Weight Sensor Inventory Tracking via Denoised Data Analysis
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Solution Overview
Problem
Traditional inventory tracking systems in facilities are intrusive, require special tags or devices, and are costly, leading to inefficiencies and inaccuracies in monitoring inventory movement.
Innovation Solution
A system utilizing weight sensors at inventory locations to generate sensor data, which is processed to determine interaction data such as item type, quantity, and location of movement, eliminating the need for user-carried devices and improving precision and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional inventory tracking systems using tags or devices are deployed, then inventory movement can be monitored, but system complexity and cost increase
Solution Approach 1:
The patent extracts the tracking functionality from user-carried devices and tags, placing weight sensors instead at the inventory location (shelf/platform) to measure weight changes. This eliminates the need for special tags or user-manipulated devices while maintaining tracking capability through automated weight monitoring.
Solution Approach 2:
The system enables the inventory location itself to perform tracking by using weight sensors that automatically detect item placement and removal without requiring user action. The sensors self-monitor weight changes and generate interaction data autonomously, eliminating dependence on user-carried scanning devices.
2Reliability
If traditional inventory tracking systems with user-carried devices are used, then inventory movement is tracked, but operational efficiency decreases
Solution Approach 1:
The weight sensors at the inventory location automatically perform tracking without requiring user intervention. The system self-monitors weight changes and generates interaction data, eliminating the need for users to manually scan or report inventory movements, thereby improving operational efficiency.
Solution Approach 2:
The patent replaces manual mechanical scanning operations with automated weight-based detection. Instead of users physically scanning items with handheld devices, the system uses weight sensors to automatically detect and record inventory interactions, streamlining the process and improving productivity.
3Reliability
If traditional inventory tracking systems are implemented, then inventory movement monitoring is achieved, but costs increase
Solution Approach 1:
The patent removes expensive components such as user-carried scanning devices, tags, and specialized hardware from each user or item. Instead, it uses simple weight sensors at the inventory location that can be integrated into existing shelf structures, significantly reducing system cost while maintaining tracking accuracy.
Solution Approach 2:
The system replaces expensive, complex tracking devices with inexpensive weight sensors that can be easily deployed and replaced. The weight sensors are simple, low-cost components compared to traditional RFID tags, barcode scanners, or other sophisticated tracking technologies.
4Measurement precision
If weight sensors are used at inventory locations, then precision and accuracy of inventory tracking improve, but device complexity increases
Solution Approach 1:
The patent extracts the complexity from user devices and concentrates it in simple weight sensors at the inventory location. The weight sensors provide precise measurement data that is then processed by a server, separating the measurement function from the processing function and reducing overall system complexity.
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
The system enhances operational efficiency, reduces costs, and improves accuracy by automating inventory tracking without the need for user-manipulated scanners, enabling precise monitoring of inventory movements within facilities.
Implementation Method 1
Weight sensors 104 at an inventory location 102 generate sensor data 112
Data Source
AI summary
One or more load cells measure the weight of items on a shelf or other fixture. Weight changes occur as items are picked from or placed to the fixture. Output from the load cells is processed to produce denoised data. The denoised data is processed to determine event data representative of a pick or a place of an item. Hypotheses are generated using information about where particular types of items are stowed, the weights of those particular types of items, and the event data. A high scoring hypothesis is used to determine interaction data indicative of the type and quantity of an item that was added to or removed from the fixture. If ambiguity exists between hypotheses, additional techniques such as data about locations of weight changes and fine grained analysis may be used to determine the interaction data.