Dynamic Menu Pruning via Usage Data Analysis
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Solution Overview
Problem
Complex menu hierarchies in applications make it difficult for new users to learn and burdensome for experienced users, as existing solutions do not provide adequate menu item recommendations or pruning to streamline usage.
Innovation Solution
A method that aggregates historical menu usage data to generate key process association rules, scores menu recommendations based on frequency and confidence, and prunes menu items below a threshold rank to present a simplified set of recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a complete menu hierarchy is presented to users, then all functionality and options are available, but the menu becomes complex and difficult to navigate
Solution Approach 1:
The patent extracts and removes less frequently used menu items from the complete menu hierarchy, retaining only the most relevant and frequently accessed items. This extraction process uses historical usage data to identify and eliminate redundant or rarely used menu options, thereby simplifying the menu structure while preserving core functionality.
Solution Approach 2:
The patent applies different levels of detail to different parts of the menu based on usage frequency and relevance. Frequently used menu items are prominently displayed with detailed options, while less used items are either simplified or hidden. This local differentiation optimizes the menu for common tasks while maintaining access to less frequent functions.
2Adaptability or versatility
If all menu items are displayed, then users have complete options, but new users find it difficult to learn and navigate
Solution Approach 1:
The patent performs preliminary analysis of historical menu usage data to pre-determine which menu items are most frequently and effectively used. This preliminary action allows the system to pre-organize and pre-highlight the most relevant menu items, creating a learned and optimized menu structure that guides new users toward commonly needed functions without overwhelming them with less relevant options.
Solution Approach 2:
The patent incorporates feedback from historical usage data to continuously refine and update the menu structure. By analyzing actual user behavior patterns, the system adjusts menu item rankings, groupings, and visibility to better match user needs, making the menu progressively more intuitive as it learns from usage patterns.
3Adaptability or versatility
If experienced users access the complete menu, then all options are available, but the menu becomes burdensome and time-consuming
Solution Approach 1:
The patent applies partial action by presenting only the essential and most frequently used menu items to experienced users, rather than the complete menu. This partial presentation is justified by usage data showing that a subset of menu items accounts for the majority of user tasks, allowing experienced users to accomplish their goals faster without needing access to all possible options.
Solution Approach 2:
The patent makes the menu dynamic by adjusting its content and structure based on user behavior patterns and usage frequency. The menu automatically reorganizes and adapts to user needs, promoting frequently accessed items and hiding less relevant ones, thereby optimizing navigation speed and efficiency for both new and experienced users.
Data Source
AI summary
Using a set of menu to key process mappings, historical menu usage data for an application is aggregated into aggregated key process usage data. A set of key process association rules, each comprising a consequent key process given a particular antecedent key process, is generated. From the set of key process association rules and a set of ranked menus by frequency of usage within each key process, a set of model menu recommendations is generated. According to an application usage history, a menu frequency ratio, and a confidence value of a modelled next menu, the set of menu recommendations is scored. A scored menu recommendation having a rank below a threshold rank is pruned from a set of menu items of the application ranked according to their scores. The pruned set of scored menu recommendations is presented for selection instead of the set of menu items.