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Patent Agency Ranking
公开(公告)号：US20200372298A1
公开(公告)日：2020-11-26
申请号：US16417245
申请日：2019-05-20
Applicant: ADOBE INC.
Inventor： Christopher John Challis , Aishwarya Asesh
Abstract: An anomaly analysis system generates models capable of more accurately identifying anomalies in data that contains unsatisfactory training data. The anomaly analysis system determines when data contains unsatisfactory training data. When an anomaly is detected in data using an initially selected model, and the data contains unsatisfactory training data, model reselection is performed. The reselected model analyzes the data. The reselected model is used to identify any anomalies in the data based on a data point from the data being outside of a confidence interval related to a predicted point by the reselected model corresponding to the data point.
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公开(公告)号：US11620474B2
公开(公告)日：2023-04-04
申请号：US17480280
申请日：2021-09-21
Applicant: ADOBE INC.
Inventor： Christopher John Challis , Aishwarya Asesh
Abstract: An anomaly analysis system generates models capable of more accurately identifying anomalies in data that contains unsatisfactory training data. The anomaly analysis system determines when data contains unsatisfactory training data. When an anomaly is detected in data using an initially selected model, and the data contains unsatisfactory training data, model reselection is performed. The reselected model analyzes the data. The reselected model is used to identify any anomalies in the data based on a data point from the data being outside of a confidence interval related to a predicted point by the reselected model corresponding to the data point.
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公开(公告)号：US20220004813A1
公开(公告)日：2022-01-06
申请号：US17480280
申请日：2021-09-21
Applicant: ADOBE INC.
Inventor： Christopher John Challis , Aishwarya Asesh
Abstract: An anomaly analysis system generates models capable of more accurately identifying anomalies in data that contains unsatisfactory training data. The anomaly analysis system determines when data contains unsatisfactory training data. When an anomaly is detected in data using an initially selected model, and the data contains unsatisfactory training data, model reselection is performed. The reselected model analyzes the data. The reselected model is used to identify any anomalies in the data based on a data point from the data being outside of a confidence interval related to a predicted point by the reselected model corresponding to the data point.
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公开(公告)号：US11132584B2
公开(公告)日：2021-09-28
申请号：US16417245
申请日：2019-05-20
Applicant: ADOBE INC.
Inventor： Christopher John Challis , Aishwarya Asesh
Abstract: An anomaly analysis system generates models capable of more accurately identifying anomalies in data that contains unsatisfactory training data. The anomaly analysis system determines when data contains unsatisfactory training data. When an anomaly is detected in data using an initially selected model, and the data contains unsatisfactory training data, model reselection is performed. The reselected model analyzes the data. The reselected model is used to identify any anomalies in the data based on a data point from the data being outside of a confidence interval related to a predicted point by the reselected model corresponding to the data point.
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公开(公告)号：US11095544B1
公开(公告)日：2021-08-17
申请号：US16904249
申请日：2020-06-17
Applicant: Adobe Inc.
Inventor： Aishwarya Asesh , Sunav Choudhary , Shiv Kumar Saini , Chris Challis
IPC: G06F16/2458 , G06F16/248 , G06F7/00 , H04L12/26
Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for determining latent components of a metrics time series and identifying anomalous data within the metrics time series based on one or both of spikes/dips and level changes from the latent components satisfying significance thresholds. To identify such latent components, in some cases, the disclosed systems account for a range of value types by intelligently subjecting real values to a latent-component constraint for decomposing the time series and intelligently excluding non-real values from the latent-component constraint. The disclosed systems can further identify significant anomalous data values from latent components of the metrics time series by jointly determining whether one or both of a subseries of a spike-component series and a level change from a level-component series satisfy significance thresholds.
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