Amazon Technologies, Inc.

Atul Deo

Bing Xiang

Ramesh M Nallapati

Andrew Oliver Arnold

Anuroop Arora

Matthew Lee

Srinivas Iragavarapu

Ankur Deepak Desai

Swaminathan Sivasubramanian

Sudipta Sengupta

Parminder Bhatia

Vikram Sathyanarayana Anbazhagan

Ashish Singh

Vasanth Philomin

Yuchen Tian

Henghui Zhu

Jessie Young

Praphruetpong Athiwaratkun

Zijian Wang

Dejiao Zhang

Yanitsa Donchev

Wei Xiao

Rama Krishna Sandeep Pokkunuri

Alexander Johannes Smola

Rajesh Goli

Miguel Ballesteros Martinez

Xiaofei Ma

Vamshidhar Krishnamurthy Dantu

Sri Ranga Akhilesh Bontala

Srikanth Prabala

Dan Roth

Shinichi Kato

Yasser El-Haggan

Sai Bharadwaj Kanduri

Arvind Arikatla

Kaustubh Kishor Khanke

Saurabh Giri

Yassine Benajiba

Raghavendraprasad Raghunath Prasad

Colin Thomas Davidson

Mingyue Shang

Zixuan Lin

Ramana Keerthi

Hantian Ding

Wasi Uddin Ahmad

Sathish Arumugam Selvaraj

Venkat Rakshith Reddy Swamireddy

Lei Gao

Wei Fang

Priyanka Shirish Kale

Harsh Yadav

Code completion suggestions may be proactively obtained and validated. An event that triggers obtaining a code completion suggestion for inclusion in a code file being edited using an integrated development environment may be detected. The code completion suggestion may be obtained. The characters of the code completion suggestion may be compared with characters added to the code file after the detection of the event that triggered obtaining the code completion suggestion to determine whether the code completion suggestion is valid. A valid code completion suggestion may then be displayed.

Validating and providing proactively generated code completion suggestions for code development

Extraction of portions of natural language communications is performed to populate tables. An obtained communication may be associated with one, or more tables. The communication may include natural language data which may extracted and evaluated to predict different value mappings to the table. The value mappings may be confirmed or automatically made to the table.

Extracting data to populate tables from natural language communications

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

Evaluation data sets may be programmatically generated for code generation models. An evaluation data set is obtained that includes items that correspond to different evaluation tests for a code generation system. The individual items of the evaluation data set maybe converted, including the conversion of a function signature for the items, the test statements for the items and using a code generation system to generate the body of the function.

Programmatically generating evaluation data sets for code generation models

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

Pre-fix matching may constrain the generation of next token predictions. Input text to perform a next token prediction may be received. Multiple tokens may be determined from the input text, including a partial token. From possible tokens, one or more matching possible tokens with the partial token may be identified. Next token predictions may then be filtered using the identified possible tokens in order to ensure that the partial token is matched.

Constrained prefix matching for generating next token predictions

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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Kirkland, WA, United States of America

Atul Deo