AI Dynamic Slice Modification Patent Application

ChangeBridge: Patent Apps - Networking (H04L)

Published September 26th, 2024

Detected March 27th, 2026

Summary

The USPTO has published a patent application (US20260089063A1) detailing methods for artificial intelligence assisted dynamic modification of network slices. The application was filed on September 26, 2024, by inventors Sharath Somashekar, Diego Andres Estrella Chavez, and Tejaswini S. Patil.

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What changed

The United States Patent and Trademark Office (USPTO) has published a new patent application, US20260089063A1, concerning "ARTIFICIAL INTELLIGENCE ASSISTED DYNAMIC SLICE MODIFICATION." The application, filed on September 26, 2024, describes systems and methods for dynamically updating network slice configurations based on machine learning models that compare actual performance to expected performance. This technology is intended to improve wireless communication efficiency and responsiveness.

This is a patent application, not a regulatory rule or guidance. Therefore, it does not impose any immediate compliance obligations or deadlines on regulated entities. However, companies involved in telecommunications, network management, and AI development may wish to review the application for potential future technological trends or intellectual property considerations. The application is publicly available for review and potential opposition.

Source document (simplified)

← USPTO Patent Applications

ARTIFICIAL INTELLIGENCE ASSISTED DYNAMIC SLICE MODIFICATION

Application US20260089063A1 Kind: A1 Mar 26, 2026

Inventors

Sharath Somashekar, Diego Andres Estrella Chavez, Tejaswini S. Patil

Abstract

Methods, devices, and systems related to dynamic modifications of slicing configuration are disclosed. In one example aspect, a method for wireless communication includes receiving, by a network server implemented as a slice orchestrator, information from a user device that is configured to operate using an instance of a network slice; determining, by the slice orchestrator based on one or more machine learning models, a difference between actual slice performance and expected slice performance according to the information; and dynamically updating, by the slice orchestrator, configuration information for the network slice according to the difference between the actual slice performance and the expected slice performance.