User-system dialog expansion

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 mode...

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Bibliographic Details
Main Authors Rastogi, Pushpendre, Gupta, Arpit, Singh, Arshdeep, Pham, Hung Tuan, Kumar, Mayank, Nave, John Arland, Kortha, Nikhil Reddy, Curtis, Dean, Prasad, Rohit, Sethy, Abhinav, Sarikaya, Ruhi, Parastatidis, Savas, Jain, Nitin Ashok, Dahiwade, Nakul
Format Patent
LanguageEnglish
Published 13.12.2022
Subjects
ACOUSTICS
CALCULATING
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
MUSICAL INSTRUMENTS
PHYSICS
SPEECH ANALYSIS OR SYNTHESIS
SPEECH OR AUDIO CODING OR DECODING
SPEECH OR VOICE PROCESSING
SPEECH RECOGNITION
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Summary: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.
Bibliography:Application Number: US202017024959