Amazon Technologies, Inc.

Anuroop Arora

Atul Deo

Swaminathan Sivasubramanian

Ramesh M Nallapati

Vikram Sathyanarayana Anbazhagan

Ashish Singh

Vasanth Philomin

Henghui Zhu

Jessie Young

Dejiao Zhang

Wei Xiao

Bing Xiang

Andrew Oliver Arnold

Xiaofei Ma

Srikanth Prabala

Sai Bharadwaj Kanduri

Arvind Arikatla

Harsh Yadav

Kaustubh Kishor Khanke

Priyanka Shirish Kale

Colin Thomas Davidson

Yasser El-Haggan

Systems and methods develop and apply one or more extractive summarization models for locating contact center conversation details in a transcript, extracting pertinent verbiage, and, in a transformation of the communication details, automatically generating summaries at one or more levels of abstraction, the summaries in full sentences, in a manner that a contact center agent understands. The models are trained using machine learning algorithms.

Systems and methods for automated communication summarization

Systems and methods to detect themes in contacts data. Contacts data may be encoded as text (e.g., chat logs), audio (e.g., audio recordings), and various other modalities. Text-based transcripts of contacts data may be parsed into turns, an issue turn may be detected using a machine learning model, a key phrase may be extracted from the issue turn. Key phrases from across multiple contacts data may be clustered to identify themes.

Programmatic theme detection in contacts analytics service

Systems and methods to implement customer contact service with real-time supervisor assistance. A supervisor may oversee multiple agents in a customer contact service. A service of a computing resource service provider may monitor a plurality of audio connections at a service of a computing resource service provider, generate transcripts for the plurality of audio data, analyze the transcripts using a set of natural language processing (NLP) techniques to generate metadata, tag the transcripts with categories based at least in prat on the metadata, generate information for at least a portion of the plurality of connections based on the transcripts, metadata, and categories, and provide the information to a supervisor of the agents.

Customer contact service with real-time supervisor assistance

Systems and methods to analyze contacts data. Contacts data may be encoded as text (e.g., chat logs), audio (e.g., audio recordings), and various other modalities. A computing resource service provider may implement a service to obtain audio data from a client, transcribe the audio data, thereby generating text, execute one or more natural language processing techniques to generate metadata associated with the text, processing at least the metadata to generate an output, determine whether the output matches one or more categories, and provide the output to the client. Techniques described herein may be performed as an asynchronous workflow.

Systems and methods to analyze customer contacts

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Seattle, WA, United States of America

Anuroop Arora