Implementing machine learning to identify, monitor and safely allocate resources to perform a current activity

Implementing machine learning to identify, monitor and safely allocate resources to perform a current activity

In an approach to resource monitoring and allocation recommendation, a computer determines a work activity for a risk assessment. A computer determines one or more resources assigned to the work activity. A computer determines one or more current activities associated with the one or more resources...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: Daley, Stan Kevin, Hatfield, Jennifer M, Bender, Michael, Dhillon, Jill S, Fox, Jeremy R
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
PHYSICS
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Beschreibung
Zusammenfassung:In an approach to resource monitoring and allocation recommendation, a computer determines a work activity for a risk assessment. A computer determines one or more resources assigned to the work activity. A computer determines one or more current activities associated with the one or more resources assigned to the work activity. Based on the one or more resources assigned to the work activity and on the one or more current activities associated with the one or more resources assigned to the work activity, a computer determines a confidence level associated with successful completion of the work activity. A computer determines the confidence level does not exceed a pre-defined threshold. A computer determines one or more resource scenarios to improve the confidence level. A computer ranks the one or more resource scenarios. A computer generates a resource allocation recommendation based on the ranked one or more resource scenarios.