Claim text not yet imported for this patent
The original legal language
Original claims
23 claims as filed with the patent office.
Concepts involved
Cite this patent
Chen, C., Yip, C., & Selinger, D. L. How Distributed AI Models Learn Privately on Edge Devices (U.S. Patent No. 20,260,197,424). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/20260197424/distributed-deep-learning-using-a-distributed-deep-neural-network-20260197424
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 Distributed AI Models Learn Privately on Edge Devices cover?
This patent describes how a central computer can train a basic artificial intelligence model, send it to a remote device, and let that device customize the model using its own private data, then use it to filter information before sending it back.
Who owns patent US 20260197424?
This patent is owned by Deep Sentinel.
When does this patent expire?
This patent is expected to expire on March 5, 2046, when the invention enters the public domain.
What problem does this patent solve?
This technology is important for applications where data privacy is critical, such as surveillance, healthcare, or industrial monitoring. By allowing AI models to be customized on local devices using private data, it reduces the need to transmit sensitive information to a central server. This approach can also make AI systems more efficient by filtering irrelevant data at the source, saving bandwidth and processing power for the central system.
What does this patent NOT cover?
Does not cover a system where all neural network training, including customization, happens only on a single central server.
Same assignee
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