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公开(公告)号：US20220300737A1
公开(公告)日：2022-09-22
申请号：US17205445
申请日：2021-03-18
Applicant: Oracle International Corporation
Inventor： Neelesh Kumar Shukla , Saurabh Thapliyal , Matthew T. Gerdes , Guang C. Wang , Kenny C. Gross
Abstract: The disclosed embodiments provide a system that detects sensor anomalies in a univariate time-series signal. During a surveillance mode, the system receives the univariate time-series signal from a sensor in a monitored system. Next, the system performs a staggered-sampling operation on the univariate time-series signal to produce N sub-sampled time-series signals, wherein the staggered-sampling operation allocates consecutive samples from the univariate time-series signal to the N sub-sampled time-series signals in a round-robin ordering. The system then uses a trained inferential model to generate estimated values for the N sub-sampled time-series signals based on cross-correlations with other sub-sampled time-series signals. Next, the system performs an anomaly detection operation to detect incipient sensor anomalies in the univariate time-series signal based on differences between actual values and the estimated values for the N sub-sampled time-series signals. Whenever an incipient sensor anomaly is detected, the system generates a notification.
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公开(公告)号：US12260304B2
公开(公告)日：2025-03-25
申请号：US17205445
申请日：2021-03-18
Applicant: Oracle International Corporation
Inventor： Neelesh Kumar Shukla , Saurabh Thapliyal , Matthew T. Gerdes , Guang C. Wang , Kenny C. Gross
Abstract: The disclosed embodiments provide a system that detects sensor anomalies in a univariate time-series signal. During a surveillance mode, the system receives the univariate time-series signal from a sensor in a monitored system. Next, the system performs a staggered-sampling operation on the univariate time-series signal to produce N sub-sampled time-series signals, wherein the staggered-sampling operation allocates consecutive samples from the univariate time-series signal to the N sub-sampled time-series signals in a round-robin ordering. The system then uses a trained inferential model to generate estimated values for the N sub-sampled time-series signals based on cross-correlations with other sub-sampled time-series signals. Next, the system performs an anomaly detection operation to detect incipient sensor anomalies in the univariate time-series signal based on differences between actual values and the estimated values for the N sub-sampled time-series signals. Whenever an incipient sensor anomaly is detected, the system generates a notification.
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公开(公告)号：US12189706B2
公开(公告)日：2025-01-07
申请号：US17711831
申请日：2022-04-01
Applicant: Oracle International Corporation
IPC: G06F16/9035 , G06F16/904 , G06F16/9535 , G06F16/9538 , G06F16/954 , G06F16/957
Abstract: A processor may receive a request for a query item may include a plurality of identifying markers, relating to data associated with the query item. A machine learning model, trained to identify similar items according to the plurality of identifying markers, may then process the plurality of identifying markers and provide a list of one or more similar items and respective similarity distances. The processor may access a respective entity profile including one or more scenario scores for each of the similar items. The processor may then calculate an entity score for each respective entity profile using the respective similarity distances and the scenario scores. The processor may then generate an entity list by ranking the respective entities associated with each respective entity profile using the entity score. The processor may then output the entity list to the client device.
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公开(公告)号：US20230315798A1
公开(公告)日：2023-10-05
申请号：US17711831
申请日：2022-04-01
Applicant: Oracle International Corporation
IPC: G06F16/957 , G06F16/9538 , G06F16/954
CPC classification number: G06F16/957 , G06F16/9538 , G06F16/954
Abstract: A processor may receive a request for a query item may include a plurality of identifying markers, relating to data associated with the query item. A machine learning model, trained to identify similar items according to the plurality of identifying markers, may then process the plurality of identifying markers and provide a list of one or more similar items and respective similarity distances. The processor may access a respective entity profile including one or more scenario scores for each of the similar items. The processor may then calculate an entity score for each respective entity profile using the respective similarity distances and the scenario scores. The processor may then generate an entity list by ranking the respective entities associated with each respective entity profile using the entity score. The processor may then output the entity list to the client device.
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