Amazon Technologies, Inc.

Facebook, Inc.

Savas Parastatidis

Ruhi Sarikaya

Rohit Prasad

Benoit F Dumoulin

Rajen Subba

Antoine Roland Raux

Wenhai Yang

Arpit Gupta

Abhinav Sethy

Stefan Nelson-Lindall

Dean Curtis

Pushpendre Rastogi

Nitin Ashok Jain

John Arland Nave

Mayank Kumar

Nakul Dahiwade

Arshdeep Singh

Nikhil Reddy Kortha

Ashish Kumar Agrawal

Arindam Mandal

Hung The Pham

Siddhartha Reddy Jonnalagadda

Prakash Krishnan

Raefer Christopher Gabriel

Shishir Sridhar Bharathi

Anushree Venkatesh

Abhay Kumar Jha

Hung Tuan Pham

David Chi-Wai Tang

Yun Lei

Arjit Biswas

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

Techniques for storing and using multi-session context are described. A system may store context data corresponding to a first interaction, where the context data may include action data, entity data and a profile identifier for a user. Later the stored context data may be retrieved during a second interaction corresponding to the entity of the second interaction. The second interaction may take place at a system different than the first interaction. The system may generate a response during the second interaction using the stored context data of the prior interaction.

Multi-session context

Exemplary embodiments relate to methods, mediums, and systems for managing a conversation. In an embodiment, a computer-implemented input interface is provided to receive an input comprising information in natural language. A dialog manager is configured to determine an intent of the input, determine information to fulfill the intent, and identify one or both of information available to the dialog manager or information that is unavailable to the dialog manager. A conversational understanding document documents the intent and the identified information. An output interface forwards the conversational understanding document towards a task completion handler separate and distinct from the dialog manager. Other embodiments are described and claimed.

Conversational understanding

Growing community of inventors

Kirkland, WA, United States of America

Savas Parastatidis