Eric L Forbes

US Patent:

20220391917, Dec 8, 2022

Filed:

Jun 2, 2021

Appl. No.:

17/336396

Inventors:

- Atlanta GA, US
Al Hooshiari - Alpharetta GA, US
Dan Celenti - Holmdel NJ, US
Eric Forbes - Canton GA, US

Assignee:

AT&T Intellectual Property I, L.P. - Atlanta GA

International Classification:

G06Q 30/00
G06Q 10/06
G06N 5/00
G06N 20/00

Abstract:

Concepts and technologies disclosed herein are directed to interpretation workflows for machine learning-enabled event tree-based diagnostic and customer problem resolution. According to one aspect, a system can receive a workflow construction specification derived from a machine learning-enabled event tree (“MLET”). The MLET can be generated for use by a customer service agent to resolve a customer problem. The workflow construction specification can include a plurality of objects, each of which represents a navigation path through the MLET. The system can traverse the workflow construction specification and can create a set of workflow creation commands based upon at least one policy. The system can generate a workflow visualization interpretation file based upon the set of workflow creation commands. The workflow visualization interpretation file can identify how the MLET derived a root cause of the customer problem. The system can then present the workflow visualization interpretation file to the customer service agent.

US Patent:

20200394576, Dec 17, 2020

Filed:

Jun 11, 2019

Appl. No.:

16/437074

Inventors:

- Atlanta GA, US
Alireza Hooshiari - Alpharetta GA, US
Dan Celenti - Holmdel NJ, US
Eric Forbes - Canton GA, US

Assignee:

AT&T Intellectual Property I, L.P. - Atlanta GA

International Classification:

G06Q 10/06
G06N 20/00

Abstract:

Concepts and technologies disclosed herein are directed to a machine learning-enabled event tree (“MLET”) for rapid and accurate customer problem resolution. According to one aspect disclosed herein, a designer system can receive a customer problem to be modeled. The designer system can create, based upon input from a designer, a plurality of levels and a plurality of nodes for an MLET to be used to resolve the customer problem. The designer system can create, further based upon the input, a plurality of Boolean logic gates between the plurality of levels of the MLET. The designer system can obtain a plurality of machine learning models and, further based upon the input, can create a navigation controller to link the plurality of machine learning models to the plurality of nodes in the MLET. The designer system can save the MLET for the customer problem.