LLM-Based Media Content Quality Evaluation and Modification
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Solution Overview
Problem
Business owners face challenges in creating effective media content, such as digital advertisements, due to limited technical expertise and time, leading to sub-optimal captions and images that fail to attract target audiences.
Innovation Solution
A method and system utilizing a Large Language Model (LLM) trained on user profiles, follower profiles, and previous post attributes to evaluate the quality of existing media content and generate optimized modified posts, simplifying the ad creation process and improving content quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If business owners create media content manually without technical expertise, then they can maintain control over content creation, but the quality and effectiveness of the content deteriorates
Solution Approach 1:
The patent introduces an AI-based intermediary system that acts as a mediator between business owners and the complex task of media content creation. The system includes training data processors that analyze follower profiles and previous posts, and generation models that create optimized media content based on this training data, thereby resolving the contradiction by providing high-quality content without requiring user expertise
Solution Approach 2:
The system enables self-service content creation by automatically training models on user-specific data and generating optimized media content without human intervention. The generation model autonomously creates captions, selects images, and optimizes posts based on learned patterns from training data, allowing business owners to maintain control while achieving professional-quality results
2Manufacturing precision
If business owners invest time and resources in creating optimized media content, then content quality improves, but the time and resources required increase
Solution Approach 1:
The system performs preliminary action by pre-training generation models on user-specific training data including follower profiles and previous post attributes before actual content creation. This pre-training phase enables the model to quickly generate optimized content without requiring users to spend time on manual optimization tasks during the actual posting process
Solution Approach 2:
The system changes parameters by automatically adjusting media content parameters such as caption text, image selection, and post attributes based on training data analysis. The generation model learns optimal parameter combinations from historical data and applies these changes automatically, achieving content optimization without user time investment
3Extent of automation
If surface level AI enhancements are used for media content creation, then some automation is achieved, but the optimization remains sub-optimal
Solution Approach 1:
The patent segments the AI system into distinct functional components: a training data processor that analyzes follower profiles and previous posts, and a generation model that creates optimized content. This segmentation allows each component to specialize in specific tasks, with the training processor handling data preparation and the generation model focusing on content creation, thereby achieving both automation and high optimization quality
Solution Approach 2:
The system implements feedback by using previous post attributes and performance data as training data for the generation model. The model learns from historical feedback about what content performed well and applies these lessons to generate optimized new content, creating a continuous improvement loop that enhances both automation capability and optimization quality
Data Source
AI summary
The present disclosure provides systems and methods for optimizing media content. One step of the method may include receiving, via a user interface, an indication of a selected post including media content associated with a user. Another step of the method may include evaluating, via a LLM model trained on training data, a quality of the media content of the selected post. A further step may include generating, via the trained LLM model and based upon the evaluated quality, a modified post including modified media content. A change in the modified post is of a first type when the evaluated quality is at or below a threshold or of a second type when the evaluated quality is above a threshold. Even a further step may include transmitting, via the user interface, the modified post for consideration by the user. Yet even a further step may include receiving, via the user interface, an indication of a rejection or an acceptance of the modified post.