Utilizing predictive modeling to identify anomaly events

US12131819B1 · kind B1 · utility

Assignee

• C/HCA, Inc. · US

Inventors

• Scott Murray
• Dan Nguyen · Tampa, US
• Janet McCallister · Tallahassee, US
• Tara Haines · Tallahassee, US
• Erica Williams · Cairo, US
• George Tucker · Orange Park, US
• Heather Fuller · Tallahassee, US
• Randy Fagin · Austin, US
• Thomas Neal Payne · Austin, US
• Sarah Dhane · Austin, US
• James E. Hicks · Newton, US
• Christopher Anthony · Franklin, US
• Chigger Bynum · Nashville, US
• Brooke Hamilton · Nashville, US
• Hannah Marshall · Asheville, US
• Megan McGee · Nashville, US
• Edmund Jackson · Nashville, US

Key dates

• Filing date: Mar 1, 2022
• Grant date: Oct 29, 2024
• Expiry date: Jan 24, 2043

Classification

• Technology area (CPC G): Physics
• CPC primary: G16H50/70
• WIPO field: Medical technology
• WIPO sector: Instruments

Abstract

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.