Dynamic Model Selection for Electronic Album Generation
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Solution Overview
Problem
Existing techniques for generating electronic albums often fail to accurately reflect user preferences when the album theme differs from previous orders, leading to unintended layouts and image selections.
Innovation Solution
An electronic album generating apparatus that accepts user order information, specifies the album theme, selects a learned model based on past user data, and generates the album using machine learning, with the option to switch to another model if the initial model is not trained on the specified theme.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a learned model is used to generate an electronic album based on user's past order, then the user preference can be reflected, but the album theme may not be accurately reflected when the theme differs from past orders
Solution Approach 1:
The system dynamically selects different learned models based on the album theme specified in the current order. When the current theme matches a theme in the user's past orders, the corresponding learned model is selected; when it doesn't match, a different learned model is chosen. This dynamic selection mechanism allows the system to adapt to varying themes while maintaining both user preference reflection and theme accuracy.
Solution Approach 2:
The system changes the parameter of model selection based on the album theme. By identifying the theme parameter from the current order and comparing it with themes from past orders, the system selects the appropriate learned model. This parameter-based selection ensures that the generated album accurately reflects both the user's preferences and the specific theme requirements.
2Device complexity
If a single learned model is used for all orders, then the system complexity is reduced, but the adaptability to different themes deteriorates
Solution Approach 1:
The system segments the learned models into multiple theme-specific models based on the user's past orders. Each learned model is associated with a specific theme, allowing the system to handle different themes with specialized models. This segmentation improves theme adaptability while keeping each individual model relatively simple and manageable.
Solution Approach 2:
The system creates a universal framework that can handle multiple themes by maintaining multiple learned models. Each model is specialized for a particular theme, but collectively they provide universal coverage for various album themes. The theme identification and model selection mechanism enables this multi-functional capability.
Data Source
AI summary
An electronic album generating apparatus includes an accepting unit configured to accept order information of an electronic album from a user, a specifying unit configured to specify a theme of the electronic album; a selecting unit configured to select, from a plurality of learned models generated by machine learning based on a past order of the user from whom the order information was accepted, a learned model to be used to generate the electronic album a generating unit configured to generate an electronic album based on the order information and the learned model selected by the selecting unit. In a case in which the learned model generated by machine learning based on the past order of the user has not learned the theme specified by the specifying unit, the selecting unit selects another learned model which has learned the theme.