# 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: - [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. - [Making AI Smarter by Focusing on Unsure 'Nodes'](https://patentbrief.org/patent/us/12423586/training-nodes-of-a-neural-network-to-be-decisive) — This 2025 patent from D5AI LLC describes a way to train AI models more effectively by boosting the learning signal for 'nodes' that aren't making clear decisions on data. - [Automated AI for Adapting to New Data Without Retraining](https://patentbrief.org/patent/us/20230162023/system-and-method-for-automated-transfer-learning-with-domain-disentanglement) — This patent describes an automated system that builds artificial intelligence models capable of adapting to new, different data without needing full retraining, by learning to ignore irrelevant changes. - [How to Save and Reuse Skills Learned by Artificial Intelligence Hardware](https://patentbrief.org/patent/us/10410117/method-and-a-system-for-creating-dynamic-neural-function-libraries) — A method for capturing the internal settings of a neuromorphic AI chip after it learns a task, allowing that 'skill' to be exported and loaded onto another AI chip. - [How to Automatically Detect and Fix Changes in AI Model Data](https://patentbrief.org/patent/us/10599957/systems-and-methods-for-detecting-data-drift-for-data-used-in-machine-learning-m) — This patent describes a system that automatically notices when the real-world data an AI model sees changes, causing its predictions to become less accurate, and then fixes the model.