Heuristic Promotional Price Tag Detection via Image Recognition
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Solution Overview
Problem
Retail chains face inefficiencies in managing promotional price tags/signage due to unpredictable product locations across stores, leading to manual sorting and potential errors in signage placement.
Innovation Solution
A system utilizing a heuristic promotional price tag description extractor module, a heuristic rule deriver module, a store shelf image acquisition system, a barcode locator and recognizer module, and a heuristic promotional price tag classifier module to detect and maintain promotional price tags by extracting descriptions, deriving parameters, and analyzing images to identify and classify promotional price tags, thereby eliminating the need for manual sorting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual sorting of signage is performed to match product locations, then signage can be placed accurately, but labor time and operational complexity increase
Solution Approach 1:
The patent replaces the manual mechanical sorting process with an automated image recognition system using deep learning models. The system captures images of shelves, detects product locations and promotional tags automatically, and generates sorted signage lists without human intervention, thereby eliminating manual sorting time while maintaining placement accuracy.
Solution Approach 2:
The patent introduces an intermediary computational system that acts as a bridge between physical shelf layouts and signage distribution. The image recognition system and processing algorithms serve as intermediaries to translate visual shelf data into actionable signage sorting information, eliminating the need for direct manual sorting while ensuring accurate matching.
2Productivity
If product locations are made predictable across stores, then signage can be pre-sorted efficiently, but store flexibility and adaptability decrease
Solution Approach 1:
The patent implements a dynamic system that adapts to each store's unique layout by capturing actual shelf images and using image recognition to detect product locations in real-time. Rather than enforcing fixed predictable locations, the system dynamically adjusts to each store's configuration, maintaining both efficiency through automation and flexibility through adaptability to local variations.
Solution Approach 2:
The patent applies local quality by allowing each store to have its own unique product location characteristics detected through image recognition. The system processes each store's specific layout individually, generating customized signage sorting instructions tailored to that store's particular configuration, thereby preserving local flexibility while achieving overall efficiency.
3Extent of automation
If automated image recognition is implemented to detect promotional tags, then labor requirements decrease, but system complexity and initial resource investment increase
Solution Approach 1:
The patent employs universal deep learning models that can perform multiple functions: detecting promotional tags, identifying product locations, and extracting pricing information from diverse shelf images. This multi-functionality reduces the need for separate specialized systems, thereby decreasing overall system complexity while maintaining high automation levels across different detection tasks.
Solution Approach 2:
The patent uses digital copies of shelf images captured by cameras as proxies for physical inspection. These image copies are processed by automated recognition systems to extract all necessary information, replacing the need for complex physical detection devices while achieving the same automation goals through software-based analysis of visual data.
Data Source
AI summary
A system to detect and maintain retail store promotional price tags (PPTs) includes a heuristic PPT description extractor module, a heuristic rule deriver module, a store shelf image acquisition system, a barcode locator and recognizer module, and a heuristic PPT classifier module. The heuristic PPT description extractor module extracts heuristic descriptions of PPTs. The heuristic rule deriver module derives a set of heuristic parameters for the PPTs. The barcode locator and recognizer module analyzes images acquired by the store shelf image acquisition system to localize and recognize barcodes. The heuristic PPT description classifier module extracts heuristic attributes from the images acquired by the store shelf image acquisition system using the set of PPT parameters supplied by the heuristic rule deriver module.