Smart Shelf Weight Distribution Pattern Recognition
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Solution Overview
Problem
Existing smart shelves lack an efficient method to accurately identify and differentiate items based on their weight distribution patterns, leading to challenges in inventory management and product recognition.
Innovation Solution
A method utilizing an array of force-sensing resistors (FSRs) to detect and analyze the weight distribution patterns on a smart shelf, comparing these patterns to known codes to identify items, and calibrating sensors to ensure accurate readings over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If force-sensing resistors are used to detect weight distribution patterns, then item identification accuracy is improved, but sensor calibration complexity increases
Solution Approach 1:
The system performs preliminary calibration by detecting known reference items (such as empty shelf states or standard test weights) and storing their weight distribution patterns as baseline data. This preliminary action establishes reference codes that simplify subsequent item identification, as the system only needs to compare new readings against pre-stored patterns rather than performing complex real-time analysis.
Solution Approach 2:
The patent creates simplified digital representations (codes) of weight distribution patterns by capturing key characteristic points from the sensor array. Instead of storing or processing the complete raw sensor data matrix, the system copies only the essential features (such as peak locations, relative weights, and spatial relationships) into compact code formats, reducing calibration and processing complexity while maintaining identification accuracy.
2Measurement precision
If multiple sensors are used to detect weight distribution patterns, then item differentiation capability is improved, but data processing complexity increases
Solution Approach 1:
The system extracts only the essential features from the complete sensor data set by identifying and isolating key characteristic points (such as maximum weight locations, secondary peaks, and spatial relationships between contact points). This extraction process removes redundant information and focuses processing only on the discriminative features needed for item identification, significantly reducing data processing complexity while maintaining differentiation capability.
Solution Approach 2:
The patent transforms the raw sensor data (multiple sensor readings across the array) into a different parameter space by converting physical weight distribution measurements into standardized codes. This parameter transformation involves normalizing values, calculating relative positions, and encoding spatial relationships in a format that simplifies comparison and pattern recognition, thereby reducing processing complexity.
3Measurement precision
If weight distribution patterns are analyzed for item identification, then product recognition accuracy is improved, but measurement time increases
Solution Approach 1:
The system performs partial analysis by focusing only on the most discriminative features of the weight distribution pattern rather than processing the complete data set. By identifying and analyzing only the key characteristic points (such as 3-5 primary contact points or peaks in the distribution) that provide sufficient differentiation, the system achieves accurate product recognition with reduced processing time compared to analyzing all sensor data points.
Solution Approach 2:
The system prepares reference weight distribution patterns and their corresponding item codes in advance during a calibration phase. During actual measurement, the system only needs to compare new readings against these pre-prepared references using simple pattern matching algorithms, significantly reducing measurement time while maintaining recognition accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables precise identification and differentiation of items on a smart shelf, improving inventory management and reducing errors in product recognition and inventory control.
Implementation Method 1
Other smart shelf embodiments have been realized where force sensing resistors (FSRs) are located at the cross-points within an area matrix. In this embodiment, it is possible to detect a variable value of resistance as a function of force applied in any particular spot or region.
Data Source
AI summary
A method of reading and quantifying pressure points or bumps or a product outline to extract a pattern that, when decoded, uniquely defines a product or class of products by defining the theoretical centers of applied pressure, determining the spatial relationship between more than one center or a product outline to define which pattern belongs to which product, and correlating each pattern with templates of stored patterns to determine what product is represented by each respective pattern.