Vision-Based Product Recognition for Barcode-Free Checkout

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Solution Overview

Problem

Conventional cashier devices primarily support barcode-based product recognition, leading to inefficiencies and errors in processing non-standard items like fruits and bulk goods, increasing checkout time and costs.

Innovation Solution

A product recognition apparatus utilizing a vision sensor, arithmetic processing unit with an image recognition model, and data transceiver to automatically identify products without barcodes, integrating with a backend server for settlement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If barcode scanning is used for product recognition, then products with barcodes can be processed efficiently, but non-standard products without barcodes require additional devices or manual determination, increasing checkout time and complexity

Engineering Contradiction:

Improvecheckout efficiencyVSAvoidproduct type coverage

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent applies universality by designing a product recognition system that can handle multiple product types through a single unified approach. The vision sensor and image recognition model can process both barcode-containing products and non-standard products (fruits, vegetables, bulk goods) without requiring separate devices or manual intervention, making the system versatile across all product categories.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent replaces the mechanical/manual product recognition process with an automated vision-based system. Instead of manual determination or separate physical scanning devices for different product types, the system uses computer vision technology with machine learning models to automatically identify and categorize all products, eliminating the need for manual intervention and additional hardware.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If manual product category determination is used, then non-standard products can be recognized, but it increases checkout time and is prone to errors

Engineering Contradiction:

Improvenon-standard product recognitionVSAvoidcheckout time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent implements self-service by enabling the system to automatically recognize and categorize products without human intervention. The image recognition model processes product images autonomously, identifying non-standard products and determining their categories independently, eliminating the need for manual determination and significantly reducing checkout time.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the manual mechanical process of product identification with an automated computer vision system. The machine learning model automatically analyzes product images and determines categories, substituting human visual inspection and manual data entry with automated digital processing, thereby eliminating errors and reducing time loss.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If manual product category determination is used, then non-standard products can be recognized, but it increases complexity and is prone to errors

Engineering Contradiction:

Improvenonstandard product recognitionVSAvoidrecognition accuracy

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent replaces manual product recognition with an automated image recognition system based on machine learning. This substitution eliminates human errors in categorization and provides consistent, reliable recognition across all product types. The system maintains high adaptability for non-standard products while significantly improving recognition accuracy through automated digital processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent incorporates feedback mechanisms where the system continuously learns from recognized products and improves its categorization accuracy. The image recognition model can be trained and refined based on actual product data, allowing it to adapt to new product types and improve reliability over time through iterative learning and feedback from real-world usage.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20260030611A1Product recognition apparatus and method

Publication Date: 2026.01.29 HANSHOW TECH CO LTD

AI summary

The present disclosure discloses a product recognition apparatus and method, in which the apparatus includes: a vision sensor configured to obtain and transmit image information of a product to an arithmetic processing unit; the arithmetic processing unit configured to deal the image information of the product based on an image recognition model, to obtain and transmit category information of the product to a data transceiver; and the data transceiver configured to transmit the category information of the product to a backend server of a seller, so that the backend server of the seller makes a settlement based on the category information of the product and preset price information of different categories of products. By adopting the product recognition apparatus, it is possible to quickly recognize the categories of various products, increase efficiency and accuracy of the cashing, and improve the user experiences.