公开(公告)号：US20240303530A1

公开(公告)日：2024-09-12

申请号：US18118782

申请日：2023-03-08

Applicant: ORACLE INTERNATIONAL CORPORATION

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

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Systems, methods, and other embodiments associated with inverse-density exemplar selection for improved multivariate anomaly detection are described. In one embodiment, a method includes determining magnitudes of vectors from a set of time series readings collected from a plurality of sensors. And, the example method includes selecting exemplar vectors from the set of time series readings to train a machine learning model to detect anomalies. The exemplar vectors are selected by repetitively (i) increasing a first density of extreme vectors that are within tails of a distribution of amplitudes for the time series readings based on the magnitudes of vectors, and (ii) decreasing a second density of non-extreme vectors that are within a head of the distribution based on the magnitudes of vectors. The repetition continues until the machine learning model generates residuals within a threshold in order to reduce false or missed detection of the extreme vectors as anomalous.

公开(公告)号：US20240354633A1

公开(公告)日：2024-10-24

申请号：US18133125

申请日：2023-04-11

Applicant: ORACLE INTERNATIONAL CORPORATION

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

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Systems, methods, and other embodiments associated with determining a quantity of exemplar vectors to select from available training vectors are described. In one embodiment, a method includes determining an available quantity of training vectors that are available in a set of time series signals. A boost function is automatically selected from a plurality of different boost functions based on the available quantity of the training vectors. A selection quantity of the exemplar vectors to select from the training vectors is generated by applying the selected boost function to the training vectors. A quantity of the exemplar vectors is selected from the training vectors based on the selection quantity. A machine learning model is trained to detect an anomaly in the time series signals based on the exemplar vectors that were selected.

公开(公告)号：US20240256959A1

公开(公告)日：2024-08-01

申请号：US18226522

申请日：2023-07-26

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Keyang RU ,  Kenneth P. BACLAWSKI ,  Richard P. SONDEREGGER ,  Dieter GAWLICK ,  Anna CHYSTIAKOVA ,  Guang Chao WANG ,  Matthew T. GERDES ,  Kenny C. GROSS

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Systems, methods, and other embodiments associated with detecting unfairness in machine learning outcomes are described. In one embodiment, a method includes generating outcomes for transactions with a machine learning tool to be tested for bias. Then, actual values for a test subset of the outcomes that is associated with a test value for a demographic classification are compared with estimated values for the test subset of outcomes. The estimated values are generated by a machine learning model that is trained with a reference subset of the outcomes that are associated with a reference value for the demographic classification. The method then detects whether the machine learning tool is biased or unbiased based on dissimilarity between the actual values and the estimated values for the test subset of the outcomes. The method then generates an electronic alert that the ML tool is biased or unbiased.

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：US20250094830A1

公开(公告)日：2025-03-20

申请号：US18370101

申请日：2023-09-19

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Keyang RU ,  Ruixian LIU ,  Kuei-Da LIAO ,  Guang Chao WANG ,  Matthew T. GERDES ,  Kenny C. GROSS

IPC: G06N5/022

Abstract: Systems, methods, and other embodiments associated with clustering of time series signals based on frequency domain analysis are described. In one embodiment, an example method includes accessing time series signals to be separated into clusters. The example method also includes determining similarity in the frequency domain among the time series signals. The example method further includes extracting a cluster of similar time series signals from the time series signals based on the similarity in the frequency domain. And, the example method includes training a machine learning model to detect anomalies based on the cluster.

公开(公告)号：US20240346361A1

公开(公告)日：2024-10-17

申请号：US18133047

申请日：2023-04-11

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Keyang RU ,  Kuei-Da LIAO ,  Matthew T. GERDES ,  Kenny C. GROSS ,  Guang Chao WANG ,  Ruixian LIU

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Systems, methods, and other embodiments associated with automatic clustering of signals including added ambient signals are described. In one embodiment, a method includes receiving time series signals (TSSs) associated with a plurality of machines (or components or other signal sources). The TSSs are unlabeled as to which of the machines the TSSs are associated with. The TSSs are automatically separated into a plurality of clusters corresponding to the plurality of the machines. A group of ambient TSSs is identified that overlaps more than one of the clusters. The group of the ambient TSSs is added into the one cluster of the clusters that corresponds to the one machine. A machine learning model is then trained to detect an anomaly based on the one cluster to generate a trained machine learning model that is specific to the one machine without using the TSSs not included in the one cluster.