Multimodal Digital Content Generation System Layout
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Solution Overview
Problem
Conventional systems for generating digital content using generative machine learning models are limited to creating single instances or simple compositions and cannot produce multiple types of digital content components arranged in a visually pleasing and cohesive layout.
Innovation Solution
A generation system that receives user input specifying characteristics, generates vector representations for candidate layouts and strategies using locality-sensitive hashing, and processes these with first and second machine learning models to create digital content components in a specific order, formatted in JavaScript Object Notation, enabling the generation of multimodal digital content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional generative machine learning models are used, then single instances or simple compositions can be generated, but multiple types of digital content components arranged in a visually pleasing and cohesive layout cannot be produced
Solution Approach 1:
The system segments the digital content generation process into distinct components: a first machine learning model generates text descriptions of individual content components, while a second machine learning model arranges these components into a cohesive layout. This segmentation enables the generation of multiple types of digital content components with visually pleasing arrangements, overcoming the limitations of conventional single-model approaches.
2Manufacturing precision
If a generation system processes output text through multiple machine learning models, then digital content components can be generated in a specific order with visually appealing layouts, but processing time and computational resources increase
Solution Approach 1:
The first machine learning model performs preliminary action by generating text descriptions of digital content components in advance. These pre-generated component descriptions are then processed by the second machine learning model to create the final arranged layout. This preliminary generation of content components enables efficient processing and reduces overall generation time while maintaining high layout precision.
Data Source
AI summary
In implementations of systems for generating digital content, a computing device implements a generation system to receive a user input specifying a characteristic for digital content. The generation system generates input text based on the characteristic for processing by a first machine learning model. Output text generated by the first machine learning model based on processing the input text is received. The output text describes a digital content component. The generation system generates the digital content component by processing the output text using a second machine learning model. The generation system generates the digital content including the digital content component for display in a user interface based on the characteristic.