C/hca, Inc.

George Tucker

Janet McCallister

Sarah Dhane

Erica Williams

Christopher Anthony

Thomas Neal Payne

Dan Nguyen

James E Hicks

Tara Haines

Heather Fuller

Chigger Bynum

Scott Murray

Randy Scott Fagin

Edmund Stephen Jackson

Hannah Marshall

Megan McGee

Brooke Hamilton

Approaches are provided for a prediction model determining a prediction indicative of the authorized user being associated with at least one anomaly event. In one example, a computer system may receive data from different sources. For example, the system may receive data associated with use of one or more monitored units of an automated storage and retrieval location. The system may also receive request data associated with a request for execution of the monitored controlled unit by an authorized user to a target user. The prediction model of the system may utilize the received data to determine the prediction that the authorized user is associated with at least one anomaly event associated with a diversion of a monitored unit away from the target user. The system may then provide the prediction for presentation.

Utilizing predictive modeling to identify anomaly events

In some examples, there may be provided systems, devices, and methods for using data from disparate sources to determine whether diversion events have occurred. A diversion event may be when a monitored unit intended for a target user is diverted away from the target user.

Aggregating data to identify diversion events

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Orange Park, FL, United States of America

George Tucker