Visual Recommendation Engine for Physical Store Customer Identification
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Solution Overview
Problem
Existing product recommendation technologies are limited to online purchase actions and struggle to provide effective recommendations in physical stores, especially for first-time customers who lack purchase or viewing records.
Innovation Solution
A method and apparatus that acquire customer images in physical stores, determine purchase tendencies using image features, and recommend items based on a purchase tendency model database, incorporating both online and offline purchase data, with augmented reality displays for personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If product recommendation is based on purchase records, then recommendation accuracy is improved, but applicability to physical stores and first-time customers deteriorates
Solution Approach 1:
The patent introduces image recognition technology as an intermediary to bridge online and offline recommendation systems. By capturing customer images in physical stores and extracting visual features, the system creates a new data pathway that works independently of purchase records, enabling recommendation functionality in physical store environments where traditional methods fail.
Solution Approach 2:
The patent replaces the traditional data-based recommendation mechanism (relying on purchase records) with an image-based visual recognition system. This substitution allows the system to function in physical stores by capturing and analyzing customer appearance, body language, and behavior patterns through cameras and image processing algorithms.
2Ease of operation
If product recommendation relies on purchase records, then personalized recommendations are improved, but service capability for first-time customers deteriorates
Solution Approach 1:
The patent performs preliminary action by capturing customer images and extracting visual features immediately upon customer entry into the physical store, before any purchase behavior occurs. This allows the system to establish a baseline customer profile based on appearance and initial behavior patterns, enabling personalized recommendations from the very beginning of the shopping experience rather than waiting for purchase data to accumulate.
Solution Approach 2:
The system enables self-service by automatically analyzing customer images and generating personalized recommendations without requiring manual data input or existing customer profiles. The image recognition system autonomously extracts relevant features and matches them with product preferences, providing reliable service for first-time customers who cannot provide their own purchase history data.
3Adaptability or versatility
If visual information is used for recommendation, then applicability to physical stores is improved, but system complexity deteriorates
Solution Approach 1:
The patent achieves universality by designing an image-based recommendation system that serves multiple functions: customer identification, preference analysis, and product recommendation. The same visual information processing infrastructure supports both online and offline recommendation scenarios, reducing the need for separate systems and managing complexity through functional consolidation.
Solution Approach 2:
The patent uses image recognition technology as an intermediary layer that simplifies the connection between physical store environments and recommendation algorithms. Rather than directly processing complex purchase data or customer interactions, the system captures visual information and translates it into actionable recommendation insights, reducing overall system complexity through this mediating processing layer.
4Ease of operation
If image analysis is performed to determine purchase tendency, then recommendation personalization is improved, but processing time deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-processing and storing extracted visual features from customer images as they are captured in the physical store. This allows the system to have customer visual profiles ready in advance, so when recommendation requests occur, the system can quickly match pre-extracted features with product databases without performing time-consuming image analysis at the moment of recommendation.
Solution Approach 2:
The patent applies partial action by focusing image analysis on specific, most relevant visual features that strongly correlate with purchase preferences, rather than analyzing all possible image attributes. This selective approach extracts only the essential features needed for accurate recommendation, reducing processing time while maintaining personalization quality.
Data Source
AI summary
An apparatus for recommending a customer item identifies a purchase tendency of a customer based on an image, determines a recommended item for the customer by selecting a purchase tendency model corresponding to the purchase tendency, and provides information associated with the determined recommended item.