Dynamic Application Icon Reordering via Machine Learning
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Solution Overview
Problem
Conventional computing systems fail to effectively prioritize and discover software applications based on user behavior, relying on favorites, user-defined groups, or last used/most used approaches, which do not account for location, time, or similarity with other users, leading to suboptimal user experiences and requiring initial training with limited data.
Innovation Solution
Implementing a machine-learning algorithm that determines weighting values for software applications based on user circumstances, behavior histories of similar users, and software launch patterns, dynamically reordering application icons in a graphical user interface to predict which applications a user is likely to use given their current location, time, and organizational position.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional computing systems use favorites or most used approaches to prioritize applications, then the system is simple to implement, but the system cannot account for location, time, or user similarity leading to suboptimal user experience
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing user behavior data, location information, and organizational data in advance. Machine learning models are trained beforehand to predict application usage patterns, allowing the system to proactively prioritize applications before the user needs them, rather than simply reacting to past usage patterns.
Solution Approach 2:
The patent introduces machine learning models and behavioral analysis systems as intermediaries between the user and the application list. These intermediaries process complex data including location, time, user similarities, and organizational context to generate prioritized application recommendations, shielding the user from the underlying complexity while delivering enhanced user experience.
2Measurement precision
If the system collects and analyzes extensive user behavior data and organizational data, then the prediction accuracy improves, but the data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data processing and model training in advance, building machine learning models that can quickly make predictions without requiring extensive real-time computation. User behavior patterns and organizational data are analyzed beforehand to establish baseline models that enable fast, accurate predictions when needed.
Solution Approach 2:
The patent implements a hybrid approach where the system uses pre-computed models for quick predictions and only performs deeper analysis when necessary or when sufficient data is available. This allows the system to provide timely predictions even with limited data, while improving accuracy progressively as more data becomes available.
3Adaptability or versatility
If the system uses machine-learning algorithms to dynamically reorder applications, then the application prioritization becomes adaptive to user circumstances, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent segments the machine learning system into multiple components: data collection modules, feature extraction modules, model training modules, and prediction modules. This segmentation allows each component to be optimized independently and distributed across different computing resources, reducing the computational burden on any single device while maintaining adaptability.
Solution Approach 2:
The system implements universal machine learning models that can serve multiple functions: predicting application usage, identifying user similarities, analyzing organizational patterns, and adapting to different contexts. These multi-functional models reduce the need for separate specialized systems, managing computational complexity while maintaining versatility.
4Measurement precision
If the system requires initial training with user data, then the predictions become more accurate for that user, but new users experience delays until sufficient data is collected
Solution Approach 1:
The patent merges individual user data with organizational data and data from similar users to create a hybrid training dataset. This combination allows new users to benefit from organizational patterns and similarities with other users immediately, while their personal data continues to be collected and integrated over time, providing accurate predictions from day one that improve progressively.
Solution Approach 2:
The system performs preliminary analysis of organizational data and user similarities in advance to create baseline predictions for new users before sufficient personal data is available. This preliminary action enables immediate useful predictions while personalization continues to develop as the user generates more data.
Data Source
AI summary
Systems and methods for ordering software applications in a computing environment. The methods involve: presenting user-selectable icons for launching a plurality of software applications in a graphical user interface in accordance with a first order; performing a machine-learning algorithm to determine a weighting value for each software application of the plurality of software applications based on information specifying at least one aspect of a software launch request and at least one aspect of a first user's current circumstance; determining a second order in which the user-selectable icons should be presented in the graphical user interface based on the weighting values determined for the software applications; and dynamically modifying the graphical user interface to present the user-selectable icons in the second order which is different from the first order.