Patents by Inventor Frank Gruber

Frank Gruber 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: 20240186225
    Abstract: A method of fabricating a semiconductor device package includes: providing a die carrier; disposing at least one semiconductor die on the die carrier, the semiconductor die comprising at least one contact pad on a main face remote from the carrier; electrically connecting the semiconductor die or another electrical device with an electrical connector; applying an encapsulant above the semiconductor die, the die carrier, and the electrical connector; and screwing a metallic drilling screw through the encapsulant so that an end of the drilling screw contacts the electrical connector.
    Type: Application
    Filed: February 15, 2024
    Publication date: June 6, 2024
    Inventors: Thorsten Scharf, Thomas Bemmerl, Martin Gruber, Thorsten Meyer, Frank Singer
  • Patent number: 11955415
    Abstract: The semiconductor device package comprises a die carrier, at least one semiconductor die disposed on the carrier, the semiconductor die comprising at least one contact pad on a main face remote from the carrier, an encapsulant disposed above the semiconductor die, an electrical connector electrically connected with the contact pad, a drilling screw screwed through the encapsulant and connected with the electrical connector.
    Type: Grant
    Filed: June 28, 2021
    Date of Patent: April 9, 2024
    Assignee: Infineon Technologies Austria AG
    Inventors: Thorsten Scharf, Thomas Bemmerl, Martin Gruber, Thorsten Meyer, Frank Singer
  • Patent number: 11396825
    Abstract: A turbine diagnostic machine learning system builds one or more turbine engine performance models using one or more parameter or parameter characteristics. A model of turbine engine performance includes ranked parameters or parameter characteristics, the ranking of which is calculated by a model builder based upon a function of AIC, AUC and p-value, resulting in a corresponding importance rank. These raw parameters and raw parameter characteristics are then sorted according to their importance rank, and selected by a selection component to form one or more completed models. The one or more models are operatively coupled to one or more other models to facilitate further machine learning capabilities by the system.
    Type: Grant
    Filed: August 14, 2017
    Date of Patent: July 26, 2022
    Assignee: GENERAL ELECTRIC COMPANY
    Inventors: Anurag Agarwal, Rajesh Alla, Frank Gruber, Lorenzo Escriche
  • Publication number: 20190048740
    Abstract: A turbine diagnostic machine learning system builds one or more turbine engine performance models using one or more parameter or parameter characteristics. A model of turbine engine performance includes ranked parameters or parameter characteristics, the ranking of which is calculated by a model builder based upon a function of AIC, AUC and p-value, resulting in a corresponding importance rank. These raw parameters and raw parameter characteristics are then sorted according to their importance rank, and selected by a selection component to form one or more completed models. The one or more models are operatively coupled to one or more other models to facilitate further machine learning capabilities by the system.
    Type: Application
    Filed: August 14, 2017
    Publication date: February 14, 2019
    Inventors: Anurag Agarwal, Rajesh Alla, Frank Gruber, Lorenzo Escriche