Context-Aware Interface Component Selection for Personalized UX
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Solution Overview
Problem
Conventional online systems provide one-dimensional product suggestions based on limited input, such as product category or genre, failing to offer a multi-dimensional user experience by not analyzing collective user activity over time.
Innovation Solution
A system and method that collect user feedback to customize the user interface by selecting optimal interface components based on scores, utilizing context information like site, buyer segmentation, domain, and keyword data to generate a personalized and revenue-maximizing user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional one-dimensional product suggestion systems are used, then the system simplicity is maintained, but the user experience quality deteriorates due to lack of multi-dimensional context analysis
Solution Approach 1:
The system segments the user experience customization into multiple independent decision points, each handling a specific aspect (product selection, interface component selection, configuration selection). This allows the complex multi-dimensional customization to be broken down into manageable one-dimensional decisions at each step, resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The patent introduces multiple dimensions of context analysis (user preferences, behavior patterns, device characteristics, environmental factors) to transform the conventional one-dimensional product suggestion into a multi-dimensional personalized experience. This adds versatility without proportionally increasing system complexity by organizing dimensions hierarchically.
2Measurement precision
If multi-dimensional context analysis is implemented, then the user experience quality is improved, but the computational resources required increase
Solution Approach 1:
The system performs preliminary actions by pre-collecting and organizing context information (user profiles, behavior data, device characteristics) before the actual product suggestion process. This preparation work is done in advance, allowing the runtime system to make rapid decisions without intensive real-time computation, thus improving measurement precision while controlling energy consumption.
Solution Approach 2:
The system implements partial context analysis by selecting only the most relevant dimensions and factors for each specific suggestion scenario, rather than analyzing all possible context data. This partial action approach achieves sufficient measurement precision for practical purposes while significantly reducing computational energy requirements.
3Productivity
If interface components are selected based on revenue scores, then the revenue generation is maximized, but the user interface customization flexibility is reduced
Solution Approach 1:
The system dynamically adjusts interface component selection by considering both revenue scores and user-specific context factors. The interface customization is not static but adapts based on real-time analysis of user preferences, behavior patterns, and situational context. This dynamic approach allows the system to maximize revenue through data-driven decisions while maintaining flexibility to customize interfaces for different user segments and scenarios.
Data Source
AI summary
Methods and systems to learn an optimal user experience. The system receives a request over a network from a user. The request includes context information. The system identifies a response to the request is to be utilized to learn whether a first interface component included in a first plurality of interface components is an optimal choice for a first decision. The response includes an interface. The interface includes the first interface component. The system identifies the response to the request is to be utilized based on the context information. Finally, the system communicates the response over the network to the user.