Patents by Inventor Torsten Schiemenz

Torsten Schiemenz 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).

  • Automatic selection of variables for a machine-learning model
    Patent number: 11308418
    Abstract: Computer-implemented systems and methods for reducing an amount of computational resources consumed by a machine-learning model are provided. A machine-learning model is applied to a dataset to generate a first output. The machine-learning model includes a plurality of variables. Variables are iteratively removed from the machine-learning model, and for each iteration, the machine-learning model is applied with one or more variables removed from the dataset to generate a second output. For each iteration, the first and second outputs are compared. A subset of the removed variables having impact below a predetermined threshold on an output of the machine-learning model is determined based on the comparisons. An optimized machine-learning model that omits the subset of variables is applied to new data to generate an output for the new data.
    Type: Grant
    Filed: September 15, 2017
    Date of Patent: April 19, 2022
    Assignee: SAP SE
    Inventor: Torsten Schiemenz
  • Automatic Selection of Variables for a Machine-Learning Model
    Publication number: 20190087744
    Abstract: Computer-implemented systems and methods for reducing an amount of computational resources consumed by a machine-learning model are provided. A machine-learning model is applied to a dataset to generate a first output. The machine-learning model includes a plurality of variables. Variables are iteratively removed from the machine-learning model, and for each iteration, the machine-learning model is applied with one or more variables removed from the dataset to generate a second output. For each iteration, the first and second outputs are compared. A subset of the removed variables having impact below a predetermined threshold on an output of the machine-learning model is determined based on the comparisons. An optimized machine-learning model that omits the subset of variables is applied to new data to generate an output for the new data.
    Type: Application
    Filed: September 15, 2017
    Publication date: March 21, 2019
    Inventor: Torsten Schiemenz