US 12,008,584 B2
Graph convolutional anomaly detection
Parker J. Erickson, Plymouth, MN (US); Gerald Liu, Maple Grove, MN (US); Rex Shen, Eden Prairie, MN (US); Devin Uner, Carpentersville, MN (US); George L Williams, Minnetonka, MN (US); Zachary Babcock, Elk River, MN (US); and Lydia M. Narum, St. Louis Park, MN (US)
Assigned to OPTUM, INC., Minnetonka, MN (US)
Filed by Optum, Inc., Minnetonka, MN (US)
Filed on Oct. 3, 2022, as Appl. No. 17/937,511.
Application 17/937,511 is a continuation of application No. 16/916,571, filed on Jun. 30, 2020, granted, now 11,494,787.
Prior Publication US 2023/0025252 A1, Jan. 26, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 30/018 (2023.01); G06F 16/23 (2019.01); G06F 16/901 (2019.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06Q 40/08 (2012.01)
CPC G06Q 30/0185 (2013.01) [G06F 16/2379 (2019.01); G06F 16/9024 (2019.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06Q 40/08 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
generating, based at least in part on related graph database input data, related graph feature data for a predictive entity, wherein the related graph feature data comprises a feature vector for each related graph database object of one or more related graph database objects associated with the predictive entity;
generating, based at least in part on the related graph feature data and using a graph convolutional neural network model, an anomaly detection score for the predictive entity, wherein at least a portion of the graph convolutional neural network model is trained using confirmation feedback data associated with a graph convolutional anomaly detection;
responsive to a determination to perform an anomaly confirmation with respect to the predictive entity:
performing the anomaly confirmation to generate a confirmation feedback data object for the predictive entity, and
integrating the confirmation feedback data object for the predictive entity into the confirmation feedback data associated with the graph convolutional anomaly detection; and
initiating the performance of one or more responsive actions based at least in part on the anomaly detection score.