Gesture-Based Machine Learning for Rapid Activity Option Sorting
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Solution Overview
Problem
Existing interfaces for planning future activities are often cumbersome, requiring significant study and failing to provide a rapid and convenient way to sort through abundant sources of information.
Innovation Solution
The technology employs a system that identifies future activity options similar to a selected activity, past activities, or those meeting descriptive language criteria, forming compatible sequences or multi-day sequences, using gesture-based machine learning for dynamic sorting and inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional interfaces are used for planning future activities, then comprehensive information can be provided, but the interface becomes cumbersome and requires considerable study
Solution Approach 1:
The patent extracts and separates the sorting function from the traditional interface, using gesture-based machine learning to dynamically sort activity options based on user preferences. This removes the burden of navigating complex traditional interfaces while maintaining comprehensive information access.
Solution Approach 2:
The system performs automatic sorting and inference of user preferences through gesture-based machine learning, eliminating the need for users to manually navigate and study complex interfaces. The system serves itself by automatically organizing information based on learned user preferences.
2Quantity of substance
If traditional sorting methods are used, then all activity options can be displayed, but users cannot rapidly sort through them
Solution Approach 1:
The patent implements dynamic sorting where the system continuously adapts and reorganizes activity options based on real-time gesture-based machine learning inference of user preferences. This dynamic reorganization enables rapid sorting by automatically prioritizing relevant options rather than requiring manual filtering.
Solution Approach 2:
The system changes the sorting parameters dynamically based on inferred user preferences, transforming the static display of all activity options into a dynamically optimized sequence that prioritizes user-relevant options, thereby enabling rapid sorting without losing comprehensive information.
3Adaptability or versatility
If comprehensive activity information is provided, then users have more choices, but the interface becomes more complex
Solution Approach 1:
The patent segments the comprehensive activity information into dynamically sorted groups based on gesture-based machine learning inference of user preferences. This segmentation presents information in manageable, preference-based segments rather than a single complex unified interface, maintaining versatility while reducing perceived complexity.
Data Source
AI summary
The technology disclosed relates to identification of future activity options. In particular, it relates to identifying future activity options that are similar to a selected future activity, similar to past activities, that meet descriptive language criteria, that form compatible sequences of activities, or that form compatible multi-day sequences of activities. The technology disclosed also relates to rapid and convenient sorting through activity options.