公开(公告)号：US20230075065A1

公开(公告)日：2023-03-09

申请号：US17463742

申请日：2021-09-01

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Ikenna D. IVENSO ,  Matthew T. GERDES ,  Kenny C. GROSS ,  Guang C. WANG ,  Hariharan BALASUBRAMANIAN

IPC: G06N20/00

Abstract: Systems, methods, and other embodiments associated with passive inferencing of signal following in multivariate anomaly detection are described. In one embodiment, a method for inferencing signal following in a machine learning (ML) model includes calculating an average standard deviation of measured values of time series signals in a set of time series signals; training the ML model to predict values of the signals; predicting values of each of the signals with the trained ML model; generating a time series set of residuals between the predicted values and the measured values; calculating an average standard deviation of the sets of residuals; determining that signal following is present in the trained ML model where a ratio of the average standard deviation of measured values to the average standard deviation of the sets of residuals exceeds a threshold; and presenting an alert indicating the presence of signal following in the trained ML model.

公开(公告)号：US20220261689A1

公开(公告)日：2022-08-18

申请号：US17382593

申请日：2021-07-22

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： William A. WIMSATT ,  Matthew T. GERDES ,  Kenny C. GROSS ,  Guang C. WANG

IPC: G06N20/00 ,  G06N5/04 ,  G06K9/00

Abstract: Systems, methods, and other embodiments associated with off-duty-cycle-robust machine learning for anomaly detection in assets with random downtimes are described. In one embodiment, a method includes inferring ranges of asset downtime from spikes in a numerical derivative of a time series signal for an asset; extracting an asset downtime signal from the time series signal based on the inferred ranges of asset downtime; determining that the asset downtime signal carries telemetry based on the variance of the asset downtime signal; training a first machine learning model for the asset downtime signal; detecting a first spike in the numerical derivative of the time signal that indicates a transition to asset downtime; and in response to detection of the first spike, monitoring the time series signal for anomalous activity with the trained first machine learning model.

公开(公告)号：US20210295210A1

公开(公告)日：2021-09-23

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: G06N20/00 ,  G06F17/16 ,  G06F17/18 ,  G06F30/27

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：US20240265308A1

公开(公告)日：2024-08-08

申请号：US18615599

申请日：2024-03-25

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Edward R. WETHERBEE ,  Kenneth P. BACLAWSKI ,  Guang C. WANG ,  Kenny C. GROSS ,  Anna MORAV ,  Dieter GAWLICK ,  Zhen Hua LIU ,  Richard Paul SONDEREGGER

IPC: G06N20/00 ,  G05B23/02 ,  G06F17/16 ,  G06F17/18 ,  G06F30/27 ,  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: Systems, methods, and other embodiments associated with auditing the results of a machine learning model are described. In one embodiment, a method accesses original time series data and machine learning estimates of the original time series data. The method generates reconstituted time series data from the machine learning estimates by reversing operations of a machine learning model trained for generating the machine learning estimates from the original time series data. The method detects tampering (or corruption) in the original time series data based on a difference between the original time series data and reconstituted time series data. And, the method generates an electronic verification report that indicates whether the tampering (or corruption) is detected in the original time series data.

公开(公告)号：US20240061139A1

公开(公告)日：2024-02-22

申请号：US18385116

申请日：2023-10-30

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： James ROHRKEMPER ,  Yifan WU ,  Guang C. WANG ,  Kenny C. GROSS

IPC: G01V3/10

CPC classification number: G01V3/10

Abstract: Systems, methods, and other embodiments for passive component (e.g., spychip) detection through polarizability and advanced pattern recognition are described. In one embodiment a method includes applying an electromagnetic field to a target electronic system while the target electronic system is emitting a test pattern of electromagnetic interference. The method takes measurements of combined electromagnetic field strength emitted by the target electronic system while the electromagnetic field is being applied. The method detects the passive component based on dissimilarity between the measurements and estimates of electromagnetic field strength for the test pattern for a golden electronic system. The golden electronic system is of similar construction to the target electronic system and does not include the passive component. The method generates an electronic alert that the passive component is present in the target electronic system.

