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Google patents system to predict server failures using machine learning

Patent drawing
Patent drawing from the official filing.

Google describes a method for collecting performance data from many computers in a data center and using a machine learning model to estimate how likely each one is to break. The system stores these health predictions and updates them regularly, allowing operators to view the status and trigger automatic fixes before a crash happens.

Why it mattersThis helps data center managers keep their services running smoothly by spotting hardware problems early rather than waiting for a server to fail.

In the filing’s own words

A computing device may be configured to receive telemetry data from multiple machines in a distributed computing environment, including time-series metrics and categorical features associated with machine-level operations. The telemetry data is processed using a machine learning model to generate predictive output, such as failure probabilities, time-to-failure estimates, or categorical health classifications. This predictive output is stored in association with machine identifiers and contextual information. As new telemetry data is received, the stored machine health data is periodically updated with refreshed predictions. Portions of the stored health data may be output for display to a user device to enable monitoring or trigger automated mitigation actions. Based on the displayed information, processing instructions may be received to initiate mitigation actions for selected machines, enabling proactive management of hardware resources based on anticipated reliability outcomes.

What exactly they claim (claim 1)

1. A method comprising: receiving, by one or more processors from a plurality of machines in a distributed computing environment, telemetry data comprising time-series metrics and categorical features associated with machine-level operations; processing, by the one or more processors using a machine learning model, the telemetry data to generate a predictive output for each machine; storing, by the one or more processors, the predictive output in association with corresponding machine identifiers and telemetry context for the plurality of machines as stored machine health data; periodically updating, by the one or more processors using the machine learning model, the stored machine health data with new predictive output based on newly received telemetry data for the plurality of machines; outputting, by the one or more processors and for display by a user device, at least a portion of the stored machine health data; and receiving, by the one or more processors, processing instructions for one or more of the plurality of machines in response to outputting the portion of the stored machine health data.

Facts

Company
Google (Google LLC)
Official title
FORECASTING MACHINE FAILURES TOWARDS IMPROVING MACHINE LEARNING WORKLOAD RELIABILITY
Publication number
US20260310532A1
Application number
19568594
Filed
Mar 16, 2026
Published
Oct 8, 2026
Inventors
8
Claims
20

Classified under

G06F 11/004 Physics & computingG06F 11/008 Physics & computingG06F 2201/81 Physics & computing
Read the full filing (official PDF) ↗ Open in the Patent Tracker

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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.