Dynamic Interface Mode Switching for Noisy Voice Input
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Solution Overview
Problem
Existing user interfaces often have fixed input and output modes, which may not adapt to changing user environments or preferences, leading to inconvenience, especially in noisy or motion-based scenarios where voice input may be disrupted or difficult to read.
Innovation Solution
A method that receives audio input, extracts paralinguistic features, and uses a predictive model to determine and activate a preferred user interface mode based on these features, along with sensor data and user profile attributes, to dynamically adjust how the interface receives input and provides output, such as switching between touch screen, speaker, or display usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voice input is used for convenience, then ease of operation is improved, but reliability deteriorates in noisy environments
Solution Approach 1:
The system dynamically adjusts the interface mode based on real-time environmental conditions and user state. It transitions between voice input mode and alternative input modes (such as text input or haptic feedback) depending on noise levels, user activity, and contextual factors, making the system adaptable rather than static.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor environmental noise levels, user responses, and interaction patterns. Based on this feedback, it automatically adjusts the input method being used, switching from voice to alternative modes when voice reliability deteriorates due to noise or other factors.
2Adaptability or versatility
If interface mode is fixed in default setting, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The system automatically determines and switches between different interface modes without requiring explicit user configuration. It self-adjusts based on environmental sensors, user behavior patterns, and contextual information, eliminating the need for users to manually configure multiple interface settings.
Solution Approach 2:
The system integrates multiple input and output modes (voice, text, haptic, visual) into a single unified interface framework. This multi-functional design allows the same system to serve multiple interaction needs without requiring separate configurations for each mode.
3Adaptability or versatility
If alternative interface modes are provided, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system dynamically selects from multiple interface modes based on real-time conditions. Rather than presenting all modes simultaneously or requiring manual selection, it actively transitions between modes (voice, text, haptic, visual) based on environmental noise, user activity state, and contextual factors.
Solution Approach 2:
The system introduces an intelligent intermediary layer that automatically mediates between the user and multiple interface modes. This intermediary analyzes contextual factors and user preferences to seamlessly transition between different input/output modes without requiring users to understand or configure the underlying complexity.
4Ease of operation
If voice input is used during exercise, then ease of operation is improved, but measurement precision deteriorates due to motion disruption
Solution Approach 1:
The system dynamically adjusts the input mode based on detected user activity state. When exercise or motion is detected through sensors or analysis of voice characteristics, it transitions from voice-only input to alternative or hybrid modes that are more robust to motion disruption.
Solution Approach 2:
The system incorporates feedback from motion sensors and voice quality analysis to detect when the user is exercising or moving. Based on this feedback, it automatically adjusts the input method to compensate for motion-related degradation in voice recognition accuracy.
Data Source
AI summary
Systems of the present disclosure adjust an interface mode of an application based on paralinguistic features of audio input. The audio input is via a microphone associated with a computing device. A predictive model uses paralinguistic features of the audio input and additional features received from sensors or a user profile to predict an interface mode that a user would currently prefer to use. The interface mode specifies how output is provided and how input is received. The interface mode may also specify which elements of a graphical user interface are displayed, where the elements are placed, and how the elements are sized.