Shelf Tag Verification for Accurate In-Store Promotion Tracking
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Solution Overview
Problem
Current stock keeping methods fail to accurately track and maintain promotional states of slots in inventory structures within stores, leading to discrepancies between scheduled promotions and actual promotional tags, which can frustrate customers and reduce sales.
Innovation Solution
A robotic system autonomously navigates through a store to capture images of inventory structures, detect shelf and promotional tags, and compare their features to verify alignment with promotion schedules, generating prompts for store associates to correct or replace tags as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual tracking methods are used for promotional states, then device complexity is reduced, but measurement precision and reliability of promotion tracking deteriorate
Solution Approach 1:
The patent replaces manual mechanical tracking methods with an automated robotic system that uses computer vision and image processing to detect and verify promotional tags. The robotic system captures images of inventory structures, processes them through AI algorithms to identify promotional tags, and compares detected promotions against scheduled promotions, thereby substituting human labor with automated optical and computational systems.
Solution Approach 2:
The system enables self-verification of promotional tags by automatically comparing detected promotional information against the promotion schedule without requiring manual intervention. The robotic system independently navigates, captures images, processes data, and generates verification results, allowing the inventory system to self-monitor and self-report promotional accuracy.
2Reliability
If automated robotic systems are deployed to track promotions, then measurement precision and reliability improve, but device complexity and initial cost increase
Solution Approach 1:
The robotic system is designed to perform multiple functions: it navigates inventory structures, captures images of shelves and tags, processes images to detect promotional tags, compares detected promotions against schedules, and generates verification reports. This multi-functional design consolidates what could be separate systems into a single integrated platform, reducing overall system complexity while maintaining high reliability.
Solution Approach 2:
The system introduces an intermediary image processing layer that bridges the physical promotional tags and the digital promotion schedule. By capturing images of tags and processing them through computer vision algorithms, the system creates a digital representation that can be automatically compared against scheduled promotions, serving as a mediator between physical and digital tracking systems.
3Measurement precision
If frequent scanning is performed to maintain accurate promotional information, then measurement precision improves, but loss of time and productivity are reduced
Solution Approach 1:
The system implements periodic scanning at optimized intervals rather than continuous monitoring. The robotic system navigates inventory structures at scheduled scan cycles, capturing images and verifying promotional tags at these periodic intervals. This approach maintains accurate promotional information while avoiding the time loss and resource consumption of continuous scanning.
Solution Approach 2:
The system performs preliminary verification by comparing promotional tags against the scheduled promotion database before generating final verification results. This preliminary action allows the system to quickly identify obvious discrepancies and focus detailed analysis only on potentially problematic areas, reducing overall processing time while maintaining accuracy.
Data Source
AI summary
One variation of a method for tracking promotional states of slots in inventory structures within a store includes: accessing an image of an inventory structure within a store; detecting a shelf tag on the inventory structure in the image; extracting a set of features from the shelf tag detected in the image; detecting a promotional tag on the inventory structure in the image; extracting a set of features from the promotional tag detected in the image; detecting a deviation between the shelf tag and the promotional tag based on a difference between the sets of features; and, in response to detecting the deviation between the shelf tag and the promotional tag, identifying the first promotional tag as erroneous, and notifying a store associate to replace the first promotional tag with a second promotional tag at the first slot, the second promotional tag correcting the difference.