# How Caterpillar Compresses Heavy Machinery Data Using Neural Networks > 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. - **Patent:** US 7664715 - **Original title:** Apparatus and method for compressing data, apparatus and method for analyzing data, and data management system - **Owner:** Caterpillar Japan Ltd - **Granted:** 2010 - **Status:** Public domain (expired) - **Times cited:** 0 - **Field:** mechanical, ai_ml, telecommunications, consumer_electronics ## What it does This patent describes a system to reduce the cost of sending data from remote construction machines to a central office. It uses a neural network to group similar operational data points into 'neurons' within a multi-dimensional space. Instead of sending every single sensor reading, the machine only sends the coordinates of these neurons, the average distance of data points to those neurons, and how often each neuron was 'hit' by incoming data. This allows the remote office to reconstruct the machine's behavior without needing the raw, high-bandwidth data stream. ## What it does NOT cover - Does not cover general-purpose data compression algorithms like ZIP or JPEG. - Does not cover supervised learning where the machine is trained on labeled data. - Does not cover transmission methods that do not rely on neuron-based model parameters. - Does not cover data processing that occurs entirely on an external server without local compression. ## The clever bit By using unsupervised learning to create a 'winning neuron' model, the system essentially creates a mathematical summary of machine behavior that adapts to the data, rather than using a fixed, rigid compression template. ## Real-world examples 1. Caterpillar Product Link remote monitoring systems 2. Remote diagnostic systems for heavy mining equipment 3. Fleet management software for construction machinery ## Why it matters In the mid-2000s, transmitting large amounts of telemetry data from remote job sites via satellite or cellular links was prohibitively expensive. This patent provided a way to maintain diagnostic visibility into heavy equipment health while drastically cutting the data volume, effectively making remote fleet management economically viable for companies like Caterpillar. ## Frequently asked questions ### What does How Caterpillar Compresses Heavy Machinery Data Using Neural Networks cover? 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. ### Who owns patent US 7664715? Caterpillar Japan Ltd owns this patent, granted in 2010. ### When does this patent expire? This patent has expired and is now in the public domain — anyone can use the invention freely. ### What problem does this patent solve? In the mid-2000s, transmitting large amounts of telemetry data from remote job sites via satellite or cellular links was prohibitively expensive. This patent provided a way to maintain diagnostic visibility into heavy equipment health while drastically cutting the data volume, effectively making remote fleet management economically viable for companies like Caterpillar. ### What does this patent NOT cover? Does not cover general-purpose data compression algorithms like ZIP or JPEG. **Full plain-English explainer:** https://patentbrief.org/patent/us/7664715/apparatus-and-method-for-compressing-data-apparatus-and-method-for-analyzing-data-and-data-management-system **Original patent:** https://patents.google.com/patent/US7664715 --- _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: - [AI System for Diagnosing Machines and Managing Data in Factories](https://patentbrief.org/patent/us/11126171/methods-and-systems-of-diagnosing-machine-components-using-neural-networks-and-h) — 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. - [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 to Automatically Expand Neural Networks by Adding New Nodes](https://patentbrief.org/patent/us/10832138/gpt-language-model-pre-training) — A method for growing artificial intelligence models by identifying underperforming parts of a network and adding new nodes based on the behavior of existing ones. - [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.