ML-Guided User Interface Navigation for Adaptive Personalization
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Solution Overview
Problem
Existing user interfaces (UIs) struggle to adapt to diverse user needs, preferences, and device types, leading to inefficiencies and user frustration, particularly in a multi-device and multi-cultural environment.
Innovation Solution
A machine learning (ML) model is trained to optimize UI navigation routes based on user behavior, automatically adjusting UI components to provide personalized and efficient interactions without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed user interface design is used, then development and maintenance are simple, but the interface cannot adapt to diverse user needs and preferences
Solution Approach 1:
The patent implements dynamic UI customization where the interface automatically adapts to user preferences, device characteristics, and contextual factors in real-time. The system transitions from static design to dynamic adaptation by monitoring user interactions and adjusting interface elements accordingly, resolving the contradiction between adaptability and complexity through automated dynamic reconfiguration
Solution Approach 2:
The system employs machine learning models that enable the interface to self-customize based on user behavior patterns and preferences. The automated customization engine learns from user interactions and independently adjusts interface parameters without requiring manual configuration or complex development processes, allowing the system to serve itself in adapting to diverse user needs
2Ease of operation
If manual UI customization is implemented, then user preferences are respected, but it requires significant user effort and time
Solution Approach 1:
The system performs preliminary customization actions by pre-configuring interface parameters based on user profiles, device types, and predicted preferences before the user actually needs them. The machine learning model anticipates user needs and proactively adjusts the interface, eliminating the need for users to spend time on manual customization while still respecting their preferences
Solution Approach 2:
The automated customization engine enables the interface to self-adjust based on user behavior patterns and preferences without requiring user intervention. The system monitors interactions, learns from them, and automatically reconfigures the interface, freeing users from the time-consuming task of manual customization while maintaining ease of use
3Adaptability or versatility
If the UI is highly customized for each user, then user satisfaction increases, but development and maintenance complexity increases
Solution Approach 1:
The patent implements a universal automated customization engine that serves multiple functions: it monitors user interactions, trains machine learning models, generates personalized interface configurations, and adapts to various device types. This single multi-functional system enables high levels of personalization across diverse users and contexts without requiring separate development efforts for each customization scenario, resolving the contradiction between adaptability and development ease
Data Source
AI summary
Embodiments optimize a user interface (“UI”) of an application for a user. Embodiments train a machine learning (“ML”) model on one or more optimized routes for navigating the UI to arrive at a desired result. Embodiments monitor at least a portion of a first navigation route during a user interaction with the UI to achieve the desired result. Embodiments determine by the ML model that the first navigation route is not the one or more optimized routes and redirect the user to one of the optimized routes during the user interaction.