Shoppable Video Generation via Deep Learning Product Detection
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Solution Overview
Problem
The manual creation of shoppable videos is time-consuming and prone to errors due to the vast number of products that need to be compared in video content, making the process impractical and inaccurate.
Innovation Solution
The automatic generation of shoppable videos by breaking down videos into frames and tiles, using deep convolutional neural networks to compute feature vectors for product images and video frames, and comparing them to identify products and associate product information, thereby reducing human error and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual creation of shoppable videos is used, then product information can be associated with video scenes, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical processes with an automated computer vision system. A deep learning-based model automatically detects products in video frames, extracts feature vectors, and matches them against product databases without human intervention, thereby eliminating time-consuming manual labor while maintaining high accuracy through algorithmic processing
Solution Approach 2:
The system performs self-service by automatically generating shoppable videos through autonomous product detection and association. The computer vision system independently identifies products, extracts visual features, retrieves matching products from databases, and associates product information with video scenes without requiring human operators to manually annotate or verify each product
2Measurement precision
If manual comparison of products with video content is performed, then product identification can be achieved, but the process becomes impractical due to vast quantity of products
Solution Approach 1:
The patent segments the video content into individual frames and further divides each frame into multiple patches or regions. This segmentation allows the system to process large numbers of products efficiently by comparing only relevant visual features within each segment against product databases, dramatically increasing processing speed while maintaining detection accuracy through localized analysis
Solution Approach 2:
The system changes parameters by extracting and comparing feature vectors (such as color histograms, texture features, shape descriptors) instead of manually comparing products. This parameter transformation enables rapid automated comparison of vast product quantities with video content, achieving both high productivity and precise product identification through computational matching
3Reliability
If manual annotation of products in video is performed, then product information can be associated with scenes, but human error leads to inaccuracies
Solution Approach 1:
The patent implements feedback mechanisms where the system automatically generates product associations and can be verified or adjusted through user interfaces. The deep learning model provides consistent automated detection that reduces human error, while feedback loops allow for correction of minor inaccuracies, ensuring high reliability without requiring extensive manual verification time
Data Source
AI summary
Embodiments of the present invention provide systems and methods for automatically generating a shoppable video. A video is parsed into one or more scenes. Products and their corresponding product information are automatically associated with the one or more scenes. The shoppable video is then generated using the associated products and corresponding product information such that the products are visible in the shoppable video based on a scene in which the products are found.