Running AI Models Locally on Edge Devices with Cloud Updates
This patent describes a system for deploying AI models to local edge devices, allowing them to run and learn from local data, with the ability to send updates back to a central cloud for further model improvement.
Original patent title: “Edge deployment of cloud-originated machine learning and artificial intelligence workloads”
This patent describes a system for deploying AI models to local edge devices, allowing them to run and learn from local data, with the ability to send updates back to a central cloud for further model improvement. Granted to Armada Systems in 2024 with 21 claims and 11 forward citations, and it is expected to expire in 2043.
Coverage
What does this patent actually cover?
This patent details a method where a containerized edge unit, placed at a remote location, can request and receive pre-trained AI or machine learning models from a central cloud. The edge unit then uses these models to process local sensor data, performing 'inference.' It sends batches of this inference information back to the cloud. The cloud uses this data to retrain or fine-tune the models, sending updated versions back to the edge unit. Crucially, the edge unit can then perform 'localized instruction tuning' on these updated models using its own local data store, adapting them for specific local tasks. For example, an edge unit at a factory could receive a general defect detection model, use it to analyze camera feeds of products on an assembly line, send data back to the cloud for model improvement, and then further fine-tune the model locally to recognize specific defects unique to that factory's production.
The gap
What does this patent NOT cover?
- Deploying AI models that are not pre-trained in a cloud environment.
- Edge units that cannot receive updated models from a cloud management platform.
- AI models that are not containerized for deployment on the edge unit.
- Systems where the edge unit does not perform localized instruction tuning on the updated model.
- Edge units that do not obtain sensor data streams at their local edge location.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The innovation lies in the hybrid approach: not only does the edge device run AI models locally and send data back for cloud retraining, but it also performs a final 'instruction tuning' step on the updated models using its own local data. This allows for highly specific, real-time adaptation of AI models to unique local conditions without needing constant cloud intervention for every minor adjustment.
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
Smart factories with AI-powered quality control systems.
Autonomous drones performing localized environmental monitoring.
Edge computing devices in retail for real-time customer behavior analysis.
Industrial IoT sensors with embedded AI for predictive maintenance.
Why it matters
The bigger picture
This patent addresses the growing need for AI processing at the 'edge' – closer to where data is generated, rather than sending everything to a distant cloud. This is crucial for applications requiring low latency, such as autonomous vehicles, industrial automation, and real-time monitoring, where delays can be unacceptable. It enables more efficient and responsive AI systems by combining the scalability of cloud training with the immediacy of edge processing.
Filed
September 5, 2023
Granted
July 9, 2024
Market context
Who's building on this
Companies in this space
Companies involved in edge AI solutions, cloud providers offering edge services (like AWS IoT Greengrass or Azure IoT Edge), and hardware manufacturers developing specialized edge computing devices are likely exploring or implementing similar architectures. Armada Systems Inc., the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is actively developing in this space.
Market impact
This patent is part of a broader trend towards distributed AI, enabling more responsive and efficient applications by processing data closer to its source. It supports the development of edge AI ecosystems, potentially reducing reliance on constant cloud connectivity for critical AI functions and opening up new possibilities for real-time decision-making in diverse environments.
Claim 1 — Plain English
What this patent covers
This patent details a method where a containerized edge unit, placed at a remote location, can request and receive pre-trained AI or machine learning models from a central cloud. The edge unit then uses these models to process local sensor data, performing 'inference.' It sends batches of this inference information back to the cloud. The cloud uses this data to retrain or fine-tune the models, sending updated versions back to the edge unit. Crucially, the edge unit can then perform 'localized instruction tuning' on these updated models using its own local data store, adapting them for specific local tasks. For example, an edge unit at a factory could receive a general defect detection model, use it to analyze camera feeds of products on an assembly line, send data back to the cloud for model improvement, and then further fine-tune the model locally to recognize specific defects unique to that factory's production.
The clever bit
The innovation lies in the hybrid approach: not only does the edge device run AI models locally and send data back for cloud retraining, but it also performs a final 'instruction tuning' step on the updated models using its own local data. This allows for highly specific, real-time adaptation of AI models to unique local conditions without needing constant cloud intervention for every minor adjustment.
What it does not cover
- Deploying AI models that are not pre-trained in a cloud environment.
- Edge units that cannot receive updated models from a cloud management platform.
- AI models that are not containerized for deployment on the edge unit.
- Systems where the edge unit does not perform localized instruction tuning on the updated model.
- Edge units that do not obtain sensor data streams at their local edge location.
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
22/40
Moderately cited
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
$150K – $479K
Midpoint $300K · 17.0 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). Running AI Models Locally on Edge Devices with Cloud Updates (U.S. Patent No. 12,033,006). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12033006/edge-deployment-of-cloud-originated-machine-learning-and-artificial-intelligence
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 Running AI Models Locally on Edge Devices with Cloud Updates cover?
This patent describes a system for deploying AI models to local edge devices, allowing them to run and learn from local data, with the ability to send updates back to a central cloud for further model improvement.
Who owns patent US 12033006?
Armada Systems owns this patent, granted in 2024.
When does this patent expire?
This patent is expected to expire on September 5, 2043, when the invention enters the public domain.
What is patent US 12033006 cited by?
This patent has been cited by 11 later patents that build on its ideas.
What problem does this patent solve?
This patent addresses the growing need for AI processing at the 'edge' – closer to where data is generated, rather than sending everything to a distant cloud. This is crucial for applications requiring low latency, such as autonomous vehicles, industrial automation, and real-time monitoring, where delays can be unacceptable. It enables more efficient and responsive AI systems by combining the scalability of cloud training with the immediacy of edge processing.
What does this patent NOT cover?
Deploying AI models that are not pre-trained in a cloud environment.
Same assignee
More from Armada Systems
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