Intelligent List Reading via Request Specificity Detection
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Solution Overview
Problem
Intelligent automated assistants face challenges in providing intuitive and natural-sounding voice-based interactions, often resulting in overly verbose or unproductive responses due to varying degrees of user request specificity, which impacts user experience and adoption.
Innovation Solution
The system determines the specificity of user requests and generates targeted responses by refining vague requests and presenting data items in manageable subsets, based on attributes and information density, to provide useful and concise information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the digital assistant provides comprehensive spoken responses to user requests, then the information completeness is improved, but the response length and complexity increase causing awkward transitions and user fatigue
Solution Approach 1:
The patent segments the list of data items into multiple subsets and presents them across multiple turns of conversation. Instead of delivering all items at once, the system divides the comprehensive list into manageable portions, allowing users to process information gradually while maintaining access to the complete dataset through follow-up interactions.
Solution Approach 2:
The patent applies partial action by providing a subset of the complete data list in the initial response rather than the full comprehensive list. This partial delivery prevents information overload while still satisfying the user's core need, with the option to retrieve additional items through subsequent interactions if needed.
2Loss of information
If the digital assistant provides detailed spoken responses with multiple data items, then the information value is improved, but the user experience deteriorates due to too much information being provided at once
Solution Approach 1:
The system segments the comprehensive data list into multiple smaller subsets distributed across conversational turns. This segmentation maintains the total information value while improving ease of operation by presenting digestible portions that users can process without cognitive overload.
Solution Approach 2:
The patent implements dynamic response adjustment based on user feedback and context. The system adapts the amount and type of information provided in subsequent turns, modifying the information delivery to match user preferences and engagement levels, thereby optimizing both information value and user experience.
3Productivity
If the digital assistant provides concise spoken responses, then the response efficiency is improved, but the information completeness decreases resulting in too little information being provided
Solution Approach 1:
The patent resolves this contradiction by segmenting the complete information set into multiple efficient portions. Each individual response remains concise and efficient, while the cumulative information across the conversation sequence achieves complete information delivery, satisfying both response efficiency and information completeness requirements.
4Loss of information
If the digital assistant provides comprehensive lists of data items, then the information coverage is improved, but the processing time and cognitive load increase causing unproductive follow-up interactions
Solution Approach 1:
The system segments the comprehensive data list into multiple subsets delivered across different turns, reducing the processing time required for each individual response. Users can begin processing and acting on information sooner without sacrificing overall information coverage, as the segmented delivery allows for incremental understanding and action.
Data Source
AI summary
Systems and processes for operating an intelligent automated assistant to perform intelligent list reading are provided. In one example process, a spoken user request associated with a plurality of data items is received. The process determines whether a degree of specificity of the spoken user request is less than a threshold level. In response to determining that a degree of specificity of the spoken user request is less than a threshold level, one or more attributes related to the spoken user request are determined. The one or more attributes are not defined in the spoken user request. Additionally, a list of data items based on the spoken user request and the one or more attributes is obtained. A spoken response comprising a subset of the list of data items is generated and the spoken response is provided.