Smart System Learns Industrial Machine Behavior
This patent describes a smart system that uses sensors and machine learning to predict outcomes like production volume or maintenance needs for industrial machines, based on industry-specific data.
Original patent title: “Systems and methods for learning data patterns predictive of an outcome”
This patent describes a smart system that uses sensors and machine learning to predict outcomes like production volume or maintenance needs for industrial machines, based on industry-specific data. Granted to Strong Force IoT Portfolio 2016 in 2024 with 24 claims and 3 forward citations, and it is expected to expire in 2042.
Coverage
What does this patent actually cover?
This patent covers a system designed for industrial settings. It uses multiple sensors to gather data about a machine's operation. A central controller then uses a machine learning component to analyze this data. This analysis helps the system learn patterns that predict specific outcomes, such as the machine's reaction rate, how much it produces, or if it needs maintenance. Crucially, the machine learning starts with a pre-built model, informed by industry feedback, and can also learn to select the most useful sensors for its predictions. For example, it could predict when a specific manufacturing machine will require a part replacement based on its vibration and temperature readings.
The gap
What does this patent NOT cover?
- Systems that do not use machine learning to analyze data patterns.
- Systems that do not learn predictive patterns for industrial machine outcomes.
- Systems that do not use industry-specific feedback to seed their learning models.
- Systems that do not learn a preferred subset of input sensors.
- Systems predicting outcomes unrelated to industrial machines, like weather.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The core innovation is seeding the machine learning model with industry-specific feedback and learning a preferred subset of sensors. This means the system doesn't start from scratch; it's guided by prior knowledge relevant to the specific industrial context, making its predictions more accurate and efficient from the outset.
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 factories
Process optimization in chemical plants
Production volume forecasting in manufacturing
Smart energy grid management
Why it matters
The bigger picture
This patent is relevant to the growing field of Industrial Internet of Things (IIoT). It provides a framework for making industrial processes more efficient and predictable by leveraging data analytics and machine learning. Such systems can help reduce downtime and optimize resource allocation in factories and other industrial environments.
Filed
December 7, 2022
Granted
September 24, 2024
Market context
Who's building on this
Companies in this space
Companies involved in industrial automation, IIoT platforms, and predictive analytics are actively developing similar technologies. Major players like Siemens, GE Digital, and Rockwell Automation, as well as numerous startups, are creating solutions that leverage machine learning for industrial optimization and maintenance.
Market impact
This patent is part of a broader trend enabling smarter, more data-driven industrial operations. It contributes to the development of predictive maintenance and process optimization solutions, which are increasingly essential for manufacturers seeking to improve efficiency, reduce costs, and maintain competitiveness in a global market.
Claim 1 — Plain English
What this patent covers
This patent covers a system designed for industrial settings. It uses multiple sensors to gather data about a machine's operation. A central controller then uses a machine learning component to analyze this data. This analysis helps the system learn patterns that predict specific outcomes, such as the machine's reaction rate, how much it produces, or if it needs maintenance. Crucially, the machine learning starts with a pre-built model, informed by industry feedback, and can also learn to select the most useful sensors for its predictions. For example, it could predict when a specific manufacturing machine will require a part replacement based on its vibration and temperature readings.
The clever bit
The core innovation is seeding the machine learning model with industry-specific feedback and learning a preferred subset of sensors. This means the system doesn't start from scratch; it's guided by prior knowledge relevant to the specific industrial context, making its predictions more accurate and efficient from the outset.
What it does not cover
- Systems that do not use machine learning to analyze data patterns.
- Systems that do not learn predictive patterns for industrial machine outcomes.
- Systems that do not use industry-specific feedback to seed their learning models.
- Systems that do not learn a preferred subset of input sensors.
- Systems predicting outcomes unrelated to industrial machines, like weather.
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
16/20
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 · 16.1 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.
Claim text not yet imported for this patent
The original legal language
Original claims
24 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. (2024). Smart System Learns Industrial Machine Behavior (U.S. Patent No. 12,099,911). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12099911/systems-and-methods-for-learning-data-patterns-predictive-of-an-outcome
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 Smart System Learns Industrial Machine Behavior cover?
This patent describes a smart system that uses sensors and machine learning to predict outcomes like production volume or maintenance needs for industrial machines, based on industry-specific data.
Who owns patent US 12099911?
Strong Force IoT Portfolio 2016 owns this patent, granted in 2024.
When does this patent expire?
This patent is expected to expire on December 7, 2042, when the invention enters the public domain.
What is patent US 12099911 cited by?
This patent has been cited by 3 later patents that build on its ideas.
What problem does this patent solve?
This patent is relevant to the growing field of Industrial Internet of Things (IIoT). It provides a framework for making industrial processes more efficient and predictable by leveraging data analytics and machine learning. Such systems can help reduce downtime and optimize resource allocation in factories and other industrial environments.
What does this patent NOT cover?
Systems that do not use machine learning to analyze data patterns.
Same assignee
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