Adaptive Cash Handling Interface for User Skill Adaptation
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Solution Overview
Problem
Special purpose computing devices, such as cash handling devices, face challenges in accommodating varying user skills, leading to inconsistent operation and potential errors due to the need for universal instructional prompts that may not suit individual user competency levels.
Innovation Solution
Implementing a machine-learning supported user interface system that tracks user interactions to classify competency levels and dynamically modify interface displays, providing tailored instructions and layouts to enhance user efficiency and reduce errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If universal instructional prompts are provided for all users, then all users can access the device, but users with varying competency levels experience inconsistent operation and errors
Solution Approach 1:
The user interface dynamically adapts its complexity and instructional content based on the detected competency level of each user. The system transitions from static universal prompts to dynamic customized interfaces that adjust in real-time according to user performance metrics, thereby maintaining operation consistency across different user skill levels.
Solution Approach 2:
Different portions of the user interface are customized according to the specific needs of each competency level. The system applies local quality by providing detailed instructional prompts for novice users while presenting streamlined interfaces for experienced users, ensuring each user receives appropriately tailored interface elements rather than a uniform design.
2Ease of operation
If detailed instructional prompts are provided for novice users, then novice users can operate the device, but experienced users experience reduced efficiency due to unnecessary instructions
Solution Approach 1:
The system changes the parameter of instructional detail based on user competency level. For novice users, the interface parameter is set to high instructional density with step-by-step guidance. For experienced users, the parameter is adjusted to low instructional density, presenting only essential information, thereby maintaining ease of operation for novices while preserving productivity for experienced users.
Solution Approach 2:
The system applies partial action by providing instructional prompts selectively rather than universally. Novice users receive comprehensive instructional coverage, while experienced users receive only the partial instructions they actually need, eliminating excessive information that would otherwise reduce their operational efficiency.
3Productivity
If multiple user competency levels are accommodated with customized interfaces, then user efficiency improves, but system complexity increases
Solution Approach 1:
The system performs self-service by automatically detecting user competency levels and configuring the appropriate interface without requiring manual intervention from administrators. The interface autonomously adapts to each user's skill level, managing the complexity of multiple interface configurations through automated detection and selection, thereby improving user efficiency without proportionally increasing operational complexity.
Solution Approach 2:
The system performs preliminary action by pre-configuring multiple interface variations tailored to different competency levels and automatically selecting the appropriate one based on user detection. This preliminary preparation of interface options allows the system to quickly adapt to user needs without real-time complexity, as the customization work is prepared in advance and automatically applied.
Data Source
AI summary
Machine-learning based modelling may be utilized to automatically select a particular display variation of a user interface to be displayed to a user of a specially configured computing device, such as a cash handling device used in a retail establishment. To provide user-specific display variations of user interfaces, activity trackers generate activity data for the users interacting with user interfaces of the specially configured computing device. A machine-learning model is then executed to select an applicable display variation of a plurality of available display variations for the user, based at least in part on the generated activity data. Thereafter, when the specially configured computing device receives a request to display the user interface, the applicable display variation of the user interface is displayed for the user.