US 12,431,239 B1
Utilizing predictive modeling to identify anomaly events
Scott Murray, Lemmesaw, GA (US); Dan Nguyen, Tampa, FL (US); Janet McCallister, Tallahassee, FL (US); Tara Haines, Tallahassee, FL (US); Erica Williams, Cairo, GA (US); George Tucker, Orange Park, FL (US); Heather Fuller, Tallahassee, FL (US); Randy Scott Fagin, Nashville, TN (US); Thomas Neal Payne, Austin, TX (US); Sarah Dhane, Austin, TX (US); James E. Hicks, Spring Hill, TN (US); Christopher Anthony, Franklin, TN (US); Chigger Bynum, Nashville, TN (US); Brooke Hamilton, Nashville, TN (US); Hannah Marshall, Asheville, NC (US); Megan McGee, Nashville, TN (US); and Edmund Jackson, Nashville, TN (US)
Assigned to C/HCA, Inc., Nashville, TN (US)
Filed by C/HCA, Inc., Nashville, TN (US)
Filed on Oct. 29, 2024, as Appl. No. 18/930,928.
Application 18/930,928 is a continuation of application No. 17/683,496, filed on Mar. 1, 2022, granted, now 12,131,819.
Application 17/683,496 is a continuation in part of application No. 16/564,767, filed on Sep. 9, 2019, granted, now 11,309,069, issued on Apr. 19, 2022.
Application 16/564,767 is a continuation in part of application No. 15/652,494, filed on Jul. 18, 2017, abandoned.
Claims priority of provisional application 63/157,555, filed on Mar. 5, 2021.
Claims priority of provisional application 62/729,242, filed on Sep. 10, 2018.
Claims priority of provisional application 62/363,615, filed on Jul. 18, 2016.
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 16/00 (2019.01); G16H 10/60 (2018.01); G16H 15/00 (2018.01); G16H 40/20 (2018.01)
CPC G16H 40/20 (2018.01) [G16H 10/60 (2018.01); G16H 15/00 (2018.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
receiving, by a computer system from one or more data sources, first data comprising a plurality of first data attributes associated with a monitored controlled unit of an automated storage and retrieval location, the automated storage and retrieval location comprising a storage unit in which is retained monitored controlled units, and the first data associated with a type of use involving the monitored controlled unit;
receiving, by the computer system from the one or more data sources, second data comprising a plurality of second data attributes corresponding to a request for the monitored controlled unit of the monitored controlled units retained in the automated storage and retrieval location, the request associated with an electronic record of a dependent user, and the second data associated with an order for the monitored controlled unit with respect to the dependent user;
receiving, by the computer system from the one or more data sources, third data comprising a plurality of third data attributes associated with an authorized user of a location that includes the automated storage and retrieval location;
determining, by a prediction model of the computer system, a prediction indicative of the authorized user being associated with at least one anomaly event, the prediction determined based at least in part on at least one of the first data, the second data, or the third data, and wherein the prediction model is trained based at least in part on features derived from at least one of the first data attributes, the second data attributes, or the third data attributes; and
providing, by the computer system, the prediction to a user device for presentation.