Neural Network Eyewear Recommendation Server
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Solution Overview
Problem
Current online business systems lack the ability to provide highly personalized and accurate eyewear equipment recommendations, as they rely on morphological rules and consumer data analysis, which may not fully capture individual fashion trends and preferences.
Innovation Solution
A server system utilizing a neural network to analyze images, classify aesthetic components, and match users with personas based on aesthetic component scores, thereby selecting eyewear equipment that aligns with their personal style and current fashion trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If morphological rules and consumer data analysis are used for eyewear recommendations, then the recommendation system is simple to implement, but the personalization accuracy and fashion trend capture are insufficient
Solution Approach 1:
The patent replaces traditional morphological rule-based systems with a neural network-based image analysis system. The neural network automatically extracts aesthetic components and fashion trends from images, substituting manual rule creation with automated machine learning-based pattern recognition, thereby improving personalization accuracy without requiring explicit programming of fashion rules
Solution Approach 2:
The system creates persona profiles that are copied from analyzed images of people wearing eyewear. By extracting aesthetic components from images and creating reusable persona templates, the system can efficiently generate personalized recommendations without analyzing each user from scratch, maintaining simplicity while improving accuracy
2Measurement precision
If neural network analysis is applied to images, then the aesthetic component classification accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-processing images to extract and store aesthetic component features before actual recommendation generation. The neural network analyzes images in advance to create persona profiles with extracted aesthetic components, so that when a user needs recommendations, the system can quickly match against pre-analyzed data rather than performing full image analysis in real-time
Solution Approach 2:
The patent segments the image analysis process into distinct aesthetic component categories (such as frame shape, color, material, and style attributes). By dividing the complex analysis task into separate classification streams, the system can process different aspects of eyewear aesthetics independently and in parallel, reducing overall processing time while maintaining comprehensive analysis accuracy
Data Source
AI summary
A server includes processing circuitry configured to receive one or more images, the one or more images including one or more representations of people. Additionally, the processing circuitry is configured to apply a neural network to the one or more images, wherein the neural network classifies at least one aesthetic component of each image of the one or more images, an aesthetic component score being generated for each image in the one or more images. Further, the processing circuitry is configured to generate a user eyewear equipment profile for a user, the user being matched to a persona from a personae database, each persona in the personae database being linked to one or more persona eyewear equipment profiles, the one or more persona eyewear equipment profiles being based on the aesthetic component score, and select eyewear equipment for the user based on the generated user eyewear equipment profile.