Managing AI and Machine Learning on Many Devices from the Cloud
This patent describes a system for remotely monitoring and updating artificial intelligence and machine learning programs running on a large collection of smart devices, like robots or sensors, from a central cloud system.
Original patent title: “Cloud-based fleet and asset management for edge computing of machine learning and artificial intelligence workloads”
This patent describes a system for remotely monitoring and updating artificial intelligence and machine learning programs running on a large collection of smart devices, like robots or sensors, from a central cloud system. Granted to Armada Systems in 2024 with 21 claims and 5 forward citations, and it is expected to expire in 2044.
Coverage
What does this patent actually cover?
This patent details a system for managing a 'fleet' of edge devices, which are small computers or sensors that perform tasks locally. It works by receiving monitoring information about AI or machine learning programs running on these edge compute units, as described in claimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1. It also collects status information from connected edge assets, such as cameras or environmental sensors. All this data is shown on a remote graphical user interface (GUI) in a cloud environment, allowing a user to see how a selected group of devices is performing. The user can then provide input through the GUI to update the configuration of an AI or machine learning workload on specific edge devices. For example, a user could tell a group of factory robots to update their object recognition model (a pre-trained ML model) to better identify new product defects, with the cloud system transmitting the necessary control information, as outlined in claim 1.
The gap
What does this patent NOT cover?
- Does not cover managing individual edge devices without a central cloud-based GUI for fleet-wide oversight and configuration.
- Does not cover systems where the AI/ML model updates are not initiated by a user configuration input received via a remote GUI.
- Does not cover edge device management where the updated configuration is not specifically for a pre-trained ML or AI model or network connectivity.
- Does not cover systems that only monitor edge devices without the capability to transmit control information for updated configurations.
- Does not cover local, on-device management of AI/ML workloads without interaction with a cloud computing environment.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The noveltynoveltyThe requirement that an invention be different from anything publicly known before its priority date.Read more → lies in providing a unified, cloud-based graphical interface to not only monitor a diverse fleet of edge devices and their AI workloads, but also to centrally push complex updates like local retraining, finetuning, or distributed learning orchestration back to specific devices or groups of devices.
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
Autonomous vehicle fleets updating their perception models
Smart factory robots receiving new defect detection algorithms
Remote sensor networks for environmental monitoring adjusting their data analysis models
Retail store cameras updating their customer behavior analytics AI
Industrial IoT deployments managing predictive maintenance models on machinery
Why it matters
The bigger picture
Managing vast numbers of smart devices, especially those running complex AI or machine learning tasks, is a significant challenge. This patent offers a structured way to centrally monitor and update these distributed systems. It is particularly relevant for industries deploying large-scale IoT (Internet of Things) solutions, where manual updates for each device are impractical. This approach helps ensure that AI models on edge devices remain accurate and efficient without needing physical access to each unit.
Filed
January 8, 2024
Granted
October 8, 2024
Market context
Who's building on this
Companies in this space
Major cloud providers like Amazon Web Services (AWS) with their IoT Greengrass, Microsoft Azure IoT Edge, and Google Cloud IoT Core are actively developing and offering similar fleet management capabilities for edge AI. Companies specializing in industrial automation and smart city infrastructure, such as Siemens and Cisco, are also building solutions that leverage these principles to manage their distributed intelligent systems. Armada Systems, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is directly involved in this space.
Market impact
This patent addresses a growing need in the market for scalable and efficient management of edge AI deployments. Its focus on centralized monitoring and remote configuration updates helps reduce operational costs and complexity for businesses deploying AI at the edge. This capability is becoming essential for enabling widespread adoption of intelligent IoT solutions across various sectors, from manufacturing to logistics, by allowing models to be continuously improved and adapted without extensive manual intervention.
Claim 1 — Plain English
What this patent covers
This patent details a system for managing a 'fleet' of edge devices, which are small computers or sensors that perform tasks locally. It works by receiving monitoring information about AI or machine learning programs running on these edge compute units, as described in claim 1. It also collects status information from connected edge assets, such as cameras or environmental sensors. All this data is shown on a remote graphical user interface (GUI) in a cloud environment, allowing a user to see how a selected group of devices is performing. The user can then provide input through the GUI to update the configuration of an AI or machine learning workload on specific edge devices. For example, a user could tell a group of factory robots to update their object recognition model (a pre-trained ML model) to better identify new product defects, with the cloud system transmitting the necessary control information, as outlined in claim 1.
The clever bit
The novelty lies in providing a unified, cloud-based graphical interface to not only monitor a diverse fleet of edge devices and their AI workloads, but also to centrally push complex updates like local retraining, finetuning, or distributed learning orchestration back to specific devices or groups of devices.
What it does not cover
- Does not cover managing individual edge devices without a central cloud-based GUI for fleet-wide oversight and configuration.
- Does not cover systems where the AI/ML model updates are not initiated by a user configuration input received via a remote GUI.
- Does not cover edge device management where the updated configuration is not specifically for a pre-trained ML or AI model or network connectivity.
- Does not cover systems that only monitor edge devices without the capability to transmit control information for updated configurations.
- Does not cover local, on-device management of AI/ML workloads without interaction with a cloud computing environment.
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
16/40
Early citations
Claim breadth
14/20
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
$94K – $300K
Midpoint $187K · 17.2 yr remaining · 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.
Claim text not yet imported for this patent
The original legal language
Original claims
21 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Prabhakara, J., Mishra, P. K., Swaminathan, A., & Nair, P. (2024). Managing AI and Machine Learning on Many Devices from the Cloud (U.S. Patent No. 12,111,744). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12111744/cloud-based-fleet-and-asset-management-for-edge-computing-of-machine-learning-an-12111744
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 Managing AI and Machine Learning on Many Devices from the Cloud cover?
This patent describes a system for remotely monitoring and updating artificial intelligence and machine learning programs running on a large collection of smart devices, like robots or sensors, from a central cloud system.
Who owns patent US 12111744?
Armada Systems owns this patent, granted in 2024.
When does this patent expire?
This patent is expected to expire on January 8, 2044, when the invention enters the public domain.
What is patent US 12111744 cited by?
This patent has been cited by 5 later patents that build on its ideas.
What problem does this patent solve?
Managing vast numbers of smart devices, especially those running complex AI or machine learning tasks, is a significant challenge. This patent offers a structured way to centrally monitor and update these distributed systems. It is particularly relevant for industries deploying large-scale IoT (Internet of Things) solutions, where manual updates for each device are impractical. This approach helps ensure that AI models on edge devices remain accurate and efficient without needing physical access to each unit.
What does this patent NOT cover?
Does not cover managing individual edge devices without a central cloud-based GUI for fleet-wide oversight and configuration.
Same assignee
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