Rule-Based UI Composition With Machine-Learned Segmentation
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Solution Overview
Problem
Existing user interfaces struggle to dynamically and personally tailor UI content for complex interactions, often relying on static or compliance-based rules that fail to adapt to individual user behaviors and preferences.
Innovation Solution
A method and system for rule-based composition of user interfaces that utilize a machine-learned rule repository to segment users based on their features and UI states, estimating rules for preferred UI content and content similarity preferences, and dynamically updating the UI with personalized content based on user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static or compliance-based rules are used to present UI content, then the system is simple to implement, but the user interface cannot dynamically adapt to individual user behaviors and preferences
Solution Approach 1:
The patent implements dynamic user interface composition by transitioning from static rules to machine-learned rules that adapt in real-time. The system continuously observes user interactions, updates user profiles, and dynamically generates personalized UI content, making the interface flexible and responsive to individual user behaviors rather than following predetermined static patterns.
Solution Approach 2:
The system employs self-service mechanisms through automated machine learning models that continuously learn from user interactions without manual intervention. The user profile automatically updates based on observed behaviors, and the UI composition automatically adapts based on learned preferences, eliminating the need for manual rule configuration and enabling autonomous adaptation to user needs.
2Ease of operation
If machine learning models are used to personalize UI content, then user experience is improved, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary action by pre-processing user interactions and continuously updating user profiles in the background before actual UI composition is needed. Machine learning models learn user preferences ahead of time through observed behaviors, so when UI content needs to be generated, the personalization can be applied efficiently without intensive real-time computation, reducing energy consumption during critical user interactions.
Data Source
AI summary
A method for rule-based composition of user interfaces. A machine-learned rule repository is established based on previously observed combinations of UI states, UI features, and user features. A classifier classifies users into segments. Each segment includes users for which a combination of user features and UI states are defined. A first machine learning model (MLM) estimates a user segment-content preference including preferred UI content. A second MLM estimates a seen content-seen content similarity UI content preferences estimated according to prior UI content a user has seen. Based on the UI state and based on the user ID, rule-based recipes are obtained. Each rule-based recipe specifies a corresponding UI content suitable for an interaction between the user and the interface. A selected rule-based recipe is selected from the rule-based recipes. Specific UI content specified by the selected rule-based recipe is obtained, and the interface is updated with the specific UI content.