Adaptive Right-Click Menu Ordering Based on User Selection Patterns
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Solution Overview
Problem
The fixed rank or order of options in right-click menus can be frustrating for users, as usage patterns vary significantly among individuals, leading to inefficient selection processes, particularly in applications like RSA Archer where users frequently need to scroll to access frequently used options.
Innovation Solution
An automated tool that learns and customizes the right-click usage patterns of users by recording their selections, assigning weights to recent data, and providing personalized recommendations for option ordering, allowing users to accept or reject the changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the option-selection menu presents a fixed rank or order, then the menu structure is simple and consistent, but users frequently need to scroll to access frequently used options, reducing usability
Solution Approach 1:
The patent applies dynamics by transforming the static, fixed-order menu into a dynamic, adaptive menu that automatically reorders options based on learned user behavior patterns. The system continuously monitors user selections and adjusts the menu ranking in real-time, allowing frequently used options to rise to the top while maintaining a simple presentation interface for the user.
Solution Approach 2:
The system implements self-service by enabling the menu to automatically learn and adapt to user preferences without requiring manual configuration. The menu autonomously tracks usage patterns, generates reordered versions, and presents recommendations, freeing users from manually customizing their menu experiences while still providing the option to accept or reject suggestions.
2Adaptability or versatility
If the menu presents all options in a fixed order, then the menu content is complete, but users with varying usage patterns experience frustration and inefficiency
Solution Approach 1:
The system applies preliminary action by proactively analyzing user behavior patterns and preparing customized menu orderings before the user needs them. By continuously learning from past interactions and pre-computing optimized menu sequences, the system reduces the time users spend searching for options in future interactions, as the menu is already arranged according to predicted user needs.
Solution Approach 2:
The patent implements feedback by creating a closed-loop system where user menu interactions are continuously monitored and fed back into the learning algorithm. This feedback mechanism allows the system to refine its understanding of individual user patterns over time, progressively improving menu personalization and further reducing the time users need to locate their desired options.
3Ease of operation
If manual customization is required for each user, then the menu can be perfectly tailored, but the setup process is complex and time-consuming
Solution Approach 1:
The system applies self-service by automatically performing the customization task that would otherwise require manual user input. The menu autonomously observes user behavior, infers preferences, and generates personalized orderings without requiring users to navigate complex customization interfaces or explicitly configure settings, thereby simplifying the process while achieving tailored results.
Solution Approach 2:
The patent uses feedback to create an iterative customization process where the system continuously monitors user interactions and automatically adjusts menu orderings based on observed patterns. This feedback-driven approach eliminates the need for manual setup while still achieving highly personalized menus, as the system learns and adapts through ongoing interaction rather than requiring initial complex configuration.
Data Source
AI summary
Data is collected during use of an application program to determine an option-selection usage pattern for a given user with respect to a given option-selection list with options listed in a first order. A recommendation is generated for a modified option-selection list with the options listed in a second order based on the option-selection usage pattern. The recommendation is presented to the given user.