Adaptive Digital Assistant Personalized Lexicon and Vocal Output
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Solution Overview
Problem
Conventional personal digital assistants lack personalization, using the same lexicon and vocal characteristics for all users, which reduces the quality and frequency of interaction and fails to cater to individual preferences in sound and voice characteristics, impacting user emotions and decision-making.
Innovation Solution
A computer-implemented method and system that selects a starter vocabulary based on user demographics and voice communications, generates a personalized lexicon, and adapts vocal output to match individual preferences, including pitch, cadence, and tonality, to provide a more human-like interaction and targeted content recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a conventional personal digital assistant uses the same lexicon and vocal characteristics for all users, then the system complexity is reduced and ease of manufacture is improved, but the personalization and user engagement are worsened
Solution Approach 1:
The system performs preliminary actions by collecting user voice communications and analyzing voice characteristics before personalization is needed. A starter vocabulary is pre-selected based on user demographics, and voice samples are collected during initial interactions to establish baseline preferences for pitch, cadence, and tonality.
Solution Approach 2:
The system dynamically adapts the lexicon and vocal characteristics based on user preferences. The personalized lexicon is generated by modifying the starter vocabulary with frequent words from user communications, and vocal output is continuously adjusted to match individual preferences in pitch, cadence, and tonality.
Solution Approach 3:
The system changes key parameters including vocabulary selection, pitch, cadence, and tonality based on user characteristics. The personalized lexicon modifies word selection, while vocal output parameters are adjusted to create a more human-like and personalized interaction experience.
2Adaptability or versatility
If the system adapts to individual user preferences with personalized lexicon and vocal characteristics, then user engagement and emotional connection are improved, but the device complexity and processing requirements increase
Solution Approach 1:
The system segments personalization into distinct modules: demographic-based vocabulary selection, voice communication analysis, frequent word list generation, and vocal characteristic adjustment. This modular approach allows each component to be developed and optimized independently.
Solution Approach 2:
The system serves itself by automatically analyzing user voice communications and generating personalized lexicons without requiring manual configuration. The frequent word list is automatically generated from user communications, and vocal characteristics are self-adjusted based on analyzed preferences.
3Measurement precision
If voice characteristics are analyzed and categorized to create spoken genome database, then content recommendation accuracy is improved, but the measurement and analysis complexity increases
Solution Approach 1:
The system changes measurement parameters by focusing on specific voice characteristics such as pitch, cadence, and tonality rather than attempting to measure all possible acoustic properties. This selective parameter approach maintains measurement precision while reducing analysis complexity.
Solution Approach 2:
The system applies different analysis methods to different aspects of voice characteristics. Spoken genome properties are categorized into specific dimensions (pitch, cadence, tonality), allowing targeted analysis of each property with appropriate measurement techniques rather than uniform complex analysis.
Data Source
AI summary
Embodiments of the invention include a context sensitive adaptive digital assistant for personalized interaction. Embodiments of the invention also include a spoken genome for characterization and analysis of human voice. Aspects of the invention include selecting a starter vocabulary, receiving voice communications from a user, and modifying the starter vocabulary to generate a personalized lexicon. Aspects of the invention also include analyzing and categorizing human voice according to a plurality of characteristics, and creating a spoken genome database.