AI Voice Assistant Context-Based Language Switching
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Solution Overview
Problem
Existing digital virtual assistants (DVAs) are limited to responding in a single language and lack the ability to dynamically adjust language based on contextual interactions, failing to accommodate multiple users with varying language proficiency within audible proximity.
Innovation Solution
A computer-implemented method and system that enables a DVA to determine the context of a situation by detecting languages spoken in its vicinity and user identities, allowing it to dynamically select a response language based on historical correlations and user profiles, thereby providing responses in a second language when context demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a DVA responds in a single language, then the system complexity is low and operation is simple, but the adaptability to multiple users with varying language proficiency deteriorates
Solution Approach 1:
The patent implements dynamic language selection by enabling the DVA to automatically switch between languages based on real-time context analysis. The system monitors audio inputs, identifies multiple languages present, and dynamically adjusts the response language to match the detected context, transforming a static single-language system into a dynamic multi-language system without requiring manual configuration
Solution Approach 2:
The DVA performs self-service language selection by autonomously analyzing the linguistic context of the environment and determining the appropriate response language without user intervention. The system automatically detects languages spoken by nearby users, evaluates contextual factors, and selects the most appropriate language for its response, eliminating the need for manual language switching by users
2Adaptability or versatility
If a DVA dynamically selects language based on context, then the adaptability to multiple users improves, but the processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary language detection and context analysis before generating the actual response. By identifying the languages present in the environment and determining the appropriate response language in advance, the system prepares the linguistic context upfront, reducing the processing time required during the actual response generation phase
Solution Approach 2:
The patent replaces complex mechanical language switching mechanisms with automated audio-based context detection. Instead of requiring manual language selection or complex rule-based switching, the system uses audio analysis and machine learning to automatically determine the appropriate language, simplifying the decision-making process and reducing processing overhead
3Adaptability or versatility
If a DVA uses multiple languages for response, then the ability to accommodate multiple users improves, but the clarity and precision of language selection deteriorates without contextual analysis
Solution Approach 1:
The system implements feedback-based language selection by continuously monitoring the linguistic environment, analyzing the languages spoken by nearby users, and adjusting its response language based on this feedback. The DVA listens to the audio context, identifies the dominant languages, and uses this feedback to select the most appropriate language for its response, ensuring precise language matching to the current context
Solution Approach 2:
The patent changes the language parameter dynamically based on contextual analysis. Instead of using a fixed language setting, the system adjusts the language parameter in real-time by analyzing contextual factors such as the languages spoken by nearby users, the importance of the information being conveyed, and the identities of the users present, thereby achieving precise language selection adapted to each specific situation
Data Source
AI summary
The computer-implemented method provides for a digital virtual assistant (DVA) receiving input spoken in a first language by a user. The DVA determines a context of a current situation based on language and identity of individuals within a proximity of the DVA. The DVA determines whether the context of the current situation includes providing a response using a second language. In response to determining the context of the current situation calls for providing the response in the second language, the DVA determines the second language based on the context, and the DVA responds to the input spoken in the first language by the user, such that the response includes a dynamic selection of the second language and is based on an interaction context of the user and the DVA, and reference to a corpus of interaction context usage of the second language in a historically similar situation.