Patents by Inventor Al Hooshiari

Al Hooshiari has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Interpretation workflows for machine learning-enabled event tree-based diagnostic and customer problem resolution
    Patent number: 12299695
    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.
    Type: Grant
    Filed: February 1, 2024
    Date of Patent: May 13, 2025
    Assignee: AT&T Intellectual Property I, L.P.
    Inventors: James Fan, Al Hooshiari, Dan Celenti, Eric Forbes
  • Interpretation Workflows for Machine Learning-Enabled Event Tree-Based Diagnostic and Customer Problem Resolution
    Publication number: 20240177173
    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.
    Type: Application
    Filed: February 1, 2024
    Publication date: May 30, 2024
    Applicant: AT&T Intellectual Property I, L.P.
    Inventors: James Fan, Al Hooshiari, Dan Celenti, Eric Forbes
  • Interpretation workflows for machine learning-enabled event tree-based diagnostic and customer problem resolution
    Patent number: 11893590
    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.
    Type: Grant
    Filed: June 2, 2021
    Date of Patent: February 6, 2024
    Assignee: AT&T Intellectual Property I, L.P.
    Inventors: James Fan, Al Hooshiari, Dan Celenti, Eric Forbes
  • Interpretation Workflows for Machine Learning-Enabled Event Tree-Based Diagnostic and Customer Problem Resolution
    Publication number: 20220391917
    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.
    Type: Application
    Filed: June 2, 2021
    Publication date: December 8, 2022
    Applicant: AT&T Intellectual Property I, L.P.
    Inventors: James Fan, Al Hooshiari, Dan Celenti, Eric Forbes