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).
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Patent number: 11681284Abstract: 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: GrantFiled: November 11, 2021Date of Patent: June 20, 2023Assignee: SAP SEInventors: Cahit Bagdelen, Atreju Florian Tauschinsky
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Patent number: 11579588Abstract: 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: GrantFiled: July 30, 2018Date of Patent: February 14, 2023Assignee: SAP SEInventors: Atreju Florian Tauschinsky, Stefan Kain, Robert Meusel
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Publication number: 20230037829Abstract: 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: ApplicationFiled: November 11, 2021Publication date: February 9, 2023Inventors: Cahit Bagdelen, Atreju Florian Tauschinsky
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Patent number: 10915391Abstract: 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: GrantFiled: June 27, 2019Date of Patent: February 9, 2021Assignee: SAP SEInventors: Robert Meusel, Jaakob Kind, Atreju Florian Tauschinsky, Janick Frasch, Minji Lee, Michael Otto
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Patent number: 10824498Abstract: 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: GrantFiled: December 14, 2018Date of Patent: November 3, 2020Assignee: SAP SEInventors: Jaakob Kind, Uta Maria Loesch, Atreju Florian Tauschinsky
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Patent number: 10749881Abstract: 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: GrantFiled: June 29, 2017Date of Patent: August 18, 2020Assignee: SAP SEInventors: Atreju Florian Tauschinsky, Robert Meusel, Oliver Frendo
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Publication number: 20200192737Abstract: 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: ApplicationFiled: December 14, 2018Publication date: June 18, 2020Applicant: SAP SEInventors: Jaakob Kind, Uta Maria Loesch, Atreju Florian Tauschinsky
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Publication number: 20200033831Abstract: 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: ApplicationFiled: July 30, 2018Publication date: January 30, 2020Inventors: Atreju Florian Tauschinsky, Stefan Kain, Robert Meusel
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Publication number: 20190317848Abstract: 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: ApplicationFiled: June 27, 2019Publication date: October 17, 2019Inventors: Robert Meusel, Jaakob Kind, Atreju Florian Tauschinsky, Janick Frasch, Minji Lee, Michael Otto
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Patent number: 10379933Abstract: 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: GrantFiled: March 20, 2017Date of Patent: August 13, 2019Assignee: SAP SEInventors: Robert Meusel, Jaakob Kind, Atreju Florian Tauschinsky, Janick Frasch, Minji Lee, Michael Otto
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Publication number: 20190007432Abstract: 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: ApplicationFiled: June 29, 2017Publication date: January 3, 2019Inventors: Atreju Florian Tauschinsky, Robert Meusel, Oliver Frendo
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Publication number: 20180374104Abstract: 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: ApplicationFiled: June 26, 2017Publication date: December 27, 2018Inventors: Robert Meusel, Atreju Florian Tauschinsky, Christine Preisach
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Publication number: 20180239662Abstract: 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: ApplicationFiled: March 20, 2017Publication date: August 23, 2018Inventors: Robert Meusel, Jaakob Kind, Atreju Florian Tauschinsky, Janick Frasch, Minji Lee, Michael Otto