Computer Vision Image Concept Prediction for User Engagement
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Solution Overview
Problem
Conventional systems fail to effectively understand and identify image concepts that appeal to human interest, leading to inadequate user interaction with digital content, as they rely on textual descriptions and lack robustness in analyzing visual content.
Innovation Solution
An online system using AI and computer vision techniques to analyze user interactions and generate machine learning models that predict image concepts likely to appeal to users, incorporating object detection and correlation with user attributes to recommend and generate visuals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use textual descriptions to represent digital content, then the system complexity remains low, but the system fails to effectively understand and identify image concepts that appeal to human interest
Solution Approach 1:
The patent replaces conventional textual description systems with computer vision-based image analysis systems. Specifically, it uses object detection algorithms, feature extraction techniques, and visual concept recognition to automatically analyze and understand image content, substituting the mechanical textual representation approach with intelligent visual processing capabilities that can accurately identify image concepts appealing to human interest
Solution Approach 2:
The patent transforms the representation parameters of digital content from textual metadata to visual feature vectors and concept tags generated through computer vision analysis. By changing the parameter space from text-based attributes to image-based features (such as object boundaries, color histograms, texture patterns, and semantic concepts), the system achieves superior image concept identification accuracy while managing complexity through automated processing pipelines
2Productivity
If the system uses automated computer vision analysis to identify image concepts, then user interaction and content relevance improve, but processing time and computational resources increase
Solution Approach 1:
The patent implements pre-processing of images to extract and store visual features, concepts, and metadata in advance before user interaction occurs. By performing initial computer vision analysis, object detection, and concept identification during content upload or idle periods, the system prepares structured visual data that can be quickly retrieved and matched against user preferences, significantly reducing real-time processing delays while maintaining high user interaction efficiency
Solution Approach 2:
The system employs automated self-service mechanisms where computer vision algorithms independently analyze images, generate concept tags, and organize visual content without requiring manual intervention or extensive real-time computational resources. The pre-extracted visual features and identified concepts serve the system itself by enabling rapid content matching and recommendation, reducing both processing time and resource consumption during user interactions
Data Source
AI summary
An online system may identify content with which a user has an interest. For example, the online system may determine that a user has an interest in the content based on interaction information indicating that the user interacted with the content. In a particular example, the online system may identify image concepts included in the content based on computer vision techniques that recognize the image concepts. The online system may model probabilities that image concepts will appeal to users. Based on the modeled probabilities, the online system may automatically recommend image concepts for inclusion in candidate images, automatically generate candidate images, or assess candidate images to determine a probability of user interaction with the assessed candidate images.