Context-Aware Content Generation Framework for Proactive User Assistance
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Solution Overview
Problem
Current speech-controlled computing systems are limited in providing additional relevant content to users beyond their direct requests, lacking proactive output that enhances user experience based on contextual information.
Innovation Solution
A system that determines additional content related to user requests using contextual information such as user specifications and system processing results, soliciting this content from multiple applications to provide timely and useful information not explicitly asked for, thereby improving user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the system only provides content directly responsive to user commands, then the system complexity remains low and response time is fast, but the user experience is limited and lacks proactive assistance
Solution Approach 1:
The system performs preliminary analysis of user intent and contextual information before generating responses. The content generation component proactively determines additional content that the user may find useful based on the command context, user profile, and situational factors, rather than waiting for explicit user requests.
Solution Approach 2:
A content generation component acts as an intermediary between the speech processing system and the user. This component analyzes command context, user profiles, and situational information to generate additional relevant content, mediating between the basic command-response mechanism and enhanced user experience needs.
2Loss of information
If the system solicits additional content from multiple applications, then the relevance and usefulness of information provided to users improves, but the processing time and system resource usage increase
Solution Approach 1:
The system implements a tiered approach to content generation, starting with essential context-based content and optionally adding supplementary information. The content generation component can generate a base level of additional content quickly, with the option to enrich it further based on available resources and user needs, rather than always performing exhaustive analysis.
Solution Approach 2:
The system incorporates user feedback mechanisms to learn from user responses to additional content. This feedback loop allows the system to refine its content generation strategies over time, improving the quality and relevance of additional content while reducing processing time for commonly requested information patterns.
Data Source
AI summary
Techniques for performing outputting additional content associated with but nonresponsive to an input command are described. A system receives input data from a device. The system determines an intent representing the input data and receives first output data responsive to the input data. The system determines, based on context data, that additional content associated with the first output data but nonresponsive to the input data should be output. The system receives second output data associated with but nonresponsive to the input data thereafter. The system then presents first content corresponding to the first output data and second content corresponding to the second output data.