公开(公告)号：US20230358872A1

公开(公告)日：2023-11-09

申请号：US17735245

申请日：2022-05-03

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Matthew T. GERDES ,  Guang C. WANG ,  Timothy D. CLINE ,  Kenny C. GROSS

IPC: G01S7/52 ,  G01S15/89

CPC classification number: G01S7/52001 ,  G01S15/89 ,  G01S7/52004

Abstract: Systems, methods, and other embodiments associated with acoustic fingerprint identification of devices are described. In one embodiment, a method includes generating a target acoustic fingerprint from acoustic output of a target device. A similarity metric is generated that quantifies similarity of the target acoustic fingerprint to a reference acoustic fingerprint of a reference device. The similarity metric is compared to a threshold. In response to a first comparison result of the comparing of the similarity metric to the threshold, the target device is indicated to match the reference device. In response to a second comparison result of the comparing of the similarity metric to the threshold, it is indicated that the target device does not match the reference device.

公开(公告)号：US20230327789A1

公开(公告)日：2023-10-12

申请号：US17715449

申请日：2022-04-07

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Guang C. WANG ,  Matthew T. GERDES ,  Kenny C. GROSS ,  Alan P. WOOD

IPC: H04B17/391

CPC classification number: H04B17/3912

Abstract: Systems, methods, and other embodiments associated with eviction of weakly correlated signals from collections are described. In one embodiment, a mock signal that has random signal properties is generated. A mock correlation coefficient between the mock signal and a measured time series signal from a collection of measured time series signals is then generated. A discrimination value that indicates a weak signal correlation is then selected, based at least in part on the mock correlation coefficient. A first measured signal is then identified from the collection of measured time series signals that has the weak signal correlation by determining that a first correlation coefficient between the first measured signal and a second measured signal is weak based on the discrimination value. The first measured signal is then evicted from the collection of signals in response to the determination that the first measured signal has the weak signal correlation.

公开(公告)号：US20220284351A1

公开(公告)日：2022-09-08

申请号：US17825189

申请日：2022-05-26

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Edward R. WETHERBEE ,  Kenny C. GROSS ,  Guang C. WANG ,  Matthew T. GERDES

IPC: G06N20/00 ,  H04L41/08 ,  H04L41/16 ,  H04L67/10 ,  H04L41/22 ,  H04L67/12 ,  G06N20/10

Abstract: Systems, methods, and other embodiments associated with autonomous cloud-node scoping for big-data machine learning use cases are described. In some example embodiments, an automated scoping tool, method, and system are presented that, for each of multiple combinations of parameter values, (i) set a combination of parameter values describing a usage scenario, (ii) execute a machine learning application according to the combination of parameter values on a target cloud environment, and (iii) measure the computational cost for the execution of the machine learning application. A recommendation regarding configuration of central processing unit(s), graphics processing unit(s), and memory for the target cloud environment to execute the machine learning application is generated based on the measured computational costs.

公开(公告)号：US20220196776A1

公开(公告)日：2022-06-23

申请号：US17694304

申请日：2022-03-14

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Edward R. WETHERBEE ,  Andrew LEWIS ,  Michael DAYRINGER ,  Guang C. WANG ,  Kenny C. GROSS

IPC: G01R35/00 ,  G01R29/08

Abstract: Systems, methods, and other embodiments associated with automated calibration in electromagnetic scanners are described. In one embodiment, a method includes: detecting one or more peak frequency bands in electromagnetic signals collected by the electromagnetic scanner at a geographic location; comparing the one or more peak frequency bands to broadcast frequencies assigned to local radio stations of the geographic location; and indicating that the electromagnetic scanner is calibrated by finding at least one match between one peak frequency band of the peak frequency bands and one of the broadcast frequencies. An electromagnetic scanner may be recalibrated based on comparing the one or more peak frequency bands to broadcast frequencies.

公开(公告)号：US20210293916A1

公开(公告)日：2021-09-23

申请号：US16820807

申请日：2020-03-17

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Edward R. WETHERBEE ,  Andrew LEWIS ,  Michael DAYRINGER ,  Guang C. WANG ,  Kenny C. GROSS

IPC: G01R35/00 ,  G01R29/08

Abstract: Systems, methods, and other embodiments associated with automated calibration of electromagnetic interference (EMI) fingerprint scanning instrumentation for utility power system counterfeit detection are described. In one embodiment, a method for detecting a calibration state of an EMI fingerprint scanning device includes: collecting electromagnetic signals with the EMI fingerprint scanning device for a test period of time at a geographic location; identifying one or more peak frequency bands in the collected electromagnetic signals; comparing the one or more peak frequency bands to assigned radio station frequencies at the geographic location to determine if a match is found; and generating a calibration state signal based at least in part on the comparing to indicate whether the EMI fingerprint scanning device is calibrated or not calibrated.