Patents by Inventor Atreju Florian Tauschinsky

Atreju Florian Tauschinsky 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).

  • Patent number: 11681284
    Abstract: The present disclosure relates to computer-implemented methods, software, and systems for predicting failure event occurrence for a machine asset. Run-to-failure sequences of time series data that include an occurrence of a failure event for the machine asset are received. One or more candidate cut-off values are determined based on iterative evaluation of a plurality of potential cut-off points. A candidate cut-off value is identified as substantially corresponding to a local peak point for calculated distances between relative frequency distributions of positive and negative sub-sequences. A failure prediction model is iteratively trained to iteratively extract sets of relevant features to determine a prediction horizon for an occurrence of the failure event for the machine asset. A candidate cut-off value associated with a model of highest quality from a set of failure prediction models determined during the iterations is selected to determine the prediction horizon for the machine asset.
    Type: Grant
    Filed: November 11, 2021
    Date of Patent: June 20, 2023
    Assignee: SAP SE
    Inventors: Cahit Bagdelen, Atreju Florian Tauschinsky
  • Patent number: 11579588
    Abstract: Methods, systems, and computer-readable storage media for receiving a time-series of data values associated with a plurality of sensors, each sensor generating at least a portion of the time-series of a respective data value, providing a plurality of auto-regression models, each auto-regression model being provided based on a respective first sub-set of the time-series of data values used as input, and a respective second sub-set of the time-series of data values used as training data during a training process, receiving respective data values associated with a time from and generated by each of the plurality of sensors, determining respective predicted values for each of the auto-regression models, and selectively indicating that an anomaly is present in the system based on respective predicted values for each of the auto-regression models, and the respective data values associated with a time.
    Type: Grant
    Filed: July 30, 2018
    Date of Patent: February 14, 2023
    Assignee: SAP SE
    Inventors: Atreju Florian Tauschinsky, Stefan Kain, Robert Meusel
  • Publication number: 20230037829
    Abstract: The present disclosure relates to computer-implemented methods, software, and systems for predicting failure event occurrence for a machine asset. Run-to-failure sequences of time series data that include an occurrence of a failure event for the machine asset are received. One or more candidate cut-off values are determined based on iterative evaluation of a plurality of potential cut-off points. A candidate cut-off value is identified as substantially corresponding to a local peak point for calculated distances between relative frequency distributions of positive and negative sub-sequences. A failure prediction model is iteratively trained to iteratively extract sets of relevant features to determine a prediction horizon for an occurrence of the failure event for the machine asset. A candidate cut-off value associated with a model of highest quality from a set of failure prediction models determined during the iterations is selected to determine the prediction horizon for the machine asset.
    Type: Application
    Filed: November 11, 2021
    Publication date: February 9, 2023
    Inventors: Cahit Bagdelen, Atreju Florian Tauschinsky
  • Patent number: 10915391
    Abstract: Some embodiments include reception of a time-series of a respective data value generated by each of a plurality of sensors, calculation of a regression associated with a first sensor of the plurality of sensors based on the received plurality of time-series, the regression being a function of the respective data values of the others of the plurality of data sources, reception of respective data values associated with a time from and generated by each the plurality of respective sensors, determination of a predicted value associated with the time for the first sensor based on the regression associated with the first sensor and on the respective data values associated with the time, comparison of the predicted value with the received value associated with the time and generated by the first sensor, and determination of a value indicating a likelihood of an anomaly based on the comparison.
    Type: Grant
    Filed: June 27, 2019
    Date of Patent: February 9, 2021
    Assignee: SAP SE
    Inventors: Robert Meusel, Jaakob Kind, Atreju Florian Tauschinsky, Janick Frasch, Minji Lee, Michael Otto
  • Patent number: 10824498
    Abstract: A method for multimodal failure analysis is provided herein. A multimodal failure analysis request may be received. An asset type may be determined based on the multimodal failure analysis request. Asset records for the asset type may be obtained. The asset records may include data on asset failures across multiple failure modes. A multimodal failure analytical model may be executed based on the asset records. Executing the multimodal failure analytical model may include calculating a distribution of failure intervals over time, probabilities of failure respectively associated with the failure intervals, and intervention scores respectively associated with the failure intervals. An intervention interval and an intervention score associated with the intervention interval may be selected based on the associated probabilities of failure. The selected intervention interval and intervention score may be provided in response to the multimodal failure analysis request.
    Type: Grant
    Filed: December 14, 2018
    Date of Patent: November 3, 2020
    Assignee: SAP SE
    Inventors: Jaakob Kind, Uta Maria Loesch, Atreju Florian Tauschinsky
  • Patent number: 10749881
    Abstract: Methods, systems, and computer-readable storage media for ranking anomaly detection algorithms, including operations of receiving a set of unlabeled data from one or more sensors in a plurality of sensors of an internet of things, generating a plurality of data distributions corresponding to the set of unlabeled data by using a plurality of anomaly detection algorithms, and ranking the plurality of anomaly detection algorithms relative to the set of unlabeled data based on a distance between a first quantile and a second quantile of each of the plurality of data distributions.
