Generative AI Interface Layouts for Personalized Multi-Device Content
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wealth management software lacks the ability to dynamically adapt user interface content based on individual user preferences and integrate external data sources effectively, leading to suboptimal user experiences and inefficient information presentation.
Innovation Solution
Implementing a generative artificial intelligence (GenAI) model that learns user interface preferences and integrates external data sources to dynamically generate and arrange content on user interfaces, utilizing machine learning and neural networks to analyze user interactions and external profile data for personalized and synchronized content display across devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If wealth management software uses static dashboard layouts with fixed content positions, then the interface is simple and easy to operate, but it cannot adapt to individual user preferences and provides suboptimal user experiences
Solution Approach 1:
The patent implements dynamic dashboard layouts where content positions, types, and arrangements are automatically adjusted based on individual user preferences and behaviors. The system transitions from static to dynamic interfaces by continuously learning user interactions and externalating personalized configurations, resolving the contradiction between adaptability and complexity through intelligent automation.
Solution Approach 2:
The system performs self-configuration by automatically learning user preferences from interaction patterns and external data sources without requiring manual setup. The AI model autonomously determines optimal dashboard layouts, content prioritization, and information presentation based on analyzed user behaviors, eliminating the need for complex manual configuration while achieving high adaptability.
2Quantity of substance
If the software integrates multiple external data sources to enrich user profiles, then the system provides more comprehensive and relevant information, but the complexity of data integration and synchronization increases
Solution Approach 1:
The patent introduces an AI model as an intermediary layer that abstracts the complexity of multiple external data sources. The system connects to various data sources through standardized interfaces, and the AI model processes, reconciles, and synthesizes information from these sources into unified user profiles, masking the underlying integration complexity from the user while providing comprehensive information.
Solution Approach 2:
The system implements a universal data integration framework that can connect to multiple external data sources through a common AI processing layer. This multi-functional approach allows the same integration mechanism to handle diverse data sources (social media, professional networks, public records) uniformly, reducing overall system complexity while increasing the quantity and variety of available profile information.
3Measurement precision
If the system processes and analyzes large amounts of user interaction data and external profile data to generate personalized content, then the content relevance and personalization improve, but the computational resources and processing time increase
Solution Approach 1:
The system performs preliminary data processing and feature extraction when user profiles are initially created or significantly updated. The AI model pre-processes large amounts of data from multiple sources, extracts meaningful features, and creates optimized representations before they are needed for dashboard generation. This batch pre-processing reduces real-time computational requirements while maintaining high personalization precision.
Solution Approach 2:
The patent applies different processing intensities to different aspects of data handling. Critical real-time decisions about content placement and prioritization use optimized AI models with reduced computational complexity, while comprehensive data analysis and profile enrichment are performed through batch processing. This local quality approach balances personalization precision with computational resource consumption by applying intensive processing only where necessary.
Data Source
AI summary
An example operation includes one or more of establishing a communication session between a first user device and a second user device from among the plurality of user devices, training an artificial intelligence (AI) model to learn user interface preferences of a plurality of user devices during the communication session, receiving a description associated with the communication session, generating a plurality of windows of content and displaying the plurality of windows of content on a user interface of the first user device during the communication session based on execution of the AI model on the description associated with the communication session, and generating a second plurality of windows of content and displaying the second plurality of windows of content on a user interface of the second user device based on an execution of the AI model on the description associated with the communication session.