Invention Grant
- Patent Title: Real-time anomaly detection and correlation of time-series data
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Application No.: US16806218Application Date: 2020-03-02
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Publication No.: US11275639B2Publication Date: 2022-03-15
- Inventor: Xiang Wang , Tara Safavi
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- Agency: Lerner, David, Littenberg, Krumholz & Mentlik, LLP
- Main IPC: G06F11/00
- IPC: G06F11/00 ; G06F11/07 ; G06K9/00 ; G06K9/62 ; G06F17/18

Abstract:
The present disclosure provides systems and methods for detecting and correlating anomalous time-series data. A system may receive and process time-series data associated with one or more network data streams to generate sets of aligned time-series data. The system may detect anomalous time-stamped data points in the sets of aligned time series data and generate groups of annotated time-series data. The annotation identifies specific time-stamped data points as anomalous. The system may determine the number of anomalous groups of annotated time-series data within all groups of annotated time-series data and may further determine the probability that one or more anomalous groups belong to at least one of the groups of annotated time-series data using a generative statistical model and outputting one or more correlated anomalous groups. The system may generate a detailed statistical report for each correlated anomalous group and output an aggregated statistical report for the correlated groups.
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