POS Item Prediction With Weight and Footprint Validation
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Solution Overview
Problem
Existing POS systems face challenges in accurately identifying items, especially when smaller items are obscured by larger ones or when items of similar appearance but different sizes are purchased, leading to incorrect predictions and increased computational resource usage.
Innovation Solution
Incorporating weight validation and item footprint analysis alongside computer vision to enhance item recognition, using sensors to measure weight and pressure points to validate predictions and reduce reliance on sole computer vision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer vision alone is used for item identification, then the system is simple to operate, but prediction accuracy deteriorates when items are obscured or visually similar
Solution Approach 1:
The patent combines computer vision technology with weight sensing technology to create a hybrid item identification system. The computer vision module captures images of items on the conveyor belt, while the weight sensing module simultaneously measures the weight of items. Both data streams are processed together to determine the identity of each item, allowing the system to overcome the limitations of either method used alone.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary that fuses visual data and weight data. The model takes both the image features extracted from computer vision and the weight measurements as inputs, processes them together, and outputs the predicted item identity. This intermediary layer enables the system to leverage the complementary strengths of both sensing modalities.
2Measurement precision
If multiple sensing modalities are integrated, then prediction accuracy improves, but computational resources increase
Solution Approach 1:
The patent implements a confidence-based processing strategy where the system first attempts to identify items using the less computationally intensive method (weight sensing or simple visual recognition). Only when confidence thresholds are not met does the system activate more resource-intensive processing, such as full computer vision analysis or iterative machine learning inference. This partial action approach reduces overall computational burden while maintaining high accuracy.
3Reliability
If weight validation is added to computer vision, then item recognition accuracy improves, but device complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model continuously compares predicted item identities against actual weight measurements and visual data. When discrepancies are detected between what the computer vision identifies and what the weight sensor measures, the system uses this feedback to correct identification errors and improve future predictions. The feedback loop enhances reliability by validating predictions through multiple independent measurement channels.
Data Source
AI summary
Techniques for predicting items for purchase at a point of sale (POS) system. These techniques include identifying one or more images, captured at a POS system, of one or more items for purchase. The techniques further include identifying a measured characteristic of the one or more items for purchase, the measured characteristic including at least one of: (i) a first weight measured using a weight sensor associated with the POS system or (ii) a footprint measured using one or more pressure or weight sensors associated with the POS system. The techniques further include predicting the items for purchase based on analyzing the one or more images using a machine learning (ML) model, and validating the predicted items for purchase using the measured characteristic.