Continuous Machine Learning for Visual Content Descriptor Extraction
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Solution Overview
Problem
Content creators often fail to provide effective tags or descriptors for visual content, making it difficult for computer systems to target specific audiences, leading to poor awareness or ineffective advertising campaigns.
Innovation Solution
A continuous machine learning system that extracts descriptors from visual content using computer vision and neural networks, refining its understanding based on user interactions to improve content targeting and adapt to changing trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content creators manually provide tags or descriptors for visual content, then the system can identify target audiences, but the quality and effectiveness of tags are inconsistent and often poor
Solution Approach 1:
The system automatically extracts descriptors from visual content using computer vision and machine learning models, eliminating the need for manual tagging by content creators. The algorithm autonomously analyzes images and videos to generate accurate descriptors without human intervention, resolving the contradiction between descriptor accuracy and ease of operation.
Solution Approach 2:
The patent replaces the manual mechanical process of tagging content with an automated computer vision system. Machine learning models process visual content to extract descriptors, substituting human creative effort with algorithmic analysis, thereby improving both accuracy and operational efficiency.
2Adaptability or versatility
If the system uses static descriptors for content targeting, then the implementation is simple, but the system cannot adapt to changing user preferences and trends
Solution Approach 1:
The system implements continuous feedback loops where user interactions with content are tracked and used to retrain machine learning models. This feedback mechanism allows the system to adapt descriptors dynamically based on actual user behavior, improving trend adaptation while managing complexity through automated learning processes.
Solution Approach 2:
The patent transforms static descriptors into dynamic, evolving characteristics. Machine learning models continuously update descriptor representations based on new data and user interactions, enabling the system to adapt to changing preferences without requiring manual reconfiguration, thus balancing adaptability with manageable system complexity.
3Measurement precision
If the system extracts detailed descriptors from visual content, then content targeting accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary extraction of key descriptors from visual content during content upload or preprocessing stages. By identifying and extracting the most important characteristics in advance, the system reduces the computational burden during real-time content delivery while maintaining high targeting accuracy, thus resolving the time-accuracy tradeoff.
Data Source
AI summary
Aspects of the present disclosure relate to machine learning techniques for continuous implementation and training of a machine learning system for identifying the natural language meaning of visual content. A computer vision model or other suitable machine learning model can predict whether a given descriptor is associated with the visual content. A set of such models can be used to determine whether particular ones of a set of descriptors are associated with the visual content, with the determined descriptors representing a meaning of the visual content. This meaning can be refined based on a multi-armed bandit tracking and analyzing interactions between the visual content and users associated with certain personas related to the determined descriptors.