Feature Metadata Video Ad Correlation System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Consumers face challenges in identifying products with specific features of interest while watching product video advertisements, especially when similar products are available.
Innovation Solution
A computer-implemented method that associates feature metadata with product video advertisements, detects triggering events during playback, and displays related product video advertisements featuring similar or related product features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If consumers watch product video advertisements to identify products with specific features, then they can learn about product features, but it becomes difficult to quickly find and review other products with similar features when multiple similar products are available
Solution Approach 1:
The system performs preliminary action by extracting and indexing feature metadata from product video advertisements before playback. This metadata is stored and ready to be instantly retrieved when a triggering event occurs, eliminating the need for consumers to manually search for similar products during viewing.
Solution Approach 2:
The system implements feedback by detecting user interactions (triggering events) during video playback and dynamically responding by displaying related product advertisements. This creates a closed-loop system where user behavior directly influences the information presented, providing personalized recommendations based on real-time viewing context.
2Adaptability or versatility
If the system displays multiple related product video advertisements during playback, then consumers can review similar products, but the complexity of the advertising system increases
Solution Approach 1:
The system segments the video advertisement content into distinct feature sections by extracting and indexing specific feature metadata (e.g., camera quality, battery life, display size). This segmentation allows the system to handle complex product information systematically by processing and storing individual feature attributes separately, making the overall system more manageable despite the versatility it enables.
Solution Approach 2:
The system introduces an intermediary layer of feature metadata that bridges the gap between raw video content and product recommendations. This metadata acts as a mediator that translates video content into structured information, enabling the system to generate relevant advertisements without requiring direct complex analysis of the video content itself during runtime.
Data Source
AI summary
A computer-implemented method for displaying advertisements. The method includes associating feature metadata with a product video advertisement of a product. The method includes identifying, based on the feature metadata, a feature of the product corresponding with a section of the product video advertisement when a triggering event is detected during playing of the product video advertisement. The method includes displaying a second product video advertisement of a second product that includes the feature of the product.