Article Management Apparatus for Label Installation Verification
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Solution Overview
Problem
Conventional article managing apparatuses face inefficiencies when dealing with large numbers of articles, as they often result in numerous mismatches between collated images and stock lists, making it cumbersome for operators to check installation conditions of labels.
Innovation Solution
An apparatus that captures images of shelves and labels, processes the images to detect character areas, recognizes characters, and compares them with pre-defined plan data to assign attributes indicating correct or incorrect installation, thereby creating a work list to streamline the checking process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional article managing apparatus collates images and stock lists for large numbers of articles, then the stock list is updated, but numerous mismatches occur making it troublesome for operators to check
Solution Approach 1:
The patent segments the checking process by dividing articles into groups based on their attributes (e.g., category, size, price range). Instead of requiring operators to check all articles uniformly, the system creates segmented checklists where operators only need to verify mismatches within specific segments, significantly reducing the overall checking workload while maintaining comprehensive coverage.
Solution Approach 2:
The patent introduces an intermediary attribute-based classification system between the image collation process and the final stock list update. This intermediary layer groups articles by attributes and highlights only those with mismatches, acting as a mediator that filters out correct matches and presents only problematic cases to operators for verification.
2Quantity of substance
If the number of articles is large, then more comprehensive stock management is achieved, but the number of mismatches between collation results and stock list increases
Solution Approach 1:
The patent applies local quality by treating different attribute categories (e.g., product type, size, color) with different levels of verification strictness. High-value or critical items are subject to more rigorous checking, while routine items use standard verification. This localized quality approach maintains high measurement precision for important articles while efficiently managing large quantities.
Solution Approach 2:
The patent implements partial action by not requiring verification of every single article match. Instead, it performs exhaustive automated collation and then applies selective human verification only to identified mismatches. This partial human intervention approach maintains high precision for critical cases while scaling efficiently to large article quantities.
3Extent of automation
If automated collation is performed for all articles, then stock list updates are comprehensive, but operator time for verifying mismatches increases
Solution Approach 1:
The patent performs preliminary automated collation and attribute-based grouping before presenting tasks to operators. By pre-processing the data to identify and segment mismatches, the system prepares the work in advance so that operators receive pre-filtered, organized tasks rather than raw comprehensive lists, significantly reducing their verification time.
Solution Approach 2:
The patent implements a feedback loop where operator verification results are fed back into the system to refine future collation and grouping. The system learns from operator corrections and adjustments, improving the accuracy of mismatch identification over time, which reduces the amount of verification time needed as the system becomes more accurate.
Data Source
AI summary
According to one embodiment, processing circuitry of an apparatus for managing articles acquires a check image by capturing labels including label information of a character string. The processing circuitry also acquires plan data including a plurality of plan areas indicating installation positions of the labels in the shelf and the label information of the labels assigned to each of the plan areas. The processing circuitry detects a character area of the character string displayed on each of the labels from the check image and recognizes characters in the character areas. The processing circuitry positions and superimposes the check image and the plan data and associates the character areas and the plan areas. The processing circuitry compares the label information of the associated plan areas and the recognition information of the character areas and assigns attributes to the character areas or the plan areas.