Image-Based Ad Targeting via Feature Vector Similarity
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Solution Overview
Problem
Current advertising technologies lack effective methods for targeting advertisements based on images, leading to inefficient ad placement and reduced user engagement.
Innovation Solution
An image-based ad targeting system that uses object recognition techniques to match images with reference images, allowing for automated bid selection and ad placement based on similarity, enabling users to browse and bid on images associated with ads, and storing bids with images for precise ad presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated bid selection based on image similarity is implemented, then ad targeting accuracy is improved, but system complexity increases
Solution Approach 1:
The system pre-processes images by generating feature vectors and storing them in a database before bidding occurs. This preliminary action enables automated bid selection based on image similarity without requiring complex real-time processing during the bidding process, thus improving targeting accuracy while managing system complexity
Solution Approach 2:
The patent introduces an intermediary mechanism (feature vector representation and similarity computation module) that bridges the gap between image content and bid selection. This intermediary transforms complex image data into comparable numerical representations, enabling automated decision-making without directly comparing entire images, thereby improving accuracy while controlling complexity
2Productivity
If image-based ad targeting is implemented, then user engagement is improved, but processing time increases
Solution Approach 1:
The system performs image feature extraction and vector generation in advance, storing these processed representations in a database. When ad targeting is needed, the system only needs to compute similarity between pre-generated vectors rather than processing raw images, significantly reducing processing time while maintaining engagement quality
Solution Approach 2:
The patent extracts essential features from images (converting them to feature vectors) and separates these extracted features from the original image data. This extraction allows the system to work with compact numerical representations instead of large image files, reducing processing time while preserving the information needed for accurate targeting
Data Source
AI summary
Techniques are described for facilitating bidding on images. The techniques may include receiving bids includes presenting a first image and a bid associated with the first image. The first image is similar to a second image for which a bid is to be submitted for presenting sponsored content based on the second image. A bid is received for presenting a sponsored-content item based on the second image. The received bid is stored in association with second image and a sponsored-content item based to be presented based on the bid and the second image.