Framework for anomaly detection and resolution prediction
AssigneeDell Products L.P.
InventorsKanika Kapish, Hung Dinh, Bijan Kumar Mohanty, Rômulo Teixeira de Abreu Pinho
Life of this patent
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Abstract
A method comprises collecting operational data for one or more devices and identifying one or more anomalies associated with the one or more devices based at least in part on the collected operational data. At least a portion of the collected operational data corresponding to the identified one or more anomalies is analyzed, and a probability of automatic resolution for respective ones of the identified one or more anomalies is determined based at least in part on the analysis. The identifying, the analyzing and the determining are performed using one or more machine learning models.
Claims20
An apparatus comprising:
- at least one processing platform comprising a plurality of processing devices;
- said at least one processing platform being configured:
- to receive operational data for one or more devices, wherein the operational data identifies one or more anomalies associated with the one or more devices;
- to open one or more data streams corresponding to the one or more anomalies based at least in part on the operational data;
- to analyze at least a portion of the operational data corresponding to the one or more anomalies;
- to determine, using one or more machine learning models, probabilities of automatic resolution for respective ones of the one or more anomalies based at least in part on the analysis;
- to generate one or more support requests for at least a subset of the one or more anomalies based at least in part on the determined probabilities;
- to determine that at least one anomaly of the one or more anomalies has been resolved;
- and
- to close a portion of the one or more data streams corresponding to the at least one anomaly.
A method comprising:
- receiving operational data for one or more devices, wherein the operational data identifies one or more anomalies associated with the one or more devices;
- opening one or more data streams corresponding to the one or more anomalies based at least in part on the operational data;
- analyzing at least a portion of the operational data corresponding to the one or more anomalies;
- determining, using one or more machine learning models, probabilities of automatic resolution for respective ones of the one or more anomalies based at least in part on the analysis;
- generating one or more support requests for at least a subset of the one or more anomalies based at least in part on the determined probabilities;
- determining that at least one anomaly of the one or more anomalies has been resolved;
- and
- closing a portion of the one or more data streams corresponding to the at least one anomaly;
- wherein the method is performed by at least one processing platform comprising at least one processing device comprising a processor coupled to a memory.
A computer program product comprising 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 platform causes said at least one processing platform:
- to receive operational data for one or more devices, wherein the operational data identifies one or more anomalies associated with the one or more devices;
- to open one or more data streams corresponding to the one or more anomalies based at least in part on the operational data;
- to analyze at least a portion of the operational data corresponding to the one or more anomalies;
- to determine, using one or more machine learning models, probabilities of automatic resolution for respective ones of the one or more anomalies based at least in part on the analysis;
- to generate one or more support requests for at least a subset of the one or more anomalies based at least in part on the determined probabilities;
- to determine that at least one anomaly of the one or more anomalies has been resolved;
- and
- to close a portion of the one or more data streams corresponding to the at least one anomaly.
Dependencies are inferred from the claim text.