Call volume prediction
AssigneeZoom Video Communications, Inc.
InventorsDavide Giovanardi, Andrew Miller-Smith
Life of this patent
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Abstract
A sequence of call volume measurements is accessed, where each of the call volume measurements is associated with respective metadata. The respective metadata may provide information regarding a time period during which a call volume measurement was made. A window of the sequence of call volume measurements with the respective metadata is input to a machine learning model to obtain a prediction of a call volume. The machine learning model includes embedding functions that are applied to the respective metadata for the call volume measurements in the window.
Claims20
A method comprising:
- accessing a sequence of call volume measurements, wherein each of the call volume measurements is associated with respective metadata, and wherein the respective metadata provides information regarding a time period during which a call volume measurement was made;
- and
- inputting a window of the sequence of call volume measurements with the respective metadata to a machine learning model to obtain a prediction of a call volume, the machine learning model includes embedding functions that are applied to the respective metadata for the call volume measurements in the window, wherein the embedding functions are implemented with a set of parameters that control nonlinear functions applied to a discrete variable of the respective metadata for the call volume measurements in the window to map the discrete variable to a continuous vector space.
A system comprising:
- a processor, and
- a memory, wherein the memory stores instructions executable by the processor to:
- access a sequence of call volume measurements for one or more telephony servers, wherein each of the call volume measurements is associated with respective metadata, and wherein the respective metadata provides information regarding a time period during which a call volume measurement was made;
- and
- input a window of the sequence of call volume measurements with the respective metadata to a machine learning model to obtain a prediction of a call volume for the one or more telephony servers, wherein the machine learning model includes embedding functions that are applied to the respective metadata for the call volume measurements in the window, wherein the embedding functions are implemented with a set of parameters that control nonlinear functions applied to a discrete variable of the respective metadata for the call volume measurements in the window to map the discrete variable to a continuous vector space.
A non-transitory computer-readable storage medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:
- accessing a sequence of call volume measurements for a unified communications as a service platform, wherein each of the call volume measurements is associated with respective metadata, and wherein the respective metadata provides information regarding a time period during which a call volume measurement was made;
- inputting a window of the sequence of call volume measurements with the respective metadata to a machine learning model to obtain a prediction of a call volume for the unified communications as a service platform, wherein the machine learning model includes embedding functions that are applied to the respective metadata for the call volume measurements in the window, wherein the embedding functions are implemented with a set of parameters that control nonlinear functions applied to a discrete variable of the respective metadata for the call volume measurements in the window to map the discrete variable to a continuous vector space;
- and
- activating one or more telephony servers based on the prediction of the call volume for the unified communications as a service platform.
Dependencies are inferred from the claim text.