-
公开(公告)号：US20230008658A1
公开(公告)日：2023-01-12
申请号：US17368840
申请日：2021-07-07
Applicant: Oracle International Corporation
Inventor： Richard P. Sonderegger , Kenneth P. Baclawski , Guang C. Wang , Anna Chystiakova , Dieter Gawlick , Zhen Hua Liu , Kenny C. Gross
Abstract: 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.
-
公开(公告)号：US20230153680A1
公开(公告)日：2023-05-18
申请号：US17455536
申请日：2021-11-18
Applicant: Oracle International Corporation
Inventor： James Charles Rohrkemper , Kenneth Paul Baclawski , Dieter Gawlick , Kenny C. Gross , Guang Chao Wang , Anna Chystiakova , Richard Paul Sonderegger , Zhen Hua Liu
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: 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.
-
公开(公告)号：US20230061280A1
公开(公告)日：2023-03-02
申请号：US17462592
申请日：2021-08-31
Applicant: Oracle International Corporation
Inventor： James Charles Rohrkemper , Richard Paul Sonderegger , Anna Chystiakova , Kenneth Paul Baclawski , Dieter Gawlick , Kenny C. Gross , Zhen Hua Liu , Guang Chao Wang
IPC: G06N20/00 , G06F16/242
Abstract: 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.
-
公开(公告)号：US20220237509A1
公开(公告)日：2022-07-28
申请号：US17379937
申请日：2021-07-19
Applicant: Oracle International Corporation
Inventor： John Frederick Courtney , Kenneth Paul Baclawski , Dieter Gawlick , Kenny C. Gross , Guang Chao Wang , Anna Chystiakova , Richard Paul Sonderegger , Zhen Hua Liu
Abstract: 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.
-
Patent Agency Ranking
公开(公告)号：US11782429B2
公开(公告)日：2023-10-10
申请号：US17368840
申请日：2021-07-07
Applicant: Oracle International Corporation
Inventor： Richard P. Sonderegger , Kenneth P. Baclawski , Guang C. Wang , Anna Chystiakova , Dieter Gawlick , Zhen Hua Liu , Kenny C. Gross
CPC classification number: G05B23/0221 , G05B13/0265 , G05B15/02 , G06N20/00 , G05B2223/02
Abstract: 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.
-
6.发明申请公开(公告)号：US20230035541A1
公开(公告)日：2023-02-02
申请号：US17386965
申请日：2021-07-28
Applicant: Oracle International Corporation
Inventor： Menglin Liu , Richard P. Sonderegger , Kenneth P. Baclawski , Dieter Gawlick , Anna Chystiakova , Guang C. Wang , Zhen Hua Liu , Hariharan Balasubramanian , Kenny C. Gross
Abstract: 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.
-
公开(公告)号：US12007759B2
公开(公告)日：2024-06-11
申请号：US17361189
申请日：2021-06-28
Applicant: Oracle International Corporation
Inventor： Dieter Gawlick , Matthew Torin Gerdes , Kirk Bradley , Anna Chystiakova , Zhen Hua Liu , Guang Chao Wang , Kenny C. Gross
IPC: G05B23/02 , G06F11/34 , G06F16/215 , G06F18/214 , G06N20/00
CPC classification number: G05B23/0283 , G06F11/3409 , G06F16/215 , G06F18/2148 , G06N20/00
Abstract: 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.
-
8.发明授权公开(公告)号：US11948051B2
公开(公告)日：2024-04-02
申请号：US16826478
申请日：2020-03-23
Applicant: ORACLE INTERNATIONAL CORPORATION
Inventor： Edward R. Wetherbee , Kenneth P. Baclawski , Guang C. Wang , Kenny C. Gross , Anna Chystiakova , Dieter Gawlick , Zhen Hua Liu , Richard Paul Sonderegger
IPC: G06F16/00 , G05B23/02 , G06F17/16 , G06F17/18 , G06F30/27 , G06N20/00 , G06F111/10 , G06N3/08 , G06N20/10
CPC classification number: G06N20/00 , G05B23/024 , G06F17/16 , G06F17/18 , G06F30/27 , G06F2111/10 , G06N3/08 , G06N20/10
Abstract: 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.
-
公开(公告)号：US20220413481A1
公开(公告)日：2022-12-29
申请号：US17361189
申请日：2021-06-28
Applicant: Oracle International Corporation
Inventor： Dieter Gawlick , Matthew Torin Gerdes , Kirk Bradley , Anna Chystiakova , Zhen Hua Liu , Guang Chao Wang , Kenny C. Gross
Abstract: 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.
-
-
-
-
-
-
-
-
Search Results
9
records
(took 0.02 seconds)
