Patents by Inventor Markus Michael Geipel

Markus Michael Geipel 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: 20220147034
    Abstract: A device obtains a set of time series data monitored on a machine and further obtains first label information indicating a first time window in the time series data. The device determines a first probabilistic model, describing dynamics of the time series data inside the first time window, and a second probabilistic model describing dynamics of the time series data adjacent to the first time window. Based on the first and second probabilistic models, the device determines a first part of the time series data that is estimated to match the first probabilistic model and a second part of the time series data that is estimated to match the second probabilistic model, e.g., using a hidden Markov model. The device then determines second label information indicating a second time window which includes the first part of the time series data and excludes the second part of the time series data.
    Type: Application
    Filed: February 17, 2020
    Publication date: May 12, 2022
    Applicant: Siemens Energy Global GmbH & Co. KG
    Inventors: Markus Michael Geipel, Nikou Günnemann-Gholizadeh, Stephan Merk, Sebastian Mittelstädt
  • Patent number: 11288805
    Abstract: A computer-implemented method and a data processing apparatus provide and apply a trained probabilistic graphical model for verifying and/or improving the consistency of labels within the scope of medical image processing. Also provided are a computer-implemented method for verifying and/or improving the consistency of labels within the scope of medical imaging processing, a data processing apparatus embodied to verify and/or improve the consistency of labels within the scope of medical image processing, and a corresponding computer program product and a computer-readable medium.
    Type: Grant
    Filed: April 1, 2020
    Date of Patent: March 29, 2022
    Assignee: Siemens Healthcare Diagnostics Inc.
    Inventors: Markus Michael Geipel, Florian Büttner, Gaby Marquardt, Daniela Seidel, Christoph Tietz
  • Patent number: 11269297
    Abstract: In order to control a technical system by means of control model a data container is received, in which data container a control model having a training structure and model type information are encoded over all the model types. One of multiple model-type specific execution modules is selected for the technical system as a function of the model type information. Furthermore, operating data channels of the technical system are assigned input channels of the control model as a function of the model type information. Operating data of the technical system are acquired via a respective operating data channel and are transferred to the control model via an input channel assigned to this operating data channel. The control model is executed by means of the selected execution module, wherein control data are derived from the transferred operating data according to the training structure and are output to control the technical system.
    Type: Grant
    Filed: February 1, 2017
    Date of Patent: March 8, 2022
    Assignee: SIEMENS AKTIENGESELLSCHAFT
    Inventors: Siegmund Düll, Markus Michael Geipel, Jean-Christoph Heyne, Volkmar Sterzing
  • Publication number: 20220012531
    Abstract: The aim of the invention is to configure an image analysis device (BA). This is achieved in that a plurality of training images (TPIC) assigned to an object type (OT) and an object sub-type (OST) are fed into a first neural network module (CNN) in Order to detect image features. Furthermore, training output data sets (FEA) of the first neural network module (CNN) are fed into a second neural network module (MLP) in Order to detect object types using image features. According to the invention, the first and second neural network module (CNN, MLP) are trained together such that training output data sets (OOT) of the second neural network module (MLP) at least approximately reproduce the object types (OT) assigned to the training images (TPIC).
    Type: Application
    Filed: September 16, 2019
    Publication date: January 13, 2022
    Inventors: Markus Michael Geipel, Florian Büttner, Christoph Tietz, Gaby Marquardt, Daniela Seidel
  • Publication number: 20210365000
    Abstract: An apparatus for monitoring an actuator system, a method for providing an apparatus for monitoring an actuator system, and a method for monitoring an actuator system where the has at least one actuator and at least one data output signal. An anomaly detector detects anomalies. A suppressing engine determines time periods in which a control intervention has been performed. In a resulting monitoring signal, only anomalies are indicated which do not overlap with time periods in which the control intervention has been performed resulting in less irrelevant alerts and false positives output to a human supervisor monitoring the actuator system. The apparatus for monitoring a system may be provided with a plurality of actuators that may affect one another over time. The apparatus may be applied to a system of submersible pumps, or a system of conveyor belts.
