Patents by Inventor Anna CHYSTIAKOVA
Anna CHYSTIAKOVA 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: 11948051Abstract: In one embodiment, a method for auditing the results of a machine learning model includes: retrieving a set of state estimates for original time series data values from a database under audit; reversing the state estimation computation for each of the state estimates to produce reconstituted time series data values for each of the state estimates; retrieving the original time series data values from the database under audit; comparing the original time series data values pairwise with the reconstituted time series data values to determine whether the original time series and reconstituted time series match; and generating a signal that the database under audit (i) has not been modified where the original time series and reconstituted time series match, and (ii) has been modified where the original time series and reconstituted time series do not match.Type: GrantFiled: March 23, 2020Date of Patent: April 2, 2024Assignee: ORACLE INTERNATIONAL CORPORATIONInventors: Edward R. Wetherbee, Kenneth P. Baclawski, Guang C. Wang, Kenny C. Gross, Anna Chystiakova, Dieter Gawlick, Zhen Hua Liu, Richard Paul Sonderegger
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Publication number: 20230376837Abstract: Systems, methods, and other embodiments associated with associated with dependency checking for machine learning (ML) models are described. In one embodiment, a method includes applying a repeating probe signal to an input signal input into a machine learning model. An estimate signal output from the machine learning model is monitored, and the repeating probe signal is checked for in the estimate signal. Based on the results of the checking for the repeating probe signal, an evaluation of dependency in the machine learning model is presented.Type: ApplicationFiled: May 23, 2022Publication date: November 23, 2023Inventors: Matthew T. GERDES, Kenneth P. BACLAWSKI, Dieter GAWLICK, Kenny C. GROSS, Guang Chao WANG, Anna CHYSTIAKOVA, Richard P. SONDEREGGER, Zhen Hua LIU
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Patent number: 11782429Abstract: The disclosed embodiments relate to a system that automatically adapts a prognostic-surveillance system to account for aging phenomena in a monitored system. During operation, the prognostic-surveillance system is operated in a surveillance mode, wherein a trained inferential model is used to analyze time-series signals from the monitored system to detect incipient anomalies. During the surveillance mode, the system periodically calculates a reward/cost metric associated with updating the trained inferential model. When the reward/cost metric exceeds a threshold, the system swaps the trained inferential model with an updated inferential model, which is trained to account for aging phenomena in the monitored system.Type: GrantFiled: July 7, 2021Date of Patent: October 10, 2023Assignee: Oracle International CorporationInventors: Richard P. Sonderegger, Kenneth P. Baclawski, Guang C. Wang, Anna Chystiakova, Dieter Gawlick, Zhen Hua Liu, Kenny C. Gross
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Publication number: 20230153680Abstract: Techniques for using machine learning model validated sensor data to generate recommendations for remediating issues in a monitored system are disclosed. A machine learning model is trained to identify correlations among sensors for a monitored system. Upon receiving current sensor data, the machine learning model identifies a subset of the current sensor data that cannot be validated. The system generates estimated values for the sensor data that cannot be validated based on the learned correlations among the sensor values. The system generates the recommendations for remediating the issues in the monitored system based on validated sensor values and the estimated sensor values.Type: ApplicationFiled: November 18, 2021Publication date: May 18, 2023Applicant: Oracle International CorporationInventors: James Charles Rohrkemper, Kenneth Paul Baclawski, Dieter Gawlick, Kenny C. Gross, Guang Chao Wang, Anna Chystiakova, Richard Paul Sonderegger, Zhen Hua Liu
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Publication number: 20230061280Abstract: Techniques for identifying a root cause of an operational result of a deterministic machine learning model are disclosed. A system applies a deterministic machine learning model to a set of data to generate an operational result, such as a prediction of a “fault” or “no-fault” in the system. The set of data includes signals from multiple different data sources, such as sensors. The system applies an abductive model, generated based on the deterministic machine learning model, to the operational result. The abductive model identifies a particular set of data sources that is associated with the root cause of the operational result. The system generates a human-understandable explanation for the operational result based on the identified root cause.Type: ApplicationFiled: August 31, 2021Publication date: March 2, 2023Applicant: Oracle International CorporationInventors: James Charles Rohrkemper, Richard Paul Sonderegger, Anna Chystiakova, Kenneth Paul Baclawski, Dieter Gawlick, Kenny C. Gross, Zhen Hua Liu, Guang Chao Wang
