Digital Assistant Voice Profile Personalization
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Solution Overview
Problem
Digital assistants face challenges in providing personalized experiences to multiple users, as they struggle to accurately identify and respond to individual users without requiring proactive sign-in, and existing technologies lack efficient methods for analyzing voice inputs to determine user characteristics and preferences.
Innovation Solution
The system employs voice and acoustic analysis to create machine-learned voice and topical profiles for each user, allowing for personalized responses without sign-in, by clustering voice data, identifying user characteristics, and categorizing inputs into topical categories using natural language processing and machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If digital assistants use voice analysis and acoustic pattern recognition to identify users, then personalization capability is improved, but system complexity increases
Solution Approach 1:
The system segments user identification into multiple independent analysis components: acoustic pattern recognition, language model analysis, and topical category classification. Each component processes specific aspects of voice input separately, then combines results to achieve accurate user identification without requiring a monolithic complex system
Solution Approach 2:
The patent introduces machine-learned voice profiles and topical profiles as intermediary data structures that mediate between raw voice input and user identification. These profiles serve as intermediate representations that simplify the matching process between current voice input and stored user characteristics
2Ease of operation
If digital assistants require proactive sign-in for user identification, then system complexity is reduced, but user convenience deteriorates
Solution Approach 1:
The system implements self-service user identification where the digital assistant automatically identifies users through their voice characteristics without requiring manual sign-in actions. The acoustic pattern recognition and language model analysis perform user identification autonomously based on voice input alone
Solution Approach 2:
The system performs preliminary voice analysis and acoustic pattern matching during the initial voice input phase, preparing user identification data before any explicit user action is required. This preliminary processing enables immediate personalization without waiting for sign-in
3Measurement precision
If digital assistants analyze multiple user characteristics and preferences, then response relevance is improved, but processing time increases
Solution Approach 1:
The system applies partial action by focusing analysis on the most discriminative acoustic patterns and language features for user identification, rather than analyzing all possible voice characteristics equally. This selective approach achieves accurate user identification with reduced processing overhead
Solution Approach 2:
The patent implements preliminary categorization of voice inputs into topical categories using language models, preparing structured data in advance that accelerates subsequent user identification and response generation. This preliminary structuring reduces processing time for detailed analysis
Data Source
AI summary
In non-limiting examples of the present disclosure, systems, methods and devices for providing personalized experiences to a computing device based on user input such as voice, text and gesture input are provided. Acoustic patterns associated with voice input, speech patterns, language patterns and natural language processing may be used to identify a specific user providing input from a plurality of users, identify user background characteristics and traits for the specific user, and topically categorize user input in a tiered hierarchical index. Topically categorized user input may be supplemented with user data and world knowledge and personalized responses and feedback for an identified specific user may be provided reactively and proactively.