Dynamic Planning Engine GUI for Personal Goal Projections
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Individuals face challenges in managing complex personal goals, such as financial and health planning, due to the complexity of existing systems, which often overwhelm users with excessive recommendations or require expertise they do not possess, leading to dissatisfaction and disengagement.
Innovation Solution
A computer-implemented method and system that presents a graphical user interface (GUI) for projecting outcomes over time, utilizing a planning engine to retrieve user data, generate predicted outcomes, and update the GUI dynamically, allowing users to visualize and modify their plans effectively, with enhanced service levels providing more sophisticated analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sophisticated planning tools and complex analysis are provided to improve planning quality, then the accuracy and depth of plan analysis is improved, but the user interface complexity increases and ease of operation deteriorates
Solution Approach 1:
The system segments the planning functionality into separate service levels (basic, enhanced, premium) with progressively more sophisticated tools. Each service level presents a simplified interface tailored to user needs, avoiding the complexity of advanced tools for users who don't require them while maintaining access to comprehensive analysis when needed.
Solution Approach 2:
The user interface dynamically adapts based on service level and user interaction history. The system provides customized interfaces that adjust complexity and feature availability according to the user's service tier, allowing sophisticated analysis tools to be revealed only when needed while maintaining simplicity for basic users.
2Adaptability or versatility
If more comprehensive planning recommendations are generated to improve planning thoroughness, then the quality of planning coverage is improved, but the quantity of recommendations overwhelms users and ease of operation deteriorates
Solution Approach 1:
The system applies different recommendation strategies to different service levels. Basic users receive localized, actionable recommendations focused on immediate priorities, while enhanced and premium users access comprehensive multi-factor analysis. This ensures planning thoroughness for those who need it while preventing overwhelm for basic users.
Solution Approach 2:
The system provides partial recommendations (only the most critical actions) for basic service level, while comprehensive recommendations are provided for premium levels. This partial action approach prevents user overwhelm by delivering only necessary information at lower service tiers while maintaining thoroughness where needed.
3Ease of operation
If customized service levels are created to match user needs, then user engagement is improved, but system complexity increases and device complexity worsens
Solution Approach 1:
The system uses a universal planning engine that serves all service levels through a single architecture. The same core planning algorithms and data models power basic, enhanced, and premium services, with differentiation achieved through configuration and interface customization rather than separate systems, reducing overall complexity.
Solution Approach 2:
Different service levels are implemented by changing parameters such as analysis depth, recommendation quantity, and interface features rather than creating fundamentally different systems. This allows customized service levels to be achieved through parameter adjustment, reducing structural complexity while maintaining user engagement.
Data Source
AI summary
A graphical user interface (GUI) includes a visual representation of projections over time for a user. The GUI calls a planning engine through an application programming interface (API) where calling the planning engine causes the planning engine to retrieve user profile data of the user, generate a predicted outcome for the user based on the user profile data, and return the predicted outcome to the GUI. In response to receiving the predicted outcome from the planning engine, the GUI updates updating the visual representation based on the predicted outcome. Certain implementations may include multiple planning engines, each with respective APIs, and/or reconfigurable planning engines.