Commodity Search Device Using Average Unit Weight and Optical Data
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Solution Overview
Problem
In individual sales of commodities like fruits and vegetables, existing technologies face challenges in accurately specifying items for pricing and accounting due to variations in quantity and weight, leading to increased risks of accounting mistakes, especially when multiple types of commodities are involved.
Innovation Solution
A commodity search device that includes a storage unit for reference unit weights, a measuring unit, a number input unit, and a unit average weighing value calculator, which calculates a unit average weighing value by dividing the total weight by the input number, and searches for commodities with similar reference unit weights, combined with an imaging unit for optical characteristic data to enhance accuracy and speed the search process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If commodity specification is performed using only optical characteristic data comparison, then the system can identify commodities visually, but search accuracy is degraded when purchase quantity changes or commodities are received in packaging
Solution Approach 1:
The patent combines optical characteristic data from image recognition with weight data from the scale into a unified commodity identification system. The commodity specification is determined by integrating both visual appearance and weight information, allowing the system to accurately identify commodities even when packaging or quantity varies. This merging of multiple data sources resolves the contradiction between maintaining identification accuracy and adapting to quantity/packaging changes.
2Measurement precision
If commodity specification is performed using only weight comparison with fixed reference values, then the system can identify commodities by weight, but the approach becomes useless when the number or weight of commodities varies in individual sale
Solution Approach 1:
The patent implements a dynamic reference weight system that adapts to individual sale conditions. Instead of using fixed reference weights, the system dynamically determines appropriate reference weights based on the number of commodities and sale type. The weight management unit can set different reference weights for single-item sales versus multi-item sales, and adjust for different packaging conditions. This dynamic adaptation resolves the contradiction between weight-based identification accuracy and versatility in variable quantity sales.
3Reliability
If the system processes all commodities in the database for identification, then comprehensive search coverage is achieved, but search time increases significantly
Solution Approach 1:
The patent segments the commodity database into multiple groups based on optical characteristics such as color, shape, and size. The image recognition unit first identifies the general category of the commodity based on its visual appearance, then the system only searches within the relevant segment of the database rather than processing all commodities. This segmentation maintains comprehensive search coverage for the identified category while dramatically reducing the number of comparisons needed, thus resolving the contradiction between search completeness and search time.
4Measurement precision
If detailed manual input is required for commodity specification, then accurate commodity identification can be achieved, but customer manipulation burden increases excessively
Solution Approach 1:
The patent implements a self-service commodity identification system where the scale and image recognition unit automatically perform commodity specification without requiring manual customer input. The system autonomously captures images of the commodities, retrieves reference images from the database, compares characteristics, and determines the commodity type. This automated self-service approach maintains accurate commodity identification while eliminating excessive manipulation burden, as customers simply need to place their commodities on the scale.
Data Source
AI summary
A commodity search device includes a storage unit, a measuring unit, a number input unit, a unit average weighing value calculator, and a first commodity search unit. The storage unit stores commodity information including a reference unit weight determined for each commodity. The measuring unit measures a total weight of commodities placed at a predetermined weighing position. The number input unit allows inputting the number of the weighed commodities. The unit average weighing value calculator divides the total weight of the commodities weighed by the measuring unit by the number input by the number input unit to calculate a unit average weighing value per one commodity. The first commodity search unit searches for commodities falling within a weight deviation from the storage unit using the unit average weighing value.