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.
Original patent title: “Methods and systems of diagnosing machine components using neural networks and having bandwidth allocation”
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. Granted to Strong Force IoT Portfolio 2016 in 2021 with 20 claims and 3 forward citations, and it is expected to expire in 2038.
Key facts
Coverage
What does this patent actually cover?
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 (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 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.
The gap
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.
- 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.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
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.
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
Predictive maintenance systems in smart factories
Industrial IoT platforms for equipment monitoring
Condition monitoring systems for manufacturing robots
Sensor networks in power plants or refineries
Automated diagnostics for heavy machinery
Why it matters
The bigger picture
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.
Filed
December 21, 2018
Granted
September 21, 2021
Market context
Who's building on this
Companies in this space
Companies focused on Industrial IoT and predictive maintenance are actively developing and deploying similar technologies. Major players like Siemens, GE Digital, and PTC offer platforms that integrate AI for asset performance management and operational intelligence. Startups in the edge computing and industrial analytics space are also building solutions that incorporate intelligent data management for factory environments.
Market impact
This type of technology has driven the adoption of AI and machine learning in industrial settings, shifting maintenance from reactive repairs to proactive prediction. It enables more robust and reliable Industrial IoT deployments by addressing the practical challenges of network congestion and data overload. This has led to increased operational efficiency, reduced unplanned downtime, and a greater emphasis on data-driven decision-making in manufacturing and heavy industry.
Claim 1 — Plain English
What this patent covers
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.
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.
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.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Moderate
Citation count
12/40
Early citations
Claim breadth
13/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
20/20
Granted within 5 years
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
$88K – $281K
Midpoint $176K · 12.5 yr remaining · industry ×1.5
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
The original legal language
Original claims
20 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
JR., G. W. D., McGuckin, J. P., Cella, C. H., & Desai, M. (2021). AI System for Diagnosing Machines and Managing Data in Factories (U.S. Patent No. 11,126,171). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/11126171/methods-and-systems-of-diagnosing-machine-components-using-neural-networks-and-h
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 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.
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