Mobile Product Mapping System for Retail Inventory Identification
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Solution Overview
Problem
Existing imaging systems are unable to effectively identify objects in captured images, particularly in retail settings, due to limitations in image processing capabilities.
Innovation Solution
A retail inventory identification system that employs multiple mobile product mapping systems equipped with image capture and processing capabilities, using machine learning modeling techniques to identify products and generate product identifying mapping, which includes product identifiers and location coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional imaging systems are used to capture product images, then image capture is achieved, but object identification capability is lacking
Solution Approach 1:
The patent introduces a separate machine learning processing system that acts as an intermediary between the imaging system and the identification output. The imaging system captures images, which are then processed by the external machine learning model to identify objects. This separates the capture function from the identification function, allowing each to be optimized independently.
Solution Approach 2:
The patent replaces traditional mechanical or algorithmic image processing methods with machine learning-based object recognition. Instead of using conventional computer vision algorithms that require complex processing, the system uses trained machine learning models that can identify objects more accurately with simpler processing pipelines.
2Measurement precision
If significant image processing capabilities are used to identify products, then identification accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies machine learning models that have been pre-trained on extensive product datasets before deployment. This preliminary training phase allows the model to learn complex product patterns and features in advance, so that during actual product identification, the processing is much faster and requires less computational effort while maintaining high accuracy.
3Adaptability or versatility
If traditional imaging systems are used, then hardware simplicity is maintained, but object recognition capability is insufficient
Solution Approach 1:
The patent divides the system into distinct functional segments: an imaging system that captures images and a separate machine learning processing system that performs identification. This segmentation allows the imaging hardware to remain relatively simple while the intelligence is concentrated in the software-based machine learning component, achieving high adaptability without proportionally increasing hardware complexity.
Data Source
AI summary
Some embodiments provide retail inventory identification systems, comprising: a mobile product mapping system comprising: a transport system, a tower housing, and a cluster processing system comprising: a plurality of product identifying (PI) systems each in different orientations and directed along different fields of view, and a mobile control circuit; wherein the PI systems each comprise an image capture system, and a location determination system, wherein each of the plurality of PI systems is configured to: capture images of products supported on product support systems; and locally process the images comprising applying a plurality of different machine learning modeling techniques, to infer identification of multiple products and generate product identifying information in association with location information; and the mobile control circuit is configured to generate product identifying mapping of products as the product mapping system moves; and communicate the generated product identifying mapping to an inventory management system.