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US 12,676,930 B2Granted
Predicting call volume using call volume data
AssigneeZoom Communications, Inc.
InventorsDavide Giovanardi, Andrew Miller-Smith
Life of this patent
Estimating the term…
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AssigneeZoom Communications, Inc.
InventorsDavide Giovanardi, Andrew Miller-Smith
Estimating the term…
A machine learning model (e.g., including a deep learning neural network) with learned embeddings is applied to time series data with associated metadata to obtain predictions of the time series value. For example, a call volume in a period of time may be predicted based on call volume data for a sequence of time bins in a window of preceding time. Time bins may be associated with respective metadata, such as day of week, hour of day, day of month, holiday, part of business cycle, weather, and/or tide. These pieces of metadata may be mapped to embedding vectors using trained embedding functions. The resulting embedding vectors may be input to a neural network along with the corresponding time series data (e.g., call volumes) to make a prediction for future time bin. For example, the prediction may be used to provision servers in a network infrastructure.
A method comprising:
A system comprising:
A non-transitory computer-readable storage medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:
Dependencies are inferred from the claim text.