Amazon Technologies, Inc.

Arpit Gupta

Ruhi Sarikaya

Anjishnu Kumar

Xing Fan

Rohit Prasad

Abhinav Sethy

Savas Parastatidis

Arshdeep Singh

Dean Curtis

Nakul Dahiwade

Nikhil Reddy Kortha

Pushpendre Rastogi

Nitin Ashok Jain

John Arland Nave

Mayank Kumar

Hung The Pham

Markus Dreyer

Faisal Ladhak

William Clinton Dabney

Hung Tuan Pham

Techniques for determining a command or intent likely to be subsequently invoked by a user of a system are described. A user inputs a command (either via a spoken utterance or textual input) to a system. The system determines content responsive to the command. The system also determines a second command or corresponding intent likely to be invoked by the user subsequent to the previous command. Such determination may involve analyzing pairs of intents, with each pair being associated with a probability that one intent of the pair will be invoked by a user subsequent to a second intent of the pair. The system then outputs first content responsive to the first command and second content soliciting the user as to whether the system to execute the second command.

Proactive command framework

Techniques for recommending a skill experience to a user after a user-system dialog session has ended are described. Upon a dialog session ending, the system uses a first machine learning model to determine potential intents to recommend to a user. The system then uses a second machine learning model to determine a particular skill and intent to recommend. The system then prompts the user to accept the recommended skill and intent. If the user accepts, the system calls the recommended skill to execute. As part of calling the skill, the system sends to the skill at least one entity provided in a natural language user input of the ended dialog session. This enables the skill to skip welcome prompts, and initiate processing to output a response based on the intent and the at least one entity of the ended dialog session.

User-system dialog expansion

A voice user interface (VUI) system use collaborative filtering to expand its own knowledge base. The system is designed to improve the accuracy and performance of the Natural Language Understanding (NLU) processing that underlies VUIs. The system leverages the knowledge of system users to crowdsource new information.

Voice user interface knowledge acquisition system

Growing community of inventors

Seattle, WA, United States of America

Arpit Gupta