Multimodal Content Modification Using Context and Prompt Guidance
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Solution Overview
Problem
The process of manually selecting and modifying social media content is time-consuming and involves complex user interfaces, making it inefficient for content distribution.
Innovation Solution
A computer-implemented method using machine-learned models to generate modified content data based on content features, context, and prompts, enabling quick and efficient generation of content recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual selection and modification of social media content is performed, then content can be customized and distributed, but the process is time-consuming and involves complex user interfaces
Solution Approach 1:
The system performs content modification automatically without requiring user intervention. The machine-learned model analyzes content features, context, and prompts to generate modified content data autonomously, eliminating the need for users to manually select and modify content through complex interfaces
Solution Approach 2:
The patent replaces manual mechanical operations with automated machine learning processes. Instead of users manually selecting and modifying content through interfaces, the system uses machine-learned models to automatically process content features, generate modifications, and create content recommendations
2Productivity
If machine-learned models are used to generate modified content, then content distribution speed increases, but the system complexity increases
Solution Approach 1:
The machine-learned model serves multiple functions within the system: it analyzes content features, processes context information, generates modifications based on prompts, and creates content recommendations. This multi-functionality consolidates what would otherwise require multiple separate systems into a single unified model
Solution Approach 2:
The machine-learned model acts as an intermediary between raw content data and the final content recommendations. It processes input content features, context, and prompts to generate modified content data, serving as a computational bridge that transforms raw data into useful recommendations without requiring direct complex interactions
Data Source
AI summary
Methods, systems, devices, and non-transitory computer readable media for generating or modifying features of content are provided. The disclosed technology can include receiving content data comprising content associated with one or more data multimodalities. Prompt data associated with modification of the content can be received. Contexts associated with the content data can be determined. Based on inputting the content data, the prompts, and context data based on the contexts into one or more machine-learned models, modified content data based on the content data and comprising one or more modifications of the one or more features of the content data can be generated. The one or more machine-learned models can be configured to modify the one or more features of the content data based on the one or more prompts and the context data. Furthermore, one or more content recommendations based on the modified content data can be generated.