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.
Original patent title: “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. Granted to Caterpillar Japan Ltd in 2010 with 6 claims, and it is now in the public domain.
Coverage
What does this patent actually cover?
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.
The gap
What does this patent 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.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
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.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Caterpillar Product Link remote monitoring systems
Remote diagnostic systems for heavy mining equipment
Fleet management software for construction machinery
Why it matters
The bigger picture
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.
Filed
April 28, 2005
Granted
February 16, 2010
Market context
Who's building on this
Companies in this space
Caterpillar remains the primary entity utilizing this specific approach for their proprietary fleet management suites. Modern industrial IoT platforms from companies like Komatsu and John Deere have since adopted similar edge-computing and data-reduction strategies to manage the massive influx of sensor data from modern 'smart' equipment.
Market impact
This patent helped formalize the 'edge computing' approach in heavy industry, where processing happens on the machine itself to save bandwidth. It enabled the transition from reactive maintenance to proactive, data-driven fleet management by making it affordable to monitor machines operating in remote or off-grid locations.
Claim 1 — Plain English
What this patent covers
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.
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.
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.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
This patent is in the public domain
See the Freedom to Build guide — what is free to use, what is not, and how to cite this patent.
PatentBrief Score
Impact Score
Limited data
Citation count
0/40
No citations yet
Claim breadth
4/20
Moderate scope
Recency
5/20
Granted 10–20 years ago
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$5K – $14K
Midpoint $9K · expired or expiring · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Patent Claims
0 independent claims · 1 dependent
Claims are the legal boundaries of the patent. An independent claim stands alone. A dependent claim adds limitations to its parent, narrowing — but not broadening — the scope.
The original legal language
Original claims
6 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Fujii, S., Vatchkov, G. L., Komatsu, K., & Murota, I. (2010). How Caterpillar Compresses Heavy Machinery Data Using Neural Networks (U.S. Patent No. 7,664,715). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/7664715/apparatus-and-method-for-compressing-data-apparatus-and-method-for-analyzing-data-and-data-management-system
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
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.
Patent monitoring