Server System for Retail Product Prioritization via Virtual Fitting
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Solution Overview
Problem
In the retail industry, existing technologies fail to effectively differentiate shopping experiences and motivate consumers to purchase products by integrating online and offline interactions, particularly in virtual fitting and sales promotion strategies.
Innovation Solution
A server system that includes units for acquiring, storing, and analyzing recognition and combination information to calculate product priorities based on user interactions, encouraging users to visit stores and try products through virtual simulations, and optimizing sales promotions by analyzing purchase data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtual fitting technology is implemented to differentiate shopping experiences, then user engagement and purchase motivation are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments user interactions into distinct types: recognition information (when users view product images online) and combination information (when users try products in stores). This segmentation allows the complex data to be organized into manageable categories that can be analyzed separately and combined to generate product priorities without overwhelming system complexity
Solution Approach 2:
The system adds a new dimension by integrating online and offline data that were previously separate. By combining recognition information from online channels with combination information from offline store visits, the system creates a multi-dimensional view of user behavior that enables sophisticated product prioritization while managing complexity through structured data dimensions
2Measurement precision
If product priorities are calculated by analyzing multiple data types, then recommendation accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by collecting and storing recognition information and combination information in advance before priority calculation is needed. Data is accumulated in storage units during normal operations, allowing the actual product priority calculation to use pre-organized data rather than processing raw information from scratch, thereby reducing computation time while maintaining accuracy
3Productivity
If user interaction data is collected and analyzed, then sales promotion effectiveness is improved, but data storage requirements and privacy concerns increase
Solution Approach 1:
The system extracts only the essential elements needed for product priority calculation from user interaction data. Rather than storing and processing all raw user data, the system extracts recognition information (product viewing events) and combination information (product try events), filtering out unnecessary data to reduce storage requirements while maintaining the analytical capability for effective sales promotions
Data Source
AI summary
According to an embodiment, a server includes a first acquiring unit, a second acquiring unit, an analyzing unit, and an output unit. The first acquiring unit is configured to acquire recognition information includes a product identification information for identifying the product. The second acquiring unit is configured to acquire combination information including the product identification information of the product to be combined with an object image including an object. The analyzing unit is configured to calculate product priorities for respective products by analyzing the recognition information and the combination information. The output unit is configured to output information based on the product priorities.