Far Edge IoT Design and Apparatus for Human Operators Assistance

ChangeBridge: Patent Grants - Business Methods (G06Q)

Published March 31st, 2026

Detected March 31st, 2026

Summary

The USPTO granted Dell Products L.P. Patent US12591791B2 covering far edge device data collection using IoT sensors, multi-stage feedback loops for processing, and inter-device learning exchange via a central manager interface. The patent includes 18 claims and provides the assignee exclusive rights to the claimed inventions in the field of distributed edge computing and machine learning.

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What changed

USPTO granted Patent US12591791B2 to Dell Products L.P., filed August 25, 2022 (Application No. 17822340), covering a method for far edge devices to collect IoT data, process it through multi-stage feedback loops, and share learned information across devices. The technology enables decentralized machine learning where edge devices update their algorithms based on learnings from other devices in the network.

This is a standard patent grant announcement and creates no compliance obligations. Competitors developing similar far edge IoT systems with inter-device learning capabilities should review the 18 claims to assess potential infringement exposure. No immediate action is required as this is a grant of existing rights rather than a new regulatory requirement.

Source document (simplified)

← USPTO Patent Grants

Far edge/IOT intelligence design and apparatus for human operators assistance

Grant US12591791B2 Kind: B2 Mar 31, 2026

Assignee

Dell Products L.P.

Inventors

Aurelian Dumitru, Eric L. Caron, Nalinkumar Mistry

Abstract

One example method is performed at a far edge device and includes collecting data with one or more IoT (Internet of Things) devices, feeding the data to a feedback loop that includes multiple stages, running the feedback loop, providing learning information, comprising output from one or more of the stages of the feedback loop, to a central manager by way of a learn interface, accessing learning information generated by one or more other far edge devices, and updating the feedback loop using the learning information generated by the one or more other far edge devices.