    Type: Grant
    Filed: June 29, 2017
    Date of Patent: August 18, 2020
    Assignee: SAP SE
    Inventors: Atreju Florian Tauschinsky, Robert Meusel, Oliver Frendo
  • Publication number: 20200192737
    Abstract: A method for multimodal failure analysis is provided herein. A multimodal failure analysis request may be received. An asset type may be determined based on the multimodal failure analysis request. Asset records for the asset type may be obtained. The asset records may include data on asset failures across multiple failure modes. A multimodal failure analytical model may be executed based on the asset records. Executing the multimodal failure analytical model may include calculating a distribution of failure intervals over time, probabilities of failure respectively associated with the failure intervals, and intervention scores respectively associated with the failure intervals. An intervention interval and an intervention score associated with the intervention interval may be selected based on the associated probabilities of failure. The selected intervention interval and intervention score may be provided in response to the multimodal failure analysis request.
    Type: Application
    Filed: December 14, 2018
    Publication date: June 18, 2020
    Applicant: SAP SE
    Inventors: Jaakob Kind, Uta Maria Loesch, Atreju Florian Tauschinsky
  • Publication number: 20200033831
    Abstract: Methods, systems, and computer-readable storage media for receiving a time-series of data values associated with a plurality of sensors, each sensor generating at least a portion of the time-series of a respective data value, providing a plurality of auto-regression models, each auto-regression model being provided based on a respective first sub-set of the time-series of data values used as input, and a respective second sub-set of the time-series of data values used as training data during a training process, receiving respective data values associated with a time from and generated by each of the plurality of sensors, determining respective predicted values for each of the auto-regression models, and selectively indicating that an anomaly is present in the system based on respective predicted values for each of the auto-regression models, and the respective data values associated with a time.
    Type: Application
    Filed: July 30, 2018
    Publication date: January 30, 2020
    Inventors: Atreju Florian Tauschinsky, Stefan Kain, Robert Meusel
  • Publication number: 20190317848
    Abstract: Some embodiments include reception of a time-series of a respective data value generated by each of a plurality of sensors, calculation of a regression associated with a first sensor of the plurality of sensors based on the received plurality of time-series, the regression being a function of the respective data values of the others of the plurality of data sources, reception of respective data values associated with a time from and generated by each the plurality of respective sensors, determination of a predicted value associated with the time for the first sensor based on the regression associated with the first sensor and on the respective data values associated with the time, comparison of the predicted value with the received value associated with the time and generated by the first sensor, and determination of a value indicating a likelihood of an anomaly based on the comparison.
    Type: Application
    Filed: June 27, 2019
    Publication date: October 17, 2019
    Inventors: Robert Meusel, Jaakob Kind, Atreju Florian Tauschinsky, Janick Frasch, Minji Lee, Michael Otto
  • Patent number: 10379933
    Abstract: Some embodiments include reception of a time-series of a respective data value generated by each of a plurality of sensors, calculation of a regression associated with a first sensor of the plurality of sensors based on the received plurality of time-series, the regression being a function of the respective data values of the others of the plurality of data sources, reception of respective data values associated with a time from and generated by each the plurality of respective sensors, determination of a predicted value associated with the time for the first sensor based on the regression associated with the first sensor and on the respective data values associated with the time, comparison of the predicted value with the received value associated with the time and generated by the first sensor, and determination of a value indicating a likelihood of an anomaly based on the comparison.
    Type: Grant
    Filed: March 20, 2017
    Date of Patent: August 13, 2019
    Assignee: SAP SE
    Inventors: Robert Meusel, Jaakob Kind, Atreju Florian Tauschinsky, Janick Frasch, Minji Lee, Michael Otto
  • Publication number: 20190007432
    Abstract: Methods, systems, and computer-readable storage media for ranking anomaly detection algorithms, including operations of receiving a set of unlabeled data from one or more sensors in a plurality of sensors of an internet of things, generating a plurality of data distributions corresponding to the set of unlabeled data by using a plurality of anomaly detection algorithms, and ranking the plurality of anomaly detection algorithms relative to the set of unlabeled data based on a distance between a first quantile and a second quantile of each of the plurality of data distributions.
    Type: Application
    Filed: June 29, 2017
    Publication date: January 3, 2019
    Inventors: Atreju Florian Tauschinsky, Robert Meusel, Oliver Frendo
  • Publication number: 20180374104
    Abstract: Methods, systems, and computer-readable storage media for automatically providing a predictive model for an asset made up of multiple sub-assets with actions including receiving asset data including data values associated with the asset and at least one of sub-asset of the multiple assets, providing, by the one or more processors, a set of features based on the asset data, and executing an iterative feature selection and supervised learning process, including, for each iteration: selecting a sub-set of features from the set of features, performing supervised learning over the sub-set of features to provide a predictive model, and determining an accuracy of the predictive model, the iterations are performed until the accuracy of the predictive model exceeds a threshold accuracy.
    Type: Application
    Filed: June 26, 2017
    Publication date: December 27, 2018
    Inventors: Robert Meusel, Atreju Florian Tauschinsky, Christine Preisach
  • Publication number: 20180239662
    Abstract: Some embodiments include reception of a time-series of a respective data value generated by each of a plurality of sensors, calculation of a regression associated with a first sensor of the plurality of sensors based on the received plurality of time-series, the regression being a function of the respective data values of the others of the plurality of data sources, reception of respective data values associated with a time from and generated by each the plurality of respective sensors, determination of a predicted value associated with the time for the first sensor based on the regression associated with the first sensor and on the respective data values associated with the time, comparison of the predicted value with the received value associated with the time and generated by the first sensor, and determination of a value indicating a likelihood of an anomaly based on the comparison.
    Type: Application
    Filed: March 20, 2017
    Publication date: August 23, 2018
    Inventors: Robert Meusel, Jaakob Kind, Atreju Florian Tauschinsky, Janick Frasch, Minji Lee, Michael Otto