How AI at the Edge Learns and Adapts Its Brain
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.
Original patent title: “Systems and Methods for Dynamic Neural Network Enhancement and Adaptive Edge Computing”
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. Owned by Veea with 39 claims and 18 forward citations, and it is expected to expire in 2045.
Coverage
What does this patent actually cover?
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 (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 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.
The gap
What does this patent 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.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The truly clever part is the combination of dynamically changing the AI's internal structure—adding or removing "brain" layers—with using a sophisticated "Kalman gain" calculation to precisely tune its learning, all while operating efficiently on smaller devices. This allows the AI to self-optimize its complexity and accuracy in real-time, adapting to its environment without needing constant human intervention or massive computing power.
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 factory sensors for quality control
Autonomous vehicle perception systems
Wearable health monitors
Smart city traffic management cameras
Agricultural drones for crop analysis
Remote environmental monitoring stations
Robotics in logistics warehouses
Predictive maintenance systems on industrial equipment
Why it matters
The bigger picture
This patent is important for making artificial intelligence more practical and powerful in "edge computing" environments, which are devices like smart sensors or robots that operate far from central data centers. By allowing AI models to adapt and optimize themselves in real-time, it reduces the need for constant human oversight and heavy data transfer to the cloud. This enables more reliable and efficient AI applications in places where internet connectivity is unreliable or computational power is limited, like remote industrial sites or autonomous vehicles. It helps AI systems maintain high accuracy even as their operating conditions change, making them more robust for real-world use.
Filed
March 10, 2025
Market context
Who's building on this
Companies in this space
Companies like NVIDIA, Intel, and Qualcomm are heavily invested in developing hardware and software for edge AI, focusing on efficient inference and on-device learning. Startups in the IoT and industrial AI space, such as those building smart cameras or predictive maintenance solutions, are also actively developing adaptive AI models for resource-constrained environments. Veea Inc., the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is focused on edge computing platforms and services, indicating their continued development in this area.
Market impact
This technology enables a significant shift towards more autonomous and resilient AI deployments at the edge, reducing reliance on constant cloud connectivity and powerful central servers. It could accelerate the adoption of AI in industries like manufacturing, logistics, and smart infrastructure, where real-time, adaptive intelligence is crucial. The ability to perform low-bandwidth updates and dynamic self-optimization could lower operational costs and expand the reach of sophisticated AI applications to previously unfeasible locations.
Claim 1 — Plain English
What this patent covers
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.
The clever bit
The truly clever part is the combination of dynamically changing the AI's internal structure—adding or removing "brain" layers—with using a sophisticated "Kalman gain" calculation to precisely tune its learning, all while operating efficiently on smaller devices. This allows the AI to self-optimize its complexity and accuracy in real-time, adapting to its environment without needing constant human intervention or massive computing power.
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.
Patent timeline
Application submitted to the patent office
Patent enters public domain
PatentBrief Score
Impact Score
Moderate
Citation count
26/40
Moderately cited
Claim breadth
20/20
Very broad protection
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
$230K – $737K
Midpoint $461K · 18.4 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
39 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Smith, C. How AI at the Edge Learns and Adapts Its Brain (U.S. Patent No. 20,250,292,091). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/20250292091/systems-and-methods-for-dynamic-neural-network-enhancement-and-adaptive-edge-com
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 AI at the Edge Learns and Adapts Its Brain cover?
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.
Who owns patent US 20250292091?
This patent is owned by Veea.
When does this patent expire?
This patent is expected to expire on March 10, 2045, when the invention enters the public domain.
What is patent US 20250292091 cited by?
This patent has been cited by 18 later patents that build on its ideas.
What problem does this patent solve?
This patent is important for making artificial intelligence more practical and powerful in "edge computing" environments, which are devices like smart sensors or robots that operate far from central data centers. By allowing AI models to adapt and optimize themselves in real-time, it reduces the need for constant human oversight and heavy data transfer to the cloud. This enables more reliable and efficient AI applications in places where internet connectivity is unreliable or computational power is limited, like remote industrial sites or autonomous vehicles. It helps AI systems maintain high…
What does this patent NOT cover?
Does not cover neural networks that only adjust weights without dynamically changing their hidden layer structure.
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