Context-Based Command Selection for Ambiguous GUI Inputs
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Solution Overview
Problem
Graphical user interfaces (GUIs) struggle to accurately interpret user inputs that have multiple possible meanings, often leading to incorrect operations and resource wastage due to ambiguity in user text inputs across applications.
Innovation Solution
A system that utilizes context data, including user profiles and displayed content, to disambiguate user inputs by applying a machine learning model to predict the most likely operation, thereby selecting the correct interpretation for user-entered terms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a system initiates operations based on user text input without disambiguation, then the system responds quickly to user input, but the system may initiate incorrect operations leading to wasted time and resources
Solution Approach 1:
The system performs preliminary disambiguation analysis by analyzing context data (displayed content, user profile, historical queries) before initiating operations. This preliminary action resolves ambiguous terms in advance, ensuring the correct application and operation are selected, thereby preventing incorrect operations while maintaining quick response times.
2Reliability
If the system analyzes context data to resolve ambiguity, then the system improves operation accuracy, but the system increases processing time and computational resources
Solution Approach 1:
The system applies context-based disambiguation selectively rather than universally. It analyzes context data (displayed content, user profile, historical queries) only when ambiguity is detected in user input. This localized approach resolves ambiguity accurately when needed while avoiding unnecessary processing for clear inputs, thus balancing accuracy with processing efficiency.
3Adaptability or versatility
If the system provides multiple possible operations for ambiguous input, then the system maintains flexibility, but the system requires additional user interaction increasing complexity
Solution Approach 1:
The system performs self-service by automatically resolving ambiguous terms using context data from displayed content, user profiles, and historical queries. Instead of presenting multiple possible operations to the user, the system autonomously determines the most likely intended operation and executes it directly, thereby maintaining flexibility while avoiding additional user interaction and interface complexity.
Data Source
AI summary
Techniques for initiating commands in a user interface by disambiguating user input terms are disclosed. As a system displays a set of data, the system receives a user input that includes a set of terms. The system determines the terms correspond to multiple different interpretations. The system selects a particular interpretation for the terms based on context data. The context data includes data, such as user profile data and display data. The user profile data includes data for a user entering the terms and data of other users related to the user entering the terms. The system selects and executes a command based on selecting the particular interpretation for the terms.