Adaptive Voice Interface for User Expertise Ranking
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Solution Overview
Problem
Existing interactive voice response systems struggle to adapt their user interfaces to accommodate users of varying expertise levels, leading to difficulties in navigation and utilization of features, especially for beginner users while being insufficiently challenging for advanced users.
Innovation Solution
A method and system that rank users based on their expertise through a dialogue engine that receives attribute information during voice interactions, using a user rank classifier to determine thresholds and update user models, thereby adapting the user interface to match the user's rank.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the user interface is designed to be simple and easy to navigate, then beginner users can easily use the system, but advanced users cannot access advanced features and functionalities
Solution Approach 1:
The system dynamically adapts the user interface complexity based on the detected expertise level of the user. Beginner users receive simplified interfaces with basic features, while advanced users automatically gain access to complex functionalities and advanced options, allowing the same system to serve multiple expertise levels effectively
Solution Approach 2:
Different portions of the interface are tailored to different user needs based on their expertise level. The system applies local quality by providing simplified navigation and basic features to beginners while simultaneously making advanced features and detailed controls available to expert users, rather than applying a uniform interface design
2Adaptability or versatility
If the user interface includes comprehensive features and functionalities, then advanced users can utilize the system effectively, but beginner users find it difficult to navigate and understand
Solution Approach 1:
The interface dynamically adjusts its complexity based on user expertise detection. When a beginner user is identified, the system automatically hides or simplifies advanced features and provides guided navigation. When an expert user is detected, the full comprehensive feature set becomes accessible, allowing the system to maintain comprehensiveness while adapting to user capability
Solution Approach 2:
The user interface is segmented into different levels or layers of complexity. Basic functionality is presented to all users, while advanced features are segmented into separate accessible layers that are revealed or activated based on the user's detected expertise level, allowing comprehensive features to exist without overwhelming beginners
3Ease of operation
If the system provides simplified interfaces and user guides, then beginner users can understand the application, but advanced users find it time-consuming and unnecessary
Solution Approach 1:
The system uses feedback from user interactions to detect expertise level and automatically adjust the amount of guidance provided. Beginner users receive comprehensive user guides and simplified interfaces, while advanced users' interactions trigger a response that reduces or eliminates basic guidance, allowing the system to save time for expert users while maintaining support for beginners
Solution Approach 2:
The system applies partial action by providing user guides and simplified interfaces only to the extent needed based on detected user expertise. Advanced users receive minimal or no basic guidance since their expertise indicates they don't need it, while beginners receive full guidance, allowing the system to avoid wasting time on unnecessary explanations for experts
4Measurement precision
If the system collects detailed information about user attributes, then it can accurately determine user expertise and personalize the interface, but it increases system complexity and data processing requirements
Solution Approach 1:
The system collects user attribute information and uses feedback from analyzing this data to determine expertise level. The collected information about user behavior, preferences, and interaction patterns provides feedback that enables accurate expertise determination, justifying the data collection effort through improved personalization accuracy
Solution Approach 2:
The user attribute information collection system serves multiple functions: it detects expertise level, personalizes the interface, improves recommendation accuracy, and enables adaptive guidance. This multi-functionality justifies the system complexity by providing comprehensive benefits from a single data collection infrastructure
Data Source
AI summary
The present invention relates to systems and methods for adapting elements of a user interface of an interactive voice response system in a voice-based interaction based on rank of a user and more particularly to ranking user for their expertise. The method of ranking the user comprises a dialogue engine receiving information related to a plurality of attributes for a voice-based interaction. A user rank classifier determines user rank and updates a user model by a user model update component. Further, the dialogue engine adapts and provides the user with a user interface based on the user's rank corresponding to his expertise thereby, enhances engagement during interaction. This makes the interface more efficient, approachable and user-friendly. Moreover, it saves time and cost for both the system operations and the user.