Dynamic Goal Image Generation for User Engagement
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Solution Overview
Problem
Existing technologies lack an effective method to visualize and enhance user engagement with goals, leading to inefficient goal pursuit and achievement.
Innovation Solution
A platform utilizing generative artificial intelligence (GenAI) models to create custom images of user-defined goals, displayed on a user interface, which can be dynamically filled in based on progress towards the goal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional goal tracking methods are used, then users can monitor their progress, but user engagement and motivation remain low
Solution Approach 1:
The system uses dynamic visual transformations where goal images evolve from blank or skeletal states to fully realized representations as users make progress. This visual metamorphosis provides immediate, intuitive feedback that engages users emotionally and motivates continued effort toward goal achievement.
Solution Approach 2:
The system pre-generates complete goal images based on user inputs (descriptions, attributes, preferences) and then progressively reveals portions of these pre-created images as users achieve milestones. This approach allows for high engagement through visual feedback without requiring complex real-time image generation during user interaction.
2Ease of operation
If custom goal images are generated for each user, then user motivation increases, but computational resources and processing time increase
Solution Approach 1:
The system performs image generation in advance by creating complete goal representations based on user inputs during setup. These pre-generated images are then used throughout the goal-tracking process, eliminating the need for time-consuming real-time generation and allowing rapid updates as users progress.
Solution Approach 2:
The system divides the goal image into multiple segments or regions that can be independently revealed or hidden based on user progress. This segmentation allows the system to efficiently update only specific portions of the image rather than regenerating entire images, reducing processing time while maintaining motivational impact.
3Measurement precision
If goal progress is tracked in detail, then accuracy of progress measurement improves, but user interface complexity increases
Solution Approach 1:
The system translates detailed progress data into simple visual changes in the goal image, such as filling in previously blank areas, changing colors, or revealing hidden elements. This visual encoding conveys precise progress information intuitively without requiring complex charts, graphs, or numerical displays.
Solution Approach 2:
The system uses the goal image itself as the primary progress indicator, where the image serves both as a motivational representation and as a visual measurement tool. This eliminates the need for separate tracking interfaces, as the image's state directly reflects progress accuracy in an easily interpretable format.
Data Source
AI summary
An example operation may include one or more of establishing a network connection between a computing system and one or more external sources over a computer network, receiving a request from a user via a software application on a user device, collecting data about the user from the one or more external sources via the established network connection, executing a machine learning model on the collected data about the user to determine a goal of the user, and displaying an image of the goal via a user interface of the software application.