Google patents system to spot odd patterns in data streams
Google describes a method for finding unusual values in sequences of data, such as server activity or sensor readings. It uses a chain of machine learning models to predict what a normal value should be and flags any data point that falls outside a specific range set by the user.
In the filing’s own words
A method includes receiving a time series anomaly detection query from a user and training one or more models using a set of time series data values. For each respective time series data value in the set, the method includes determining, using the trained models, an expected data value for the respective time series data value and determining a difference between the expected data value and the respective time series data value. The method also includes determining that the difference between the expected data value and the respective time series data value satisfies a threshold. In response to determining that the difference between the expected data value and the respective time series data value satisfies the threshold, the method includes determining that the respective time series data value is anomalous and reporting the anomalous respective time series data value to the user.
What exactly they claim (claim 1)
1. A method comprising: receiving, by data processing hardware, a time series anomaly detection query from a user, the time series anomaly detection query comprising a user-specified anomaly probability and requesting the data processing hardware to determine one or more anomalies in a set of time series data values; training, by the data processing hardware, a chain of two or more models using each time series data value in the set of time series data values, wherein the two or more models includes a separate model for each component from a plurality of components to improve scalability; and for each respective time series data value in the set of time series data values: determining, by the data processing hardware and using the chain of two or more models, an expected data value for the respective time series data value; determining, by the data processing hardware, an interval size based on the user-specified anomaly probability; determining, by the data processing hardware, an upper bound and a lower bound based on the expected data value and the interval size; determining, by the data processing hardware, whether the respective time series data value is greater than the upper bound or less than the lower bound; in response to determining that the respective time series data value is greater than the upper bound or less than the lower bound, determining, by the data processing hardware, that the respective time series data value is anomalous; and reporting, by the data processing hardware, the respective time series data value to the user.
Facts
- Company
- Google (Google LLC)
- Official title
- MACHINE LEARNING TIME SERIES ANOMALY DETECTION
- Publication number
- US20260278075A1
- Application number
- 19676483
- Filed
- May 13, 2026
- Published
- Sep 17, 2026
- Inventors
- 4
- Claims
- 20
Classified under
G06F 21/552 Physics & computingG06F 16/2438 Physics & computingG06N 20/00 Physics & computingG06F 2221/034 Physics & computingMore from Google in AI & software
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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.