Image Segmentation and Vector Matching for Trend-Aware Styling
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Solution Overview
Problem
Existing product recommendation systems in online shopping rely heavily on transaction data, which is often insufficient and outdated, especially for rapidly changing trends in fashion and home décor, leading to inaccurate and irrelevant recommendations.
Innovation Solution
An intelligent styling system that segments images from public sources like social media and merchant websites into predefined categories, generates numerical vector representations, and identifies similar images to create a recommendation catalog for complementary products.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If product recommendation systems rely on transaction data to learn co-occurrence patterns, then recommendations can be generated, but the recommendations become insufficient and outdated when transaction data is limited or trends change quickly
Solution Approach 1:
The patent introduces visual data from social media and merchant websites as an intermediary source alongside traditional transaction data. This visual data serves as a mediator that captures current fashion trends and styling patterns, allowing the system to generate accurate recommendations even when transaction data is insufficient or outdated. The visual data bridge connects product images with styling contexts, enabling timely recommendations that reflect current trends rather than relying solely on historical purchase patterns.
Solution Approach 2:
The patent transitions from relying solely on transactional data (one-dimensional purchase records) to incorporating visual data dimensions (images, styles, aesthetics). By adding the visual dimension, the system can analyze fashion trends, color palettes, and styling combinations directly from images, enabling it to capture current trends in real-time without being constrained by historical transaction patterns. This dimensional expansion allows the system to recommend products based on visual appeal and current fashion contexts rather than only past purchase behavior.
2Adaptability or versatility
If the system uses visual data from public sources to identify current style trends, then recommendations become timely and relevant, but the system complexity increases
Solution Approach 1:
The patent segments the visual data processing into distinct functional modules: image collection from social media and merchant websites, image segmentation into predefined categories (e.g., tops, bottoms, accessories), vector representation generation, and similarity matching. This segmentation allows each module to handle specific tasks independently, making the overall complex system more manageable and maintainable. The segmentation also enables the system to process visual data systematically rather than monolithically, reducing computational complexity while maintaining trend-awareness.
Solution Approach 2:
The patent creates a recommendation catalog that copies and stores visual data representations (vectors and images) from public sources. Instead of processing every new image in real-time, the system pre-processes and stores visual data from social media and merchant websites into a structured catalog. When generating recommendations, the system queries this pre-built catalog rather than analyzing raw images from scratch, significantly reducing computational complexity while maintaining the ability to reflect current trends.
3Productivity
If the system segments images into predefined categories and generates vector representations, then similar images can be identified efficiently, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary processing of visual data by segmenting images into predefined categories and generating vector representations in advance. This pre-processing creates a ready-to-query recommendation catalog that stores processed visual data. When generating recommendations, the system only needs to query the pre-processed catalog rather than processing raw images in real-time, significantly reducing computational energy consumption and processing time while maintaining high productivity.
Solution Approach 2:
The patent transforms visual data into numerical vector representations, changing the parameter space from raw pixel data to compressed vector space. This parameter transformation reduces the dimensionality and complexity of visual data, making similarity matching more efficient. By working with vectors rather than raw images, the system can quickly identify similar products and generate recommendations with lower computational energy consumption while maintaining high processing speed.
Data Source
AI summary
Systems and methods are provided for segmenting each image of a first plurality of images, using an image processing segmentation technique, into one or more category of a plurality of predefined categories to generate a set of image segments for the image. A numerical vector representation is generated for each image segment and for each image in a second plurality of images and used to determine a similarity between image segments and images in the second plurality of images. Each image segment in each set of image segment are replaced with an image in the second plurality of images that is similar to the image segment to generate a recommendation catalog including a plurality of sets of recommendation images.