# AI System for Diagnosing Machines and Managing Data in Factories > This patent describes a system that uses artificial intelligence to detect problems in industrial machines while smartly adjusting how much data it collects and sends to avoid overwhelming the network. - **Patent:** US 11126171 - **Original title:** Methods and systems of diagnosing machine components using neural networks and having bandwidth allocation - **Owner:** Strong Force IoT Portfolio 2016 - **Granted:** 2021 - **Status:** Active - **Times cited:** 3 - **Field:** industrial_iot, ai_ml, telecommunications, manufacturing, software, consumer_electronics ## What it does The system collects data from various sensors connected to industrial machines, like a robot's vibration sensors or a factory line's temperature gauges. A 'data collector' gathers this information from a selected group of these sensors, called 'input channels,' based on a specific routine (Claim 1). An 'expert system analysis circuit' then uses a type of artificial intelligence called a 'neural network' (which could be probabilistic, time delay, or convolutional, as per Claim 1) to analyze these data streams and identify 'fault conditions' or problems with the machine components (Claim 1). Crucially, the system also monitors the total amount of data being collected, called the 'aggregate rate' (Claim 1). If this rate goes over the network's current capacity, known as the 'bandwidth allocation rate,' the system automatically asks the network for more bandwidth (Claim 1). Until more bandwidth is available, it can temporarily increase data storage capacity (Claim 1), or it might reduce the data by eliminating some collected information (Claim 2), deactivating monitoring points (Claim 4), or reducing how often data is sampled or its detail level (Claim 6). For example, it could detect an unusual vibration pattern in a motor and, if the network is busy, temporarily reduce the sampling rate of less critical sensors to ensure the critical vibration data gets through. ## What it does NOT cover - Does not cover systems that diagnose machine faults without using a neural network as part of their expert system analysis circuit. - Does not cover general data collection systems that do not dynamically monitor and request changes to network bandwidth allocation rates. - Does not cover systems that only detect faults but do not implement specific data reduction strategies like eliminating data, deactivating monitoring points, or modifying sampling parameters when bandwidth is exceeded. - Does not cover systems operating outside of an industrial environment or not focused on machine components. - Does not cover systems that simply drop data without first attempting to increase bandwidth allocation or manage data capacity. ## The clever bit The truly clever part is combining AI-driven fault detection with an adaptive system that actively manages network bandwidth and data collection in real-time. It doesn't just detect issues; it also ensures the data needed for detection can actually get where it needs to go, even under network strain, by intelligently requesting more bandwidth or reducing less critical data. ## Real-world examples 1. Predictive maintenance systems in smart factories 2. Industrial IoT platforms for equipment monitoring 3. Condition monitoring systems for manufacturing robots 4. Sensor networks in power plants or refineries 5. Automated diagnostics for heavy machinery ## Why it matters This technology is important because it helps factories prevent expensive machine breakdowns by catching problems early using AI. By intelligently managing data flow, it ensures that critical diagnostic information can be sent and analyzed even when network resources are limited, which is common in complex industrial settings. This capability can significantly reduce downtime and maintenance costs, making industrial operations more efficient and reliable. ## Frequently asked questions ### What does AI System for Diagnosing Machines and Managing Data in Factories cover? This patent describes a system that uses artificial intelligence to detect problems in industrial machines while smartly adjusting how much data it collects and sends to avoid overwhelming the network. ### Who owns patent US 11126171? Strong Force IoT Portfolio 2016 owns this patent, granted in 2021. ### When does this patent expire? This patent is expected to expire on December 21, 2038, when the invention enters the public domain. ### What is patent US 11126171 cited by? This patent has been cited by 3 later patents that build on its ideas. ### What problem does this patent solve? This technology is important because it helps factories prevent expensive machine breakdowns by catching problems early using AI. By intelligently managing data flow, it ensures that critical diagnostic information can be sent and analyzed even when network resources are limited, which is common in complex industrial settings. This capability can significantly reduce downtime and maintenance costs, making industrial operations more efficient and reliable. ### What does this patent NOT cover? Does not cover systems that diagnose machine faults without using a neural network as part of their expert system analysis circuit. **Full plain-English explainer:** https://patentbrief.org/patent/us/11126171/methods-and-systems-of-diagnosing-machine-components-using-neural-networks-and-h **Original patent:** https://patents.google.com/patent/US11126171 --- _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 Caterpillar Compresses Heavy Machinery Data Using Neural Networks](https://patentbrief.org/patent/us/7664715/apparatus-and-method-for-compressing-data-apparatus-and-method-for-analyzing-data-and-data-management-system) — A method for shrinking massive amounts of sensor data from construction equipment into small, efficient packets for cheaper wireless transmission by using neural network training. - [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. - [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. - [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.