Adaptive Bill Layout Based on User Learning Style
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Solution Overview
Problem
Conventional Electronic Bill Presentment and Payment (EBPP) systems fail to provide a personalized viewing experience for customers, as they typically offer only a limited format for bill/statement viewing, which can be frustrating for users with visual or learning limitations, and do not adapt to individual preferences, requiring repeated manual formatting changes and consuming network resources.
Innovation Solution
A system and method that determines an optimal viewing layout for electronic content items on a computing device based on user preference data, allowing users to customize the layout and format of bills/statement, which is then stored and applied consistently across future views, reducing network congestion and saving time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single standardized format is used for bill presentment, then system simplicity and ease of implementation are improved, but user adaptability and personalization capability deteriorate
Solution Approach 1:
The system dynamically adapts the bill presentation format based on user preferences and device characteristics. Templates are selected and customized in real-time according to user profile data, allowing the same bill content to be presented in different formats (e.g., condensed for mobile, detailed for desktop) without requiring multiple static versions of each bill.
Solution Approach 2:
The system changes presentation parameters such as layout density, font size, information hierarchy, and visual emphasis based on user preferences stored in the database. This allows the core bill data to remain standardized while the presentation parameters are customized for each user, resolving the contradiction between standardized data and personalized display.
2Adaptability or versatility
If multiple personalized templates are provided for bill viewing, then user adaptability and personalization are improved, but system complexity and data management burden increase
Solution Approach 1:
The system uses a universal template structure that can serve multiple purposes. A single template framework handles different bill types, device types, and user preferences through parameter customization rather than requiring separate templates for each scenario. This reduces system complexity while maintaining high adaptability.
Solution Approach 2:
The system creates lightweight copies of template configurations in the user preference database rather than storing multiple full bill presentations. Each user has a profile that references and customizes the base template, reducing data management burden while enabling personalization.
3Ease of operation
If manual formatting changes are required for each bill view, then user control over presentation is improved, but time efficiency and user convenience deteriorate
Solution Approach 1:
The system performs preliminary configuration by storing user formatting preferences in advance in the preference database. When a bill is presented, the system automatically applies the pre-configured formatting parameters from the user profile, eliminating the need for users to manually adjust settings each time they view a bill.
Solution Approach 2:
The system provides feedback mechanisms that allow users to adjust their preferences and have these preferences automatically applied to future bill presentations. This creates a closed-loop system where user control is maintained through preference updates, but the time-consuming manual formatting process is eliminated for routine views.
4Measurement precision
If repeated manual formatting adjustments are necessary, then user control precision is improved, but network resource consumption and system efficiency worsen
Solution Approach 1:
The system serves itself by automatically selecting and applying the appropriate template and formatting parameters based on user profile data stored in the database. This eliminates the need for repeated manual formatting requests and transmissions, reducing network resource consumption while maintaining precise formatting control through automated preference-based selection.
Data Source
AI summary
Examples described herein relate to apparatuses and methods of determining an optimal viewing layout of a content item on a computing device based on user preference data. The method includes receiving a request for a content item associated with an account of a user. The method includes selecting a template having a set of elements for generating a content item. The method includes selecting a content item dataset associated with the content item. The method includes generating layout data based on the content item dataset and the selected template. The method includes sending the layout data, causing operations comprising assembling the content item for display in an application based on the layout data, and gathering user preference data in response to an interaction of the user with the displayed content item. The method includes updating the template based on the user preference data.