# Running AI Models Locally on Edge Devices with Cloud Updates > 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. - **Patent:** US 12033006 - **Original title:** Edge deployment of cloud-originated machine learning and artificial intelligence workloads - **Owner:** Armada Systems - **Granted:** 2024 - **Status:** Active - **Times cited:** 11 - **Field:** consumer_electronics, software, telecommunications, ai_ml, semiconductors ## What it does This patent details a method where a containerized edge unit, placed at a remote location, can request and receive pre-trained AI or machine learning models from a central cloud. The edge unit then uses these models to process local sensor data, performing 'inference.' It sends batches of this inference information back to the cloud. The cloud uses this data to retrain or fine-tune the models, sending updated versions back to the edge unit. Crucially, the edge unit can then perform 'localized instruction tuning' on these updated models using its own local data store, adapting them for specific local tasks. For example, an edge unit at a factory could receive a general defect detection model, use it to analyze camera feeds of products on an assembly line, send data back to the cloud for model improvement, and then further fine-tune the model locally to recognize specific defects unique to that factory's production. ## What it does NOT cover - Deploying AI models that are not pre-trained in a cloud environment. - Edge units that cannot receive updated models from a cloud management platform. - AI models that are not containerized for deployment on the edge unit. - Systems where the edge unit does not perform localized instruction tuning on the updated model. - Edge units that do not obtain sensor data streams at their local edge location. ## The clever bit The innovation lies in the hybrid approach: not only does the edge device run AI models locally and send data back for cloud retraining, but it also performs a final 'instruction tuning' step on the updated models using its own local data. This allows for highly specific, real-time adaptation of AI models to unique local conditions without needing constant cloud intervention for every minor adjustment. ## Real-world examples 1. Smart factories with AI-powered quality control systems. 2. Autonomous drones performing localized environmental monitoring. 3. Edge computing devices in retail for real-time customer behavior analysis. 4. Industrial IoT sensors with embedded AI for predictive maintenance. ## Why it matters This patent addresses the growing need for AI processing at the 'edge' – closer to where data is generated, rather than sending everything to a distant cloud. This is crucial for applications requiring low latency, such as autonomous vehicles, industrial automation, and real-time monitoring, where delays can be unacceptable. It enables more efficient and responsive AI systems by combining the scalability of cloud training with the immediacy of edge processing. ## Frequently asked questions ### What does Running AI Models Locally on Edge Devices with Cloud Updates cover? 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. ### Who owns patent US 12033006? Armada Systems owns this patent, granted in 2024. ### When does this patent expire? This patent is expected to expire on September 5, 2043, when the invention enters the public domain. ### What is patent US 12033006 cited by? This patent has been cited by 11 later patents that build on its ideas. ### What problem does this patent solve? This patent addresses the growing need for AI processing at the 'edge' – closer to where data is generated, rather than sending everything to a distant cloud. This is crucial for applications requiring low latency, such as autonomous vehicles, industrial automation, and real-time monitoring, where delays can be unacceptable. It enables more efficient and responsive AI systems by combining the scalability of cloud training with the immediacy of edge processing. ### What does this patent NOT cover? Deploying AI models that are not pre-trained in a cloud environment. **Full plain-English explainer:** https://patentbrief.org/patent/us/12033006/edge-deployment-of-cloud-originated-machine-learning-and-artificial-intelligence **Original patent:** https://patents.google.com/patent/US12033006 --- _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 5G Networks Coordinate AI Models Across Different Devices](https://patentbrief.org/patent/us/20230412513/providing-distributed-ai-models-in-communication-networks-and-related-nodesdevic) — A method for 5G networks to translate AI requirements into network performance settings so that AI models can run efficiently across cloud, edge, and local devices. - [Training AI Models Across Different Computers](https://patentbrief.org/patent/us/12574477/distributed-deep-learning-using-a-distributed-deep-neural-network) — This 2026 patent describes a way to train AI models on one computer, send a version to another computer for further training with private data, and then update the original model with the improvements. - [Adapting AI Models to Fit Device Resources](https://patentbrief.org/patent/us/20220383078/data-processing-method-and-related-device) — This patent describes how a computer system can automatically shrink a large artificial intelligence model, specifically a "transformer" type, to fit the available computing power of a phone or other device. - [How Devices Train Shared AI Models While Keeping Your Data Private](https://patentbrief.org/patent/us/12443890/partially-local-federated-learning) — This patent describes a method for training a machine learning model across many devices, where each device keeps some parts of the model and its data private, only sharing updates for the common, global parts of the model.