Amazon Technologies, Inc.

Phillip H Keung

Wei Niu

Pasquale DeMaio

Jon R Jay

Yung-Chun Lin

Thomas Boyd Johnston

Nicholas Sun

Tak Chung Lung

Yiwei Li

Fan Luo

Richard Julian Rodriguez

Manjeshwar Donthi

Techniques for action recommendation based on conversational log for real time assistance are described. Pre-generated intent clusters can be used to identify a relevant intent of a user in a given conversation between the user and a contact center agent. Based on the identified intent, certain recommended actions can be performed on the computing device of the contact center agent to facilitate the conversation between the user and the contact center agent. Feedback relating to the conversation and/or the recommended actions can be recorded and used to update the pre-generated intent clusters to improve the quality and relevance of the actions recommended for future conversations.

Computing action recommendation system for facilitating real time communication

Methods, systems, and computer-readable media for accurate usage forecasting for virtual contact centers are disclosed. A contact center management system configures a contact center instance associated with a client. The contact center instance comprises a plurality of queues configured to store contacts. At least a portion of the contacts are routed to a plurality of agents. The contact center management system determines, using one or more machine learning models associated with the contact center instance, a plurality of predictions for a plurality of metrics for the contact center instance for a plurality of time horizons. At least a portion of the predictions are generated for individual queues of the contact center instance.

Accurate individual queue level metric forecasting for virtual contact center queues with insufficient data, using models trained at higher granularity level

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Seattle, WA, United States of America

Phillip H Keung