公开(公告)号：US20230358597A1

公开(公告)日：2023-11-09

申请号：US18098277

申请日：2023-01-18

Applicant: ORACLE INTERNATIONAL CORPORATION

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

IPC: G01H3/08 ,  G06F17/14 ,  G06F17/18 ,  G08B21/00

CPC classification number: G01H3/08 ,  G06F17/14 ,  G06F17/18 ,  G08B21/00

Abstract: Systems, methods, and other embodiments associated with acoustic detection of changes in mass of cargo carried by a vehicle are described herein. In one example method for acoustic cargo surveillance, a first acoustic output of a vehicle carrying cargo at a first time of surveillance of the vehicle is recorded. Then, a second acoustic output of the vehicle at a subsequent time in the surveillance of the vehicle carrying the cargo is recorded. A change in a mass of the cargo carried by the vehicle is acoustically detected based at least on an acoustic change between the first acoustic output and the second acoustic output. An electronic alert is generated that the mass of the cargo has changed based on the acoustic change.

公开(公告)号：US20230113706A1

公开(公告)日：2023-04-13

申请号：US17495880

申请日：2021-10-07

Applicant: ORACLE INTERNATIONAL CORPORATION

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

IPC: G01V3/10

Abstract: Embodiments for passive spychip detection through polarizability and advanced pattern recognition are described. For example a method includes inducing a magnetic field in a passive component of a target system while the target system is emitting EMI with changes in amplitude repeating at a time interval; generating a time series of measurements of a combined magnetic field strength of the induced magnetic field and the EMI; executing a frequency-domain to time-domain transformation on the time series of measurements to create time series signals of combined magnetic field strength over time at a specific frequency range; monitoring the time series signals with an ML model trained to predict correct signal values to determine whether predicted and measured values of the time series agree; and indicating that the target device may contain a passive spychip where anomalies are detected, and is free of passive spychips where no anomalies are detected.

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：US20240230733A1

公开(公告)日：2024-07-11

申请号：US18094509

申请日：2023-01-09

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Keyang RU ,  Ruixian LIU ,  Kenny C. GROSS ,  Guang Chao WANG

IPC: G01R21/133 ,  G01R23/16

CPC classification number: G01R21/133 ,  G01R23/16

Abstract: Systems, methods, and other embodiments associated with frequency-domain resampling of time series are described. An example method includes generating a power spectrum for a first time series signal that is sampled inconsistently with a target sampling rate. Prominent frequencies are selected from the power spectrum. Sets of first phase factors that map the prominent frequencies to a frequency domain at first time points are generated. Coefficients are identified that relate the sets of first phase factors to values of the first time series signal at the first time points. Sets of second phase factors that map the prominent frequencies to a frequency domain at second time points are generated. A second time series signal that is resampled at the target sampling rate is generated by generating new values at the second time points from the coefficients and sets of second phase factors.

公开(公告)号：US20240206766A1

公开(公告)日：2024-06-27

申请号：US18085974

申请日：2022-12-21

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Zejin DING ,  Matthew T. GERDES ,  Guang Chao WANG ,  Kenny C. GROSS ,  Andrew VAKHUTINSKY

IPC: A61B5/11 ,  A61B5/00

CPC classification number: A61B5/112 ,  A61B5/1126 ,  A61B5/7203 ,  A61B5/746 ,  A61B2562/0219 ,  G01P15/18

Abstract: Systems, methods, and other embodiments associated with detecting impairment using a vibration fingerprint that characterizes gait dynamics are described. An example method includes receiving measurements of a gait of a being from a sensor. The measurements of the gait are converted into a time series of observations for each frequency bin in a set of frequency bins. A time series of residuals is generated for each range of the set by pointwise subtraction between the time series of observations and a time series of references for each range of the set. An impairment metric is generated based on the time series of residuals. The impairment metric is compared to a threshold for the impairment. In response to the impairment metric satisfying the threshold, the being is indicated to be impaired.

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：US20240045927A1

公开(公告)日：2024-02-08

申请号：US17881864

申请日：2022-08-05

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Zejin DING ,  Matthew T. GERDES ,  Kenny C. GROSS ,  Guang Chao WANG

IPC: G06K9/00 ,  G06K9/62 ,  G01M99/00 ,  G06N20/00

CPC classification number: G06K9/0053 ,  G06K9/6256 ,  G01M99/005 ,  G06N20/00

Abstract: Systems, methods, and other embodiments associated with associated with preserving signal extrema for ML model training when ensemble averaging time series signals for ML anomaly detection are described. In one embodiment, a method includes identifying locations and values of extrema in a training signal; ensemble averaging the training signal to produce an averaged training signal; placing the values of the extrema into the averaged training signal at respective locations of the extrema to produce an extrema-preserved averaged training signal; placing the values of the extrema into the averaged training signal at respective locations of the extrema to produce an extrema-preserved averaged training signal; and training a machine learning model using the extrema-preserved averaged training signal to detect anomalies in a signal.