Patents by Inventor Sebastian Boblest

Sebastian Boblest 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: 20250013857
    Abstract: A method for generating program code which, when executed on a hardware platform, creates a neural network having a given architecture. In the method: for at least one layer and/or group of neurons, a non-linear activation function of the neurons in that layer and/or group is ascertained from the given architecture; possible values that can be assumed by the activation function are precalculated and stored in a lookup table; program code is generated which, for all neurons in the layer and/or group respectively: aggregates the inputs of the respective neuron to form an argument of the activation function in accordance with the given architecture, ascertains an index from this argument, under which the associated value of the activation function is stored in the lookup table for the respective layer or group, and ascertains the output of the neuron by retrieving the value from the lookup table with this index.
    Type: Application
    Filed: June 11, 2024
    Publication date: January 9, 2025
    Inventors: Duy Khoi Vo, Benjamin Wagner, Dennis Sebastian Rieber, Sebastian Boblest, Ulrik Hjort, Walid Hussien
  • Publication number: 20240320465
    Abstract: A method for providing a neural network on a data processing device. The method includes: ascertaining, from a set of implementation variants of the neural network, a subset with a plurality of implementation variants of the neural network, wherein each implementation variant of the subset cannot be improved with respect to any of main memory requirement, non-volatile memory requirement, and execution time, when executed on the data processing device, without impairing at least one of the other two, and the subset for each of main memory requirement, non-volatile memory requirement and execution time, when executed on the data processing device, contains at least one particular implementation variant that is optimal in this respect from the set of implementation variants; selecting one of the ascertained implementation variants according to a user input that specifies a selection from the subset; and storing the selected implementation variant in the data processing device.
    Type: Application
    Filed: March 5, 2024
    Publication date: September 26, 2024
    Inventors: Sebastian Boblest, Benjamin Wagner, Duy Khoi Vo, Ulrik Hjort, Dennis Sebastian Rieber, Walid Hussien
  • Patent number: 12008295
    Abstract: A method for creating a model of a technical system as a function of measured sensor data of the technical system. The method includes the following steps: initializing a symbolic regression problem. A list of mathematical functions is established, including at least one linear and/or non-linear function and/or at least a one-dimensional parameterizable characteristic curve. The at least one-dimensional characteristic curve is implemented by a Smoothed Grid Regression (SGR) model. Solving the symbolic regression problem with the aid of a genetic algorithm.
    Type: Grant
    Filed: January 25, 2021
    Date of Patent: June 11, 2024
    Assignee: ROBERT BOSCH GMBH
    Inventors: Andrej Junginger, Holger Ulmer, Jens Stefan Buchner, Patrick Engel, Sebastian Boblest
  • Patent number: 11803732
    Abstract: A device and a computer-implemented method for classifying data, in particular for a Controller Area Network or an automotive Ethernet network. A plurality of messages is received from a communications network. A message that has a predefined message type is selected for an input variable for an input model of a plurality of input models of an artificial neural network associated with the predefined message type. The input variable is determined as a function of the message, and in an output area of the artificial neural network a prediction is output that is usable for classifying the message as a function of the input variable, or a reconstruction of an input variable is output that is usable for classifying the message as a function of this input variable.
    Type: Grant
    Filed: January 14, 2020
    Date of Patent: October 31, 2023
    Assignee: ROBERT BOSCH GMBH
    Inventors: Markus Hanselmann, Holger Ulmer, Katharina Dormann, Thilo Strauss, Andrej Junginger, Jens Stefan Buchner, Sebastian Boblest
  • Publication number: 20210264075
    Abstract: A method for creating a model of a technical system as a function of measured sensor data of the technical system. The method includes the following steps: initializing a symbolic regression problem. A list of mathematical functions is established, including at least one linear and/or non-linear function and/or at least a one-dimensional parameterizable characteristic curve. The at least one-dimensional characteristic curve is implemented by a Smoothed Grid Regression (SGR) model. Solving the symbolic regression problem with the aid of a genetic algorithm.
    Type: Application
    Filed: January 25, 2021
    Publication date: August 26, 2021
    Inventors: Andrej Junginger, Holger Ulmer, Jens Stefan Buchner, Patrick Engel, Sebastian Boblest
  • Publication number: 20200234101
    Abstract: A device and a computer-implemented method for classifying data, in particular for a Controller Area Network or an automotive Ethernet network. A plurality of messages is received from a communications network. A message that has a predefined message type is selected for an input variable for an input model of a plurality of input models of an artificial neural network associated with the predefined message type. The input variable is determined as a function of the message, and in an output area of the artificial neural network a prediction is output that is usable for classifying the message as a function of the input variable, or a reconstruction of an input variable is output that is usable for classifying the message as a function of this input variable.
    Type: Application
    Filed: January 14, 2020
    Publication date: July 23, 2020
    Inventors: Markus Hanselmann, Holger Ulmer, Katharina Dormann, Thilo Strauss, Andrej Junginger, Jens Stefan Buchner, Sebastian Boblest