US 12,113,660 B1
Graphical user interface and alerting system for detecting and mitigating problems in an agent network
Jennifer Lee Hargrove, Raleigh, NC (US); Ellen Laura Mir, Chapel Hill, NC (US); John L. Maynard, Fancy Gap, VA (US); Karen D. Haralson, Palm Coast, FL (US); and Corey K. Kozak, Durham, NC (US)
Assigned to SAS INSTITUTE INC., Cary, NC (US)
Filed by SAS Institute Inc., Cary, NC (US)
Filed on Feb. 20, 2024, as Appl. No. 18/581,450.
Claims priority of provisional application 63/546,207, filed on Oct. 28, 2023.
Claims priority of provisional application 63/540,019, filed on Sep. 22, 2023.
Int. Cl. H04L 41/06 (2022.01); H04L 41/046 (2022.01); H04L 41/22 (2022.01)
CPC H04L 41/06 (2013.01) [H04L 41/046 (2013.01); H04L 41/22 (2013.01)] 30 Claims
OG exemplary drawing
 
1. A computing system of an entity, the computing system comprising:
one or more processors; and
one or more memories including program code that is executable by the one or more processors for causing the one or more processors to:
receive a plurality of sets of usage data from a plurality of agent computer systems associated with a plurality of agents, each agent computing system of the plurality of agent computing systems being associated with a corresponding agent of the plurality of agents and providing a corresponding set of usage data of the plurality of sets of usage data, wherein:
the plurality of agent computing systems are different from the computing system,
the plurality of agents are different from the entity,
the plurality of agent computing systems provide an infrastructure to support a plurality of service providers in providing one or more services to a plurality of service users,
the plurality of service providers are distinct from the plurality of agents, the entity, and the plurality of service users, and
the plurality of sets of usage data describe interactions between the plurality of service providers and the plurality of service users;
generate a corresponding set of metric values for a common set of metrics for each respective agent of the plurality of agents based on a corresponding set of usage data of the plurality of sets of usage data, wherein:
at least one metric value of the corresponding set of metric values is generated based on an output from a trained machine-learning model,
the common set of metrics are usable for all of the plurality of agents to detect anomalies related to the plurality of agents, and
the corresponding set of metric values for at least one agent of the plurality of agents is generated at least in part by normalizing the corresponding set of usage data from a first format to a second format;
generate a respective score for each respective agent of the plurality of agents based on the corresponding set of metric values, wherein the respective score indicates a respective risk level associated with the respective agent;
compare the respective score for each respective agent of the plurality of agents to a predefined threshold to identify one or more agents among the plurality of agents having respective scores that meet or exceed the predefined threshold; and
generate a graphical user interface indicating the one or more identified agents.