Real-time Consumer Goods Monitoring via Sensor Tracking
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Solution Overview
Problem
Current methods for monitoring consumer goods in consumer settings rely on human counts, which are prone to errors and lack automated analysis, leading to inaccurate tracking and inventory management, and incur additional costs for human capital.
Innovation Solution
A real-time monitoring system using sensor devices, such as cameras, with convolutional neural networks and object tracking algorithms to detect, identify, and track consumer goods on a grill, generating notifications for inventory management, sales analysis, and safety protocols, and integrating with local and cloud computing for data processing and analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human physical counts are used to monitor consumer goods, then the system is simple and low-cost, but the accuracy and reliability of tracking are poor due to human error
Solution Approach 1:
The patent replaces manual human counting with an automated sensor-based monitoring system that uses cameras and image processing to detect, identify, and track consumer goods. This substitution eliminates human error while maintaining operational simplicity through automated processes.
Solution Approach 2:
The system enables self-monitoring of consumer goods through automated sensors and algorithms that independently track items without requiring human intervention. The convolutional neural networks and object tracking algorithms autonomously perform detection, identification, and tracking functions.
2Reliability
If automated sensor-based monitoring is implemented, then tracking accuