Product Identifier Clustering for Accurate Shelf Inventory Mapping
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Solution Overview
Problem
Manual inspection of product storage facilities is time-consuming and increases operational costs due to the large number of shelves and products, necessitating a more efficient inventory management system.
Innovation Solution
A system utilizing a trained machine learning model to group product identifiers based on visual and textual similarity, and relationships, with a mobile image capture device capturing images from various angles and transmitting them for processing to update inventory automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of product storage areas is performed, then inventory accuracy can be maintained, but time consumption and operational costs increase significantly
Solution Approach 1:
The patent replaces manual visual inspection with an automated image recognition system using machine learning models. Image capture devices photograph products on shelves, and trained ML models automatically identify and classify products, substituting human mechanical inspection with automated optical and computational systems.
Solution Approach 2:
The system enables self-service inventory monitoring where the machine learning model autonomously processes images, identifies products, updates inventory databases, and triggers restocking alerts without human intervention. The system serves itself by automatically completing the entire inventory management workflow.
2Reliability
If manual inspection is performed frequently to maintain inventory accuracy, then stock availability can be ensured, but operational costs increase due to worker time
Solution Approach 1:
The system replaces human workers with automated image capture devices and machine learning models to perform inventory monitoring. This substitution eliminates labor costs associated with frequent inspections while maintaining continuous stock availability through automated tracking.
Solution Approach 2:
The automated system enables continuous inventory monitoring without the interruptions inherent in manual inspection schedules. Image capture devices can continuously or periodically photograph shelves, and the ML model processes images in real-time, ensuring uninterrupted stock availability monitoring.
3Measurement precision
If product identifiers are grouped based on multiple criteria (visual, textual, relational), then identification accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the product identification process into distinct analytical components: visual similarity analysis (image comparison), textual similarity analysis (description matching), and relationship analysis (product hierarchy). Each component processes specific data types independently, then results are integrated to form comprehensive product groupings.
Solution Approach 2:
The system changes analytical parameters by examining products from multiple dimensions: visual characteristics (images), textual characteristics (descriptions), and relational characteristics (product relationships). By varying the parameters of analysis, the system achieves accurate identification while managing complexity through structured multi-criteria evaluation.
Data Source
AI summary
In some embodiments, apparatuses and methods are provided herein useful to processing captured images. In some embodiments, there is provided a system for processing captured images of objects at a product storage facility including a trained machine learning model; and a control circuit. The control circuit may group a plurality of product identifiers into one or more clusters based on at least one of visual similarity of corresponding images, textual similarity of corresponding associated descriptions, and associated relationships between product identifiers of the plurality of product identifiers; determine clusters having common elements that are at least within a similarity threshold of each other; merge the clusters with the common elements; and generate a mapping dataset used to retrain the trained machine learning model to identify a plurality of objects. The mapping dataset may include a plurality of associations of associated product identifiers to a single object.