AI In-App Asset Variation Generation
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Solution Overview
Problem
Creating multiple variations of in-app assets, such as audio and graphical content, for enhanced realism in video games and virtual environments is labor-intensive and time-consuming, requiring significant creative work to achieve diversity and realism.
Innovation Solution
An AI-driven system that automatically generates variations of multi-layer in-app assets based on contextual specifications by training an AI model with reference assets and metadata, allowing for the adjustment of neural node weightings to produce diverse and realistic content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual creation methods are used to generate variations of in-app assets, then creative quality and diversity can be achieved, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The AI model enables self-service by automatically generating asset variations without requiring manual creative intervention. The system trains on existing assets and contextual metadata, then autonomously produces new variations that maintain creative quality while eliminating labor-intensive manual processes
Solution Approach 2:
The patent replaces the mechanical system of manual asset creation with an AI-based automated system. The neural network model substitutes human creative labor with algorithmic generation, maintaining output quality while dramatically improving production efficiency and speed
2Adaptability or versatility
If multiple variations of in-app assets are created manually, then diversity and realism are enhanced, but the time and effort required increases significantly
Solution Approach 1:
The AI model generates diverse asset variations by manipulating parameters such as contextual features, metadata attributes, and neural network weights. This allows rapid production of multiple diverse versions of in-app assets without manual intervention, maintaining versatility while minimizing time investment
Solution Approach 2:
The system performs preliminary action by pre-training the AI model on extensive datasets of in-app assets and their contextual metadata. This preparatory training enables the model to quickly generate diverse variations during actual use, reducing creation time while maintaining high adaptability and realism
3Productivity
If AI models are used to generate asset variations, then productivity and speed are improved, but the complexity of the system increases
Solution Approach 1:
The AI model serves multiple functions: it generates variations of different asset types (images, audio, video), processes various contextual metadata, and adapts to different application contexts. This multi-functionality consolidates complex operations into a single versatile system, improving productivity without proportionally increasing overall system complexity
Data Source
AI summary
A description of a reference version of an in-app asset is provided to an artificial intelligence model. A contextual communication is provided to the artificial intelligence model, wherein the contextual communication specifies a contextual feature for generation of variations of the reference version of the in-app asset. The artificial intelligence model is executed to automatically generate a variation of the in-app asset based on the contextual feature specified by the contextual communication, where the variation of the in-app asset is defined relative to the reference version of the in-app asset. The automatically generated variation of the in-app asset is conveyed for human assessment. In some embodiments, the automatically generated variation of the in-app asset is subjected to an automatic culling process before being conveyed for human assessment. In some embodiments, the in-app asset is defined by multiple layers. The in-app asset is either an audio asset or a graphical asset.