Verbal Output Personalization for Context-Aware Attention
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current interactive computing devices, such as smart speakers and robots, offer limited personalization in verbal output, failing to adapt to different user contexts, which reduces their effectiveness in capturing attention.
Innovation Solution
A system that analyzes historical interactions to determine optimal output parameters like name, volume, cadence, and language, and modifies verbal output based on current context, including environmental noise and user activity, to enhance attentiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If verbal output uses predefined voice styles only, then device complexity is reduced, but adaptability to different user contexts deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting verbal interaction data during normal operation and pre-processing it into a corpus with annotated effectiveness measures. This preparation work is done in advance so that when contextual adaptation is needed, the system can quickly query and apply pre-analyzed patterns rather than performing complex real-time analysis, thus achieving adaptability without proportionally increasing operational complexity
Solution Approach 2:
The patent introduces an intermediary component that acts as a bridge between the simple predefined voice styles and the complex real-time contextual analysis. This intermediary processes historical data to create lookup tables and patterns that map contexts to effective verbal output parameters, allowing the system to achieve sophisticated adaptation through pre-computed intermediaries rather than complex real-time processing
2Reliability
If verbal output is highly personalized based on historical analysis, then user attentiveness is improved, but loss of information increases due to data processing requirements
Solution Approach 1:
The system extracts only the essential and relevant features from historical verbal interaction data, focusing specifically on elements that correlate with user attentiveness and engagement. Rather than processing and storing complete raw interaction records, the system extracts key patterns such as effective voice styles, optimal timing, and contextual triggers, thereby achieving reliable personalized output while minimizing data processing overhead by taking out only the necessary information
3Measurement precision
If the system collects and analyzes extensive verbal interaction data, then measurement precision of user preferences improves, but device complexity increases
Solution Approach 1:
The system applies local quality by focusing measurement and analysis precision on specific local aspects of verbal interactions that most directly impact user attentiveness, rather than attempting to analyze all possible dimensions of every interaction. It identifies and concentrates computational resources on key local features such as voice tone effectiveness, timing patterns, and contextual relevance, achieving high measurement precision for critical parameters while avoiding the complexity of comprehensive analysis
Data Source
AI summary
A method includes: determining, by a computer device, a current context associated with a user that is the target audience of an unprompted verbal output of an interactive computing device; determining, by the computer device, one or more parameters that are most effective in getting the attention of the user for the determined current context; and modifying, by the computer device, the unprompted verbal output of the interactive computing device using the determined one or more parameters.