Article deduction apparatus, article deduction method, and program
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Solution Overview
Problem
Current systems lack precision in automatically determining articles taken out from shelves, which hinders labor reduction efforts in stores and factories.
Innovation Solution
An apparatus and method utilizing weight change data from sensors and movement data from depth sensors to accurately identify articles removed from shelves, combining these data types to output precise article determination information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If weight sensor data alone is used to determine article removal, then the system is simple to implement, but determination precision is insufficient
Solution Approach 1:
The patent combines weight change data from weight sensors with movement data from depth sensors to determine article removal. This merging of multiple data sources improves determination precision by cross-validating the removal event through both weight change and hand movement detection, resolving the contradiction between simplicity and precision.
Solution Approach 2:
The patent introduces an article deduction apparatus as an intermediary system that processes and correlates data from multiple sensors (weight sensors and depth sensors). This intermediary component integrates the information from different sources to achieve precise article determination without requiring direct complex interaction between the sensors themselves.
2Measurement precision
If multiple data sources are combined to improve article determination, then precision improves, but device complexity increases
Solution Approach 1:
The article deduction apparatus serves multiple functions: it acquires weight change data, acquires movement data, correlates these data sources, and determines article removal. This multi-functional design consolidates the complexity into a single apparatus that handles all processing tasks, improving precision while managing system complexity through functional integration.
3Productivity
If manual article tracking is used, then system complexity is low, but productivity and labor efficiency decrease
Solution Approach 1:
The system enables automatic detection and determination of article removal events through sensor data correlation. The weight sensors and depth sensors automatically track and identify when articles are removed without requiring manual intervention, thereby improving productivity and labor efficiency while the automated processing manages the system complexity.
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
Enhances the precision of article determination, improving operational efficiency by accurately identifying articles taken out, thus supporting labor reduction initiatives.
Implementation Method 1
weight change data being data based on a change in a detected value of a weight sensor
Data Source
AI summary
An article information deduction apparatus (10) includes an acquisition unit (110) and an output unit (120). The acquisition unit (110) acquires data based on a change in a detected value of a weight sensor (30) (hereinafter described as weight change data). For example, the acquisition unit (110) determines data acquired by chronologically arranging data acquired from the weight sensor (30) as weight change data. Further, the acquisition unit (110) acquires data indicating a movement of a hand of a person positioned in a shelf-front space (hereinafter described as movement data). For example, the acquisition unit (110) acquires data acquired by chronologically arranging data output from a depth sensor (40) to the article information deduction apparatus (10) as movement data. The output unit (120) outputs article determination information of the article deduced to be taken out by the hand of the person positioned in the shelf-front space, by using the weight change data and the movement data.