Product Bundle Generation Using Image Recognition and Recipe Association

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Solution Overview

Problem

Current product bundling methods do not effectively utilize online browsing data and image recognition to offer personalized and discounted bundles of food products based on user-specific and collective shopping habits, limiting the efficiency and appeal of bundled offers.

Innovation Solution

A system that uses image recognition on user devices to identify stocked food products, associates them with recipes, and generates a product bundle for purchase with a bundled price lower than individual prices, while also using historical shopping data from users to create personalized bundles and digital promotions, facilitating purchase through e-commerce platforms and point-of-sale terminals.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If traditional product bundling methods are used, then the system is simple and easy to implement, but the personalization and relevance of bundles to user preferences is limited

Engineering Contradiction:

Improvepersonalization of product bundlesVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system automatically analyzes user browsing data, shopping history, and product images to generate personalized bundles without requiring manual input from users. The machine learning models autonomously process data to create relevant product combinations, reducing the need for user intervention while maintaining high personalization levels

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Traditional manual or rule-based bundling approaches are replaced with machine learning algorithms that automatically detect patterns in user behavior and product characteristics. The system uses neural networks and data processing to generate intelligent bundle recommendations, substituting complex manual processes with automated AI-based mechanisms

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If image recognition is used to identify stocked food products, then the accuracy of product identification is improved, but the processing time and computational resources increase

Engineering Contradiction:

Improveproduct identification accuracyVSAvoidimage processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system pre-processes and stores product image data and characteristics in databases before actual identification is needed. Product information including images, descriptions, and categorizations are prepared in advance, allowing rapid retrieval and matching during the bundle generation process without requiring real-time analysis of all product data

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system extracts and focuses on specific visual features and key characteristics from product images rather than processing the entire image data set. By identifying and isolating important product attributes such as packaging shapes, colors, and label features, the system achieves accurate identification while reducing processing time through selective analysis

Inventive Principle:

Principle #2Taking out (Extraction)

3Adaptability or versatility

If historical shopping data from multiple users is used to generate bundles, then the relevance of bundles to user preferences is improved, but the amount of data processing and machine learning requirements increase

Engineering Contradiction:

Improvebundle relevance to user preferencesVSAvoiddata processing efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system combines and aggregates historical shopping data from multiple users to identify common purchasing patterns and preferences. By merging data sets and analyzing collective behavior patterns, the system generates bundles that reflect broader user preferences while maintaining individualization through targeted filtering and selection algorithms

Inventive Principle:

Principle #5Merging (Combining)

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This solution enables the creation of personalized and cost-effective product bundles that align with user preferences, enhancing the shopping experience by offering relevant recipes and discounts, thereby improving sales and customer satisfaction.

Implementation Method 1

identify each of the plurality of stocked food products using image recognition

Methodology Applied

Scientific EffectImage recognition: Image Processing

Data Source

PatentUS12062063B1System for a product bundle including digital promotion redeemable toward the product bundle and related methods

Publication Date: 2024.08.13 INMAR CLEARING INC

AI summary

A system for a product bundle may include a user device for acquiring an image including stocked food products, and a bundle server configured to obtain the image from the user device. The bundle server may identify each of the stocked food products using image recognition, and associate at least one of the identified stocked food products with a recipe. The recipe may include needed food products. The bundle server may generate a product bundle for purchase that includes the needed food products. The product bundle may have a bundle price less than a sum of individual purchase prices of each of the needed food products. The bundle server may also communicate the product bundle for purchase and the bundle price to the user device, and generate a digital promotion redeemable toward the purchase of the product bundle and communicate the digital promotion to the user device.