Information processing apparatus, information processing method, and non-transitory storage medium
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image recognition technologies for product registration in checkout processes can only obtain product identification information, failing to acquire attachment information like quality maintenance dates or lot numbers, leading to inefficiencies and potential errors in processing.
Innovation Solution
An information processing apparatus and method that combines image data acquisition, analysis, and a reading necessity check to identify products requiring code reading, allowing for the extraction and reading of specific product codes containing attachment information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image recognition technology is used for product registration, then registration efficiency is improved, but attachment information such as quality maintenance dates and lot numbers cannot be acquired
Solution Approach 1:
The patent combines image recognition technology with barcode/QR code reading functionality into a single integrated system. The imaging unit captures both the product exterior for recognition and the product code for attachment information extraction, merging two separate functions into one unified process that simultaneously achieves efficient product identification and complete information acquisition.
Solution Approach 2:
The imaging unit is designed to perform multiple functions: it serves both as an image recognition device for product identification and as a code reading device for extracting attachment information. This multi-functional approach eliminates the need for separate readers and enables the system to handle both product identification and information acquisition through a single device.
2Loss of information
If product codes are read for all recognized products, then attachment information is acquired, but processing time and system complexity increase
Solution Approach 1:
The system applies different processing approaches to different products based on their characteristics. For products where image recognition provides sufficient information, only image analysis is performed. For products requiring attachment information, code reading is activated. This selective processing approach optimizes the balance between information completeness and processing efficiency.
Solution Approach 2:
Instead of reading codes for all products, the system performs code reading only when necessary - specifically when attachment information is required for certain products. This partial action approach reduces overall processing time while still acquiring the necessary attachment information for products that need it.
3Productivity
If image recognition is used for all products, then registration speed is improved, but recognition accuracy may decrease due to similar product exteriors
Solution Approach 1:
The system uses a feedback mechanism where the image recognition result is verified by code reading when necessary. If there is uncertainty in image recognition (such as for products with similar exteriors), the system activates code reading to verify or correct the recognition result, ensuring high accuracy while maintaining overall registration speed.
Solution Approach 2:
The system performs image recognition first as a preliminary step to quickly identify products. Based on the recognition result and product characteristics, it then determines whether code reading is necessary. This preliminary action approach allows the system to maintain high speed for most products while ensuring accuracy for ambiguous cases.
Data Source
AI summary
A checkout apparatus (10) includes an image data acquisition unit (11) that acquires data of an image; an image analysis unit (12) that recognizes a plurality of products in the image using a feature value of an exterior of each of the products registered in a feature value storage unit (14) and the data of the image; a reading necessity or non-necessity check unit (15) that extracts the product for which it is necessary to read a product code from among the recognized products, using an object-to-be-read storage unit (16) in which the product for which it is necessary to read the product code is registered in advance; and a reading unit (17) that reads the product code of the product extracted by the reading necessity or non-necessity check unit (15).