AI Tagging System for User Content Discovery
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Solution Overview
Problem
Current methods for leveraging user-generated content (UGC) in online marketing are inefficient in processing and selecting valuable content from vast quantities, failing to effectively utilize social media content for product promotion and brand management.
Innovation Solution
A system and method utilizing an artificial intelligence model to identify and associate tags with user content, select relevant content based on probability, and iteratively train the model to discover tagged user content featuring products, incorporating neural network or other models for efficient content management and display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processing and selection of user-generated content is used, then content quality may be maintained, but processing efficiency and productivity are severely limited
Solution Approach 1:
The patent replaces manual mechanical processing of UGC with an automated system combining computer vision algorithms, natural language processing, and machine learning models. This substitution enables high-volume content processing while maintaining quality standards through automated relevance scoring, object detection, and sentiment analysis.
Solution Approach 2:
The patent introduces an intermediary automated processing layer between UGC collection and marketing utilization. This intermediary system uses AI models to filter, tag, and score content, bridging the gap between raw user content and marketing-ready materials without requiring direct manual intervention at each stage.
2Productivity
If automated processing systems are implemented to handle vast quantities of UGC, then processing speed and productivity improve, but measurement precision and content quality control may deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where processed UGC is continuously evaluated against marketing performance metrics. The system uses reinforcement learning to adjust processing parameters based on which content types and styles drive engagement, thereby improving measurement precision while maintaining high processing volumes through iterative optimization.
Solution Approach 2:
The patent dynamically adjusts processing parameters such as relevance thresholds, tagging sensitivity, and filtering criteria based on content volume and marketing objectives. This allows the system to maintain high productivity while adapting measurement precision to different contexts, ensuring quality control even at scale.
3Adaptability or versatility
If comprehensive tagging and analysis of all UGC is performed, then content discovery capability improves, but processing time and resource consumption increase
Solution Approach 1:
The patent segments UGC processing into multiple stages: initial filtering by basic criteria, intermediate tagging by AI models, and final selection based on marketing relevance. This segmentation enables comprehensive analysis of content characteristics while reducing overall processing time by eliminating redundant operations and focusing detailed analysis only on promising content.
Solution Approach 2:
The patent applies partial processing to the entire UGC corpus by performing basic filtering and metadata extraction on all content, then applying more intensive tagging and analysis only to a subset of content that passes initial thresholds. This approach achieves adequate content discovery capability without the time cost of exhaustive analysis of every piece of UGC.
Data Source
AI summary
A method, system, and computer program product for discovering from user content, at least one tagged item that includes a product, includes identifying plural tags to be associated with each of the user-content item, and the corresponding probability that each of the plural tags is associated with products. There is also the feature of associating the plural tags and their corresponding probability of being associated with products. There are also the features of generating at least one subset of the tagged user content based upon the probability of a first one of the plural tags being associated with a product, and discovering the tagged user content comprising the product, from the subset of the tagged user content based upon the probability of the first one of the plural tags being associated with a product.