GAN Design Adaptation for What-If Scenario Driven Image Changes
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Solution Overview
Problem
Existing user interface and product design systems lack the ability to dynamically adapt to multiple what-if scenarios and user preferences, requiring manual iteration and inefficient feedback loops.
Innovation Solution
A computer-implemented method using a generative adversarial network (GAN) to generate design adaptations based on user-selected what-if scenarios, leveraging AI knowledge corpus for influencing factors and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual iteration and feedback loops are used for design adaptation, then design quality can be improved, but time consumption and efficiency deteriorate
Solution Approach 1:
The patent replaces manual design iteration (mechanical human process) with an automated system comprising scenario analysis modules, influence factor identification modules, and generative adversarial network modules. This substitution maintains design quality through systematic analysis while dramatically reducing time consumption by eliminating manual feedback loops.
Solution Approach 2:
The system enables self-service design adaptation by automatically analyzing what-if scenarios, identifying influence factors, and generating adapted designs without requiring manual intervention. The generative adversarial network autonomously iterates to produce high-quality design adaptations, resolving the contradiction between quality and time consumption.
2Adaptability or versatility
If design systems accommodate multiple what-if scenarios and user preferences, then adaptability improves, but system complexity increases
Solution Approach 1:
The patent segments the design adaptation system into distinct functional modules: scenario analysis modules for processing what-if scenarios, influence factor identification modules for extracting relevant factors, and generative adversarial network modules for generating adaptations. This segmentation manages complexity by organizing functions into independent, manageable components while maintaining high adaptability.
Solution Approach 2:
The system achieves universal adaptability by designing modules that can handle multiple what-if scenarios and various user preferences through a unified framework. The generative adversarial network serves multiple functions by adapting designs across different scenarios and preferences, reducing overall system complexity through multi-functionality.
3Productivity
If automated GAN-based design adaptation is implemented, then productivity improves, but measurement and control difficulty increases
Solution Approach 1:
The patent implements feedback mechanisms where the system analyzes user preferences and scenario outcomes, identifies influence factors, and uses this feedback to iteratively improve design adaptations through the generative adversarial network. This feedback loop maintains control over the automated process while maximizing productivity by continuously learning from results.
Data Source
AI summary
A computer-implemented method, a computer program product, and a computer system for design adaptation. One or more computing devices receive images provided by a user, receive form a designer one or more designer provided what-if scenarios of a design related to the images, receive form the user one or more user selected what-if scenarios from the one or more designer provided what-if scenarios, and retrieve information about influencing factors form an artificial intelligence (AI) knowledge corpus, where the influencing factors affect qualities of the images. One or more computing devices input the retrieved information into a generative adversarial network (GAN) module. One or more computing devices change the images by the GAN module to generate adapted images for the one or more user selected what-if scenarios, based on the retrieved information.