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Publication number: 20230035541Abstract: The disclosed embodiments relate to a system that optimizes a prognostic-surveillance system to achieve a user-selectable functional objective. During operation, the system allows a user to select a functional objective to be optimized from a set of functional objectives for the prognostic-surveillance system. Next, the system optimizes the selected functional objective by performing Monte Carlo simulations, which vary operational parameters for the prognostic-surveillance system while the prognostic-surveillance system operates on synthesized signals, to determine optimal values for the operational parameters that optimize the selected functional objective.Type: ApplicationFiled: July 28, 2021Publication date: February 2, 2023Applicant: Oracle International CorporationInventors: Menglin Liu, Richard P. Sonderegger, Kenneth P. Baclawski, Dieter Gawlick, Anna Chystiakova, Guang C. Wang, Zhen Hua Liu, Hariharan Balasubramanian, Kenny C. Gross
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Publication number: 20230008658Abstract: The disclosed embodiments relate to a system that automatically adapts a prognostic-surveillance system to account for aging phenomena in a monitored system. During operation, the prognostic-surveillance system is operated in a surveillance mode, wherein a trained inferential model is used to analyze time-series signals from the monitored system to detect incipient anomalies. During the surveillance mode, the system periodically calculates a reward/cost metric associated with updating the trained inferential model. When the reward/cost metric exceeds a threshold, the system swaps the trained inferential model with an updated inferential model, which is trained to account for aging phenomena in the monitored system.Type: ApplicationFiled: July 7, 2021Publication date: January 12, 2023Applicant: Oracle International CorporationInventors: Richard P. Sonderegger, Kenneth P. Baclawski, Guang C. Wang, Anna Chystiakova, Dieter Gawlick, Zhen Hua Liu, Kenny C. Gross
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Publication number: 20220413481Abstract: Techniques for geometric aging data reduction for machine learning applications are disclosed. In some embodiments, an artificial-intelligence powered system receives a first time-series dataset that tracks at least one metric value over time. The system then generates a second time-series dataset that includes a reduced version of a first portion of the time-series dataset and a non-reduced version of a second portion of the time-series dataset. The second portion of the time-series dataset may include metric values that are more recent than the first portion of the time-series dataset. The system further trains a machine learning model using the second time-series dataset that includes the reduced version of the first portion of the time-series dataset and the non-reduced version of the second portion of the time-series dataset. The trained model may be applied to reduced and/or non-reduced data to detect multivariate anomalies and/or provide other analytic insights.Type: ApplicationFiled: June 28, 2021Publication date: December 29, 2022Applicant: Oracle International CorporationInventors: Dieter Gawlick, Matthew Torin Gerdes, Kirk Bradley, Anna Chystiakova, Zhen Hua Liu, Guang Chao Wang, Kenny C. Gross
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Publication number: 20220237509Abstract: Techniques for providing decision rationales for machine-learning guided processes are described herein. In some embodiments, the techniques described herein include processing queries for an explanation of an outcome of a set of one or more decisions guided by one or more machine-learning processes with supervision by at least one human operator. Responsive to receiving the query, a system determines, based on a set of one or more rationale data structures, whether the outcome was caused by human operator error or the one or more machine-learning processes. The system then generates a query response indicating whether the outcome was caused by the human operator error or the one or more machine-learning processes.Type: ApplicationFiled: July 19, 2021Publication date: July 28, 2022Applicant: Oracle International CorporationInventors: John Frederick Courtney, Kenneth Paul Baclawski, Dieter Gawlick, Kenny C. Gross, Guang Chao Wang, Anna Chystiakova, Richard Paul Sonderegger, Zhen Hua Liu
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Publication number: 20210295210Abstract: In one embodiment, a method for auditing the results of a machine learning model includes: retrieving a set of state estimates for original time series data values from a database under audit; reversing the state estimation computation for each of the state estimates to produce reconstituted time series data values for each of the state estimates; retrieving the original time series data values from the database under audit; comparing the original time series data values pairwise with the reconstituted time series data values to determine whether the original time series and reconstituted time series match; and generating a signal that the database under audit (i) has not been modified where the original time series and reconstituted time series match, and (ii) has been modified where the original time series and reconstituted time series do not match.Type: ApplicationFiled: March 23, 2020Publication date: September 23, 2021Inventors: Edward R. WETHERBEE, Kenneth P. BACLAWSKI, Guang C. WANG, Kenny C. GROSS, Anna CHYSTIAKOVA, Dieter GAWLICK, Zhen Hua LIU, Richard Paul SONDEREGGER