Text Story Reading Detection With Context-Aware Physical Effects
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Solution Overview
Problem
Traditional virtual assistants struggle to accurately detect when a user has finished reading a text source aloud, leading to unnecessary resource consumption and potential recording of private conversations due to non-linear reading behaviors such as skipping, repeating, or adding content.
Innovation Solution
A method that enhances virtual assistants by comparing audio data with text source data using phoneme analysis and fuzzy matching to determine when a user has discontinued reading, allowing for the deactivation of audio processing and resource conservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the virtual assistant continuously monitors and processes audio data to detect reading discontinuation, then the accuracy of detecting when reading has stopped is improved, but the power and resource consumption increases
Solution Approach 1:
The system performs audio processing at periodic intervals rather than continuously. The processor compares audio data to text source data at specific intervals, enabling detection of reading discontinuation while reducing overall processing time and power consumption compared to continuous monitoring.
Solution Approach 2:
The system performs preliminary comparisons of audio data segments against the text source data before determining whether reading has discontinued. By pre-processing and comparing audio segments in advance, the system can quickly identify when reading has stopped without requiring continuous heavy processing.
2Reliability
If the virtual assistant continuously records audio data to ensure no private conversations are missed, then the reliability of privacy protection is improved, but the risk of recording private conversations increases
Solution Approach 1:
The system uses feedback from the comparison between audio data and text source data to dynamically control recording. When the comparison indicates the user is still reading (audio matches text), recording continues. When the comparison shows a mismatch (reading discontinued), recording stops, thus protecting privacy without missing relevant content.
Solution Approach 2:
The system automatically detects reading discontinuation through self-monitoring of the audio-text correspondence and autonomously controls the recording state. This self-service mechanism ensures privacy protection without requiring external intervention, reliably stopping recording when appropriate while maintaining continuous monitoring capability.
3Productivity
If the system processes audio data in real-time to provide synchronized special effects, then the user experience is improved, but the computational complexity increases
Solution Approach 1:
The system segments audio data into discrete segments or frames for processing. By dividing the continuous audio stream into manageable segments and comparing each segment against corresponding text portions, the system achieves real-time processing with reduced computational complexity compared to analyzing the entire audio stream simultaneously.
Data Source
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AI summary
The disclosure provides technology for enhancing the ability of a computing device to detect when a user has discontinued reading a text source. The disclosures also provides techniques for initiating a physical effect corresponding to a text source being read aloud by a user. When determining that a match exists between a spoken word of the user and the text source, a physical effect is initiated in response to the determined match and corresponding to the text source, based on contextual data indicative of a user context. The contextual data including sound data, light data of an environment of the user. The contextual data also including a time data, weather data, calendar data or user profile data including an age of a child user.