# 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. - **Patent:** US 20250292091 - **Original title:** Systems and Methods for Dynamic Neural Network Enhancement and Adaptive Edge Computing - **Owner:** Veea - **Status:** Active - **Times cited:** 18 - **Field:** ai_ml, consumer_electronics, telecommunications, software, robotics, automotive ## What it does 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. ## 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. ## 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. ## Real-world examples 1. Smart factory sensors for quality control 2. Autonomous vehicle perception systems 3. Wearable health monitors 4. Smart city traffic management cameras 5. Agricultural drones for crop analysis 6. Remote environmental monitoring stations 7. Robotics in logistics warehouses 8. Predictive maintenance systems on industrial equipment ## Why it matters 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. ## 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. **Full plain-English explainer:** https://patentbrief.org/patent/us/20250292091/systems-and-methods-for-dynamic-neural-network-enhancement-and-adaptive-edge-com **Original patent:** https://patents.google.com/patent/US20250292091 --- _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: - [How to Update AI on Small Devices with Slow Internet](https://patentbrief.org/patent/us/20250363357/systems-and-methods-for-deploying-and-updating-neural-networks-at-the-edge-of-a-) — This patent describes a method for efficiently updating artificial intelligence models on small, internet-connected devices, like smart cameras, by sending only the changes, or 'patches,' instead of the entire updated model, which saves bandwidth. - [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 AI Learns to Run Faster on Specific Computer Chips](https://patentbrief.org/patent/us/20250371349/methods-and-apparatus-for-hardware-aware-machine-learning-model-training) — This patent describes how a smart AI system, called a reinforcement learning agent, trains other AI models to run more efficiently on specific computer hardware by cleverly reducing their size without losing too much accuracy. - [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. - [Running AI Models Locally on Edge Devices with Cloud Updates](https://patentbrief.org/patent/us/12033006/edge-deployment-of-cloud-originated-machine-learning-and-artificial-intelligence) — This patent describes a system for deploying AI models to local edge devices, allowing them to run and learn from local data, with the ability to send updates back to a central cloud for further model improvement.