US 2025/0053870 A1 · SWITCHING MACHINE LEARNING FUNCTIONALITY BASED ON RESOURCE AVAILABILITY

Abstract

Systems, methods, apparatuses, and computer program products for a switching machine learning functionality based on resource availability. A method may include executing a machine learning functionality, feature, or model at a first network element. The method may also include monitoring a machine learning resource at the first network element that impacts performance of the machine learning functionality, feature, or model. The method may further include indicating, to a second network element based on the monitoring, a machine learning-related resource availability impacting performance of the machine learning functionality, feature, or model. In addition, the method may include receiving, from a third network element, a configuration for an action to be executed by the first network element based on the machine learning-related resource availability impacting performance of the machine learning functionality, feature, or model. Further, the method may include executing the action.

Claims20

Dependent claims

An apparatus, comprising:

at least one processor;
and
at least one memory comprising computer program code,
wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to
execute a machine learning functionality, feature, or model at the apparatus;
monitor a machine learning resource at the apparatus that impacts performance of the machine learning functionality, feature, or model;
indicate, to a first network entity based on the monitoring, a machine learning-related resource availability impacting performance of the machine learning functionality, feature, or model;
receive, from a second network entity, a configuration for an action to be executed by the apparatus based on the machine learning-related resource availability impacting performance of the machine learning functionality, feature, or model;
and
execute the action.

7 more elements

An apparatus, comprising:

at least one processor;
and
at least one memory comprising computer program code,
wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to
receive, from a first network entity, a machine learning-related resource availability impacting performance of a machine learning functionality, feature, or model executed by the first network element;
determine, based on the machine learning-related resource availability impacting performance of the machine learning functionality, feature, or model, a performance of the machine learning functionality, feature, or model;
and
transmit an indication to a second network entity or the first network entity indicating the performance of the machine learning functionality, feature, or model.

5 more elements

An apparatus, comprising:

at least one processor;
and
at least one memory comprising computer program code,
wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to
receive, from a first network entity, an indication indicating a performance of a machine learning functionality, feature, or model executed by a second network entity;
determine, based on the indication, an action to be executed by the second network entity based on the indication;
and
configure the second network entity to execute the action that complies with a machine learning-related resource availability impacting performance of the machine learning functionality, feature, or model at the second network entity.

5 more elements

Dependencies are inferred from the claim text.

Switching machine learning functionality based on resource availability

Life of this patent

Switching machine learning functionality based on resource availability

Life of this patent

Abstract

Claims20