    Type: Application
    Filed: January 11, 2019
    Publication date: November 25, 2021
    Applicant: Siemens Aktiengesellschaft
    Inventors: Sebastian Mittelstädt, Markus Michael Geipel, Klaus Arthur Schmid, Klaus-Peter Hitzel, Thomas Runkler, Michael Schnurbusch
  • Publication number: 20210201151
    Abstract: To train a machine learning routine (BNN), a sequence of first training data (PIC) is read in through the machine learning routine. The machine learning routine is trained using the first training data, wherein a plurality of learning parameters (LP) of the machine learning routine is set by the training. Furthermore, a value distribution (VLP) of the learning parameters, which occurs during the training, is determined and a continuation signal (CN) is generated on the basis of the determined value distribution of the learning parameters. Depending on the continuation signal, the training is then continued with a further sequence of the first training data or other training data (PIC2) are requested for the training.
    Type: Application
    Filed: July 29, 2019
    Publication date: July 1, 2021
    Inventors: Markus Michael Geipel, Stefan Depeweg, Christoph Tietz, Gaby Marquardt, Daniela Seidel
  • Patent number: 10983485
    Abstract: In order to control a technical system, e.g. of a wind turbine, a temporal sequence of operating parameter values of the technical system that is continuously recorded and continuously converted into a sequence of filtered signal values by a trainable digital filter. The sequence of the filtered signal values is supplied to a mechanical learning routine which derives prediction values therefrom for a target operating parameter. The digital filter and the mechanical learning routine are trained to reduce a distance between derived prediction values and temporally corresponding, actually recorded values of the target operation parameter. The prediction values for controlling the technical system are then emitted.
    Type: Grant
    Filed: February 6, 2018
    Date of Patent: April 20, 2021
    Inventors: Alexander Hentschel, Markus Michael Geipel
  • Publication number: 20200320709
    Abstract: The present invention relates to a computer-implemented method and a data processing apparatus for providing and applying a trained probabilistic graphical model for verifying and/or improving the consistency of labels within the scope of medical image processing, the use of the model for verifying and/or improving the consistency of labels within the scope of medical image processing, a computer-implemented method for verifying and/or improving the consistency of labels within the scope of medical imaging processing, a data processing apparatus embodied to verify and/or improve the consistency of labels within the scope of medical image processing, and a corresponding computer program product and a computer-readable medium.
    Type: Application
    Filed: April 1, 2020
    Publication date: October 8, 2020
    Inventors: Markus Michael Geipel, Florian Büttner, Gaby Marquardt, Daniela Seidel, Christoph Tietz
  • Publication number: 20200301375
    Abstract: In order to control a technical system by means of control model a data container is received, in which data container a control model having a training structure and model type information are encoded over all the model types. One of multiple model-typespecific execution modules is selected for the technical system as a function of the model type information. Furthermore, operating data channels of the technical system are assigned input channels of the control model as a function of the model type information. Operating data of the technical system are acquired via a respective operating data channel and are transferred to the control model via an input channel assigned to this operating data channel. The control model is executed by means of the selected execution module, wherein control data are derived from the transferred operating data according to the training structure and are output to control the technical system.
    Type: Application
    Filed: February 1, 2017
    Publication date: September 24, 2020
    Inventors: Siegmund Düll, Markus Michael Geipel, Jean-Christoph Heyne, Volkmar Sterzing
  • Publication number: 20200192304
    Abstract: In order to control a technical system, e.g. of a wind turbine, a temporal sequence of operating parameter values of the technical system that is continuously recorded and continuously converted into a sequence of filtered signal values by a trainable digital filter. The sequence of the filtered signal values is supplied to a mechanical learning routine which derives prediction values therefrom for a target operating parameter. The digital filter and the mechanical learning routine are trained to reduce a distance between derived prediction values and temporally corresponding, actually recorded values of the target operation parameter. The prediction values for controlling the technical system are then emitted.
    Type: Application
    Filed: April 4, 2017
    Publication date: June 18, 2020
    Inventors: Alexander Hentschel, Markus Michael Geipel
  • Publication number: 20170091347
    Abstract: In the method for modeling a technical system, a semantic system model of the technical system is generated and the dependencies inside the system model are analyzed by a dependency analysis based on properties of the semantic system model.
    Type: Application
    Filed: September 28, 2016
    Publication date: March 30, 2017
    Inventors: Markus Michael Geipel, Steffen Lamparter, Martin Ringsquandl