US 11,537,459 B2 · Automatically predicting device failure using machine learning techniques

Abstract

Methods, apparatus, and processor-readable storage media for automatically predicting device failure using machine learning techniques are provided herein. An example computer-implemented method includes obtaining telemetry data from at least one client device; predicting failure of at least a portion of the at least one client device by processing at least a portion of the telemetry data using a first set of one or more machine learning techniques; predicting lifespan information pertaining to at least a portion of the at least one client device by processing the predicted failure and at least a portion of the telemetry data using a second set of one or more machine learning techniques; and performing at least one automated action based at least in part on one or more of the predicted failure and the predicted lifespan information.

Claims20

Dependent claims

A computer-implemented method comprising:

obtaining telemetry data from at least one client device;
predicting failure of at least a portion of the at least one client device by processing at least a portion of the telemetry data using a first set of one or more machine learning techniques, wherein processing at least a portion of the telemetry data using the first set of one or more machine learning techniques comprises processing at least a portion of the telemetry data using one or more Bayes classifier algorithms in conjunction with one or more probabilistic supervised machine learning algorithms;
predicting lifespan information pertaining to at least a portion of the at least one client device by processing the predicted failure and at least a portion of the telemetry data using a second set of one or more machine learning techniques;
and
performing at least one automated action based at least in part on one or more of the predicted failure and the predicted lifespan information;
wherein the method is performed by at least one processing device comprising a processor coupled to a memory.

3 more elements

A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device:

to obtain telemetry data from at least one client device;
to predict failure of at least a portion of the at least one client device by processing at least a portion of the telemetry data using a first set of one or more machine learning techniques, wherein processing at least a portion of the telemetry data using the first set of one or more machine learning techniques comprises processing at least a portion of the telemetry data using one or more Bayes classifier algorithms in conjunction with one or more probabilistic supervised machine learning algorithms;
to predict lifespan information pertaining to at least a portion of the at least one client device by processing the predicted failure and at least a portion of the telemetry data using a second set of one or more machine learning techniques;
and
to perform at least one automated action based at least in part on one or more of the predicted failure and the predicted lifespan information.

2 more elements

Dependencies are inferred from the claim text.

Automatically predicting device failure using machine learning techniques

Life of this patent

Automatically predicting device failure using machine learning techniques

Life of this patent

Abstract

Claims20