Patents by Inventor Guvenc Degirmenci

Guvenc Degirmenci 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).

  • Publication number: 20240111831
    Abstract: A multitenant solver execution service provides managed infrastructure for defining and solving large-scale optimization problems. In embodiments, the service executes solver jobs on managed compute resources such as virtual machines or containers. The compute resources can be automatically scaled up or down based on client demand and are assigned to solver jobs in a serverless manner. Solver jobs can be initiated based on configured triggers. In embodiments, the service allows users to select from different types of solvers, mix different solvers in a solver job, and translate a model from one solver to another solver. In embodiments, the service provides developer interfaces to, for example, run solver experiments, recommend solver types or solver settings, and suggest model templates. The solver execution service relieves developers from having to manage infrastructure for running optimization solvers and allows developers to easily work with different types of solvers via a unified interface.
    Type: Application
    Filed: September 29, 2022
    Publication date: April 4, 2024
    Applicant: Amazon Technologies, Inc.
    Inventors: Shreyas Vathul Subramanian, Amey K Dhavle, Guvenc Degirmenci, Kai Fan Tang, Daniel Romero
  • Publication number: 20240111832
    Abstract: A multitenant solver execution service provides managed infrastructure for defining and solving large-scale optimization problems. In embodiments, the service executes solver jobs on managed compute resources such as virtual machines or containers. The compute resources can be automatically scaled up or down based on client demand and are assigned to solver jobs in a serverless manner. Solver jobs can be initiated based on configured triggers. In embodiments, the service allows users to select from different types of solvers, mix different solvers in a solver job, and translate a model from one solver to another solver. In embodiments, the service provides developer interfaces to, for example, run solver experiments, recommend solver types or solver settings, and suggest model templates. The solver execution service relieves developers from having to manage infrastructure for running optimization solvers and allows developers to easily work with different types of solvers via a unified interface.
    Type: Application
    Filed: September 29, 2022
    Publication date: April 4, 2024
    Applicant: Amazon Technologies, Inc.
    Inventors: Shreyas Vathul Subramanian, Amey K Dhavle, Guvenc Degirmenci, Kai Fan Tang, Daniel Romero
  • Publication number: 20240112067
    Abstract: A multitenant solver execution service provides managed infrastructure for defining and solving large-scale optimization problems. In embodiments, the service executes solver jobs on managed compute resources such as virtual machines or containers. The compute resources can be automatically scaled up or down based on client demand and are assigned to solver jobs in a serverless manner. Solver jobs can be initiated based on configured triggers. In embodiments, the service allows users to select from different types of solvers, mix different solvers in a solver job, and translate a model from one solver to another solver. In embodiments, the service provides developer interfaces to, for example, run solver experiments, recommend solver types or solver settings, and suggest model templates. The solver execution service relieves developers from having to manage infrastructure for running optimization solvers and allows developers to easily work with different types of solvers via a unified interface.
    Type: Application
    Filed: September 29, 2022
    Publication date: April 4, 2024
    Applicant: Amazon Technologies, Inc.
    Inventors: Shreyas Vathul Subramanian, Amey K Dhavle, Guvenc Degirmenci, Kai Fan Tang, Daniel Romero