Adaptive Software Interface via Usage Mining
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Solution Overview
Problem
Software developers face challenges in anticipating user interaction patterns and collecting feedback, leading to inefficient user interface designs that do not adapt to individual user needs or frequent usage patterns across a large population.
Innovation Solution
A system that dynamically updates user interface features by processing local and global usage data using a data mining engine, which records operator interactions, generates local and global adjustments, and merges these to optimize user interface accessibility and layout based on usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the user interface is designed according to the designer's perception of user interaction, then the interface layout is initially organized, but it cannot adapt to actual usage patterns or individual user needs
Solution Approach 1:
The system implements feedback by automatically collecting and analyzing usage data from multiple sources including user interactions, survey responses, and usage statistics. This feedback loop enables the interface to learn from actual user behavior and systematically adapt its layout and organization to match observed usage patterns, resolving the contradiction between initial design organization and actual usability.
Solution Approach 2:
The interface transitions from a static design to a dynamic system that continuously evolves based on usage data. The interface layout and organization are no longer fixed but dynamically adjusted through automated analysis of usage patterns, allowing the system to adapt to changing user needs and behaviors over time.
2Loss of information
If developers collect feedback from users through surveys, then they can gather usage information, but users lack time and desire to respond
Solution Approach 1:
The system implements self-service by automatically collecting usage data directly from user interactions with the software, eliminating the need for users to manually complete surveys. The system autonomously gathers information about usage patterns, preferences, and behaviors through passive observation of interaction data, thereby recovering useful information without consuming user time.
Solution Approach 2:
The system introduces an intermediary mechanism that passively collects usage data between the user and the feedback collection process. Instead of directly asking users for feedback, the system uses intermediate data points from user interactions to infer usage patterns and preferences, thereby obtaining valuable information without requiring direct user engagement.
3Ease of operation
If the interface layout is based on design decisions, then the initial organization is established, but frequently used features may not be easily accessible
Solution Approach 1:
The system applies local quality by customizing the interface layout for different users based on their individual usage patterns. Instead of a uniform design, the interface organization is locally adapted to each user's specific needs and behaviors, placing frequently used features in optimal positions for that particular user while maintaining overall system consistency.
Solution Approach 2:
The system changes interface parameters such as layout configuration, feature positioning, and organization structure based on analyzed usage data. By dynamically adjusting these parameters according to observed usage patterns, the system optimizes feature accessibility without requiring complex manual reconfiguration, thereby improving ease of operation while managing interface complexity through automated parameter optimization.
Data Source
AI summary
A system for dynamically updating user accessible features of a software application on a client computer has a user interface, a local usage data file, and a data mining engine. The user interface is adapted to receive operator inputs. The local usage data file is adapted to store usage information corresponding to the operator inputs. The data mining engine is adapted to process the stored usage information and to generate local adjustments to a user interface of the software application based on the operator inputs. In one embodiment, a server is adapted to receive usage data from a plurality of application instances on a plurality of client computers and to generate global adjustments based on the received usage data. In one embodiment, the system has a merge feature adapted to blend and resolve conflicts between local and global adjustments to generate an interface adjustment for the user interface.