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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Bibliographische Detailangaben
Hauptverfasser:	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
Sprache:	eng
Schlagworte:	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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Beschreibung
Zusammenfassung:	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.