Amazon Technologies, Inc.

Wei Niu

Thomas Boyd Johnston

Nicholas Sun

Tak Chung Lung

Phillip H Keung

Pasquale DeMaio

Jon R Jay

Yung-Chun Lin

Yiwei Li

Manjeshwar Donthi

Fan Luo

Sharifa Monawer

Richard Julian Rodriguez

Daniel Stephen Edmiston

Yi-Hsin Chen

Jing Zhang

Spyridon Garyfallos

Vinay Kale

Correcting time series predictions for seasonality using trends may be performed. An initial time series prediction for a period of time using a machine learning model trained on a time series may be made. Data of the time series from more than an analysis time period prior to the period of time may be obtained. The time series prediction and the obtained data of the time series may be decomposed in respective components including a trend component. The respective trend component of the obtained data may evaluated to determine whether a trend replacement criteria is satisfied. The respective trend component of the time series prediction may be replaced with a portion corresponding to the period of time in a prior analysis time period from the respective component of the obtained data of the time series to correct the time series prediction for seasonality.

Correcting time series predictions for seasonality with trends

Techniques for document recommendation based on conversational log for real time assistance are described. A first machine learning module identifies key phrases of a conversational log in real time. The first machine learning module executes multiple machine learning models trained to determine a probability that a portion of a conversation includes a key phrase. A second machine learning module identifies assistance pertaining to the identified key phrases of the conversational log. The second machine learning module executes a machine learning model trained to identify semantic similarity and word matching features of embedding representations of the key phrases and a knowledge base of assistance. The assistance is provided to a user during a conversation in real time.

Document recommendation based on conversational log for real time assistance

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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Bellevue, WA, United States of America

Wei Niu