Retail Recommendation System Using Customer Tags

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Solution Overview

Problem

Conventional product recommendation systems in retail environments are complex, costly, and difficult to maintain, relying on vendor-provided information and requiring high-performance computing, with recommendations often not accurately reflecting customer preferences due to fixed and inflexible linking of customer profiles to products.

Innovation Solution

A personalized product recommendation system that uses customer-provided or autonomously determined tags to categorize products based on customer preferences, learned over time through purchase data, allowing for accurate and personalized recommendations without the need for third-party maintenance, utilizing existing cameras for image analysis and mobile devices to provide location-based product suggestions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If conventional recommendation systems use vendor-provided information and customer profile interoperability, then recommendation capability is provided, but system complexity and maintenance cost increase significantly

Engineering Contradiction:

Improverecommendation capabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent extracts and eliminates the dependency on vendor-provided information and customer profile interoperability from the recommendation system. By using only data from the retailer's own database without requiring external vendor systems or customer profile linkages, the system provides recommendation capability while avoiding the complexity and maintenance burden of multi-system integration

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent creates a universal recommendation system that works with any retailer's existing database without requiring specialized vendor systems or customer profile integrations. The system can provide recommendations using solely the retailer's product and sales data, making it adaptable to different retailers without increasing system complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Power

If conventional recommendation systems require high-performance computing environment, then processing power is sufficient, but system cost and maintenance difficulty increase

Engineering Contradiction:

Improveprocessing powerVSAvoidsystem maintenance difficulty

Core Design Contradiction:

PowerVSDevice complexity

Solution Approach 1:

The patent replaces expensive, high-performance computing infrastructure with simpler, more affordable computing resources. By using basic computing capabilities that can be found in ordinary servers or even mobile devices, the system achieves sufficient processing power without requiring costly hardware investments or specialized maintenance resources

Inventive Principle:

Principle #27Cheap short-living objects (Disposable)

3Device complexity

If conventional systems use fixed and inflexible customer-product linking, then system structure is simple, but recommendation accuracy decreases

Engineering Contradiction:

Improvesystem structure simplicityVSAvoidrecommendation accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent implements dynamic, flexible customer-product linking that adapts to individual customer preferences and behaviors. Instead of fixed predefined links, the system dynamically determines relationships based on actual purchase patterns and product attributes, enabling accurate recommendations while maintaining simple system structure through flexible data association

Inventive Principle:

Principle #15Dynamics

4Loss of information

If conventional recommendation systems rely on multiple interconnected systems, then comprehensive data is available, but maintenance cost and complexity increase

Engineering Contradiction:

Improvedata comprehensivenessVSAvoidmaintenance cost

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent merges the recommendation system functionality entirely within the retailer's existing database and mobile application infrastructure. By combining product information, sales data, and recommendation logic into a single integrated system using existing resources, the system maintains comprehensive data availability while eliminating the need to maintain separate vendor systems or customer profile databases

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS11263686B2Method, medium, and system for generating control messages for recommendations

Publication Date: 2022.03.01 TOSHIBA GLOBAL COMMERCE SOLUTIONS HLDG

AI summary

A network node associated with a retail store maintains personalized purchasing information identifying one or more products purchased by a customer. Each product is associated with one or more preference tags identifying a respective product category for the product, and indicating the customer's preference for products in that product category. Based on this information, the network node recommends products for the customer to purchase, and indicates those recommendations by controlling the customer's wireless device to visually indicate the location of the recommended products in the store.