# Training AI Models Across Different Computers > 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. - **Patent:** US 12574477 - **Original title:** Distributed deep learning using a distributed deep neural network - **Owner:** Deep Sentinel - **Granted:** 2026 - **Status:** Active - **Times cited:** 0 - **Field:** consumer_electronics, software, telecommunications, ai_ml ## What it does This patent details a method for distributed AI training. A central computer (first host system) trains an initial neural network using data that has already been filtered from multiple remote sources. This initial network is then sent to a remote computer (second host system). The remote computer further trains this network using its own private data, creating a customized version. This customized network is then installed and used on the remote computer to process its live data stream. Crucially, the remote computer sends updated coefficients back to the central computer, allowing the original network to be improved based on the private data insights gained remotely. For example, a central server could train a general security camera AI, send it to individual homes, where each home's camera further trains it on local activity, and then sends back anonymized updates to improve the central AI. ## What it does NOT cover - Training that only occurs on a single computer system. - Using a neural network that is not further trained on private, local data at the second host system. - Sending raw, unfiltered event data from the remote systems to the first host system. - Updating the central neural network without receiving updated coefficients from a remote system. - Training that does not involve evaluating the neural network at both the first and second host systems. ## The clever bit The innovation lies in the two-way learning process: a central model is improved by private, local data without that data ever leaving the local system, and the central model, in turn, helps filter data for the local system. This creates a continuous, privacy-preserving feedback loop for AI model improvement. ## Real-world examples 1. Security camera systems that learn local activity patterns. 2. Smart home devices that personalize their functions based on user behavior. 3. Industrial monitoring systems that adapt to specific machine behaviors. ## Why it matters This patent addresses the challenge of training powerful AI models while respecting data privacy. It enables AI systems, like those used in security or smart home devices, to learn from diverse, real-world data without needing to collect all that sensitive data in one central location. This approach is key for developing intelligent systems that can adapt to local conditions while maintaining a globally improved model. ## Frequently asked questions ### What does Training AI Models Across Different Computers cover? 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. ### Who owns patent US 12574477? Deep Sentinel owns this patent, granted in 2026. ### When does this patent expire? This patent is expected to expire on April 19, 2037, when the invention enters the public domain. ### What problem does this patent solve? This patent addresses the challenge of training powerful AI models while respecting data privacy. It enables AI systems, like those used in security or smart home devices, to learn from diverse, real-world data without needing to collect all that sensitive data in one central location. This approach is key for developing intelligent systems that can adapt to local conditions while maintaining a globally improved model. ### What does this patent NOT cover? Training that only occurs on a single computer system. **Full plain-English explainer:** https://patentbrief.org/patent/us/12574477/distributed-deep-learning-using-a-distributed-deep-neural-network **Original patent:** https://patents.google.com/patent/US12574477 --- _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. - [Training AI on Private Data Without Seeing It](https://patentbrief.org/patent/us/12518214/distributed-machine-learning-systems-including-generation-of-synthetic-data) — This patent describes a way to train artificial intelligence models using private data stored on many separate computers, by generating fake data that mimics the real data's patterns, so the private data itself never leaves its original location. - [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. - [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. - [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.