# How AI Models Learn from Both Local and Central AI Systems > This patent describes a system for training specialized AI models at local sites, like factories, by combining insights from small local AI systems and large central AI systems, especially when data patterns change. - **Patent:** US 20260065072 - **Original title:** Knowledge acquisition for distributed learning models - **Owner:** Dell Products LP - **Status:** Active - **Times cited:** 0 - **Field:** ai_ml, software, telecommunications, consumer_electronics, manufacturing, industrial_automation ## What it does The patent describes a "distributed learning knowledge acquirer" which is a system designed to improve how AI models learn. This acquirer sits at a "distributed edge site," like a factory (Claim 2), and gathers data from three places: a "local small foundation system" (a smaller AI model at the edge), a "distributed endpoint management system" (which manages devices at the edge), and a "centralized large foundation system" (a powerful AI model in a central location). The acquirer checks if this incoming data shows a "conceptual drift" (a change in data patterns). If it does, this new data is used as "local training data" to update a "local discriminative model." This local model then learns from both the local small AI's classifications and the central large AI's classifications (Claim 1), making it better at quickly classifying new input data at the edge site (Claim 3). ## What it does NOT cover - Does not cover training discriminative AI models without using data from both a local small foundation system and a centralized large foundation system. - Does not cover training AI models that do not perform a "drift evaluation" to identify data causing "conceptual drift" before using it for training. - Does not cover systems where the local small foundation system has more than 10 billion parameters (Claim 5). - Does not cover training discriminative AI models solely on human-labeled data, rather than on output data from other AI models. - Does not cover systems that lack a "distributed learning knowledge acquirer" to coordinate data reception and model training across edge and central systems. ## The clever bit The novelty lies in intelligently combining the strengths of small, local generative AI models with large, centralized generative AI models to train discriminative AI models. It specifically uses the concept of "conceptual drift" to identify when local data changes enough to warrant updating the local model, ensuring the edge AI remains relevant and accurate. ## Real-world examples 1. Quality control AI in smart factories 2. Predictive maintenance systems on industrial equipment 3. Edge computing devices for real-time data analysis 4. Automated inspection systems in manufacturing 5. Smart city infrastructure monitoring ## Why it matters In today's world, many devices, like those in smart factories, need to make quick decisions without sending all their data to a central cloud. This patent helps train "discriminative AI models" (AI that classifies things) more effectively and cheaply right where they are used. By combining local, specialized knowledge with broad, central intelligence, these models can adapt quickly to new situations, like changes on a factory floor, making the entire system more efficient and responsive. ## Frequently asked questions ### What does How AI Models Learn from Both Local and Central AI Systems cover? This patent describes a system for training specialized AI models at local sites, like factories, by combining insights from small local AI systems and large central AI systems, especially when data patterns change. ### Who owns patent US 20260065072? This patent is owned by Dell Products LP. ### When does this patent expire? This patent is expected to expire on November 29, 2044, when the invention enters the public domain. ### What problem does this patent solve? In today's world, many devices, like those in smart factories, need to make quick decisions without sending all their data to a central cloud. This patent helps train "discriminative AI models" (AI that classifies things) more effectively and cheaply right where they are used. By combining local, specialized knowledge with broad, central intelligence, these models can adapt quickly to new situations, like changes on a factory floor, making the entire system more efficient and responsive. ### What does this patent NOT cover? Does not cover training discriminative AI models without using data from both a local small foundation system and a centralized large foundation system. **Full plain-English explainer:** https://patentbrief.org/patent/us/20260065072/knowledge-acquisition-for-distributed-learning-models **Original patent:** https://patents.google.com/patent/US20260065072 --- _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: - [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. - [How Distributed AI Models Learn Privately on Edge Devices](https://patentbrief.org/patent/us/20260197424/distributed-deep-learning-using-a-distributed-deep-neural-network-20260197424) — 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. - [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. - [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. - [Training AI Models Together with Unlabeled Data Using a Teacher](https://patentbrief.org/patent/us/20220012637/federated-teacher-student-machine-learning) — This patent describes a way for multiple AI systems to learn together from data that hasn't been manually labeled, using a 'teacher' AI to create temporary labels for a 'student' AI.