- Patent Title: Model reselection for accommodating unsatisfactory training data
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Application No.: US16417245Application Date: 2019-05-20
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Publication No.: US11132584B2Publication Date: 2021-09-28
- Inventor: Christopher John Challis , Aishwarya Asesh
- Applicant: ADOBE INC.
- Applicant Address: US CA San Jose
- Assignee: ADOBE INC.
- Current Assignee: ADOBE INC.
- Current Assignee Address: US CA San Jose
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G06K9/62 ; G06F11/32 ; G06F11/00

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.
Public/Granted literature
- US20200372298A1 MODEL RESELECTION FOR ACCOMMODATING UNSATISFACTORY TRAINING DATA Public/Granted day:2020-11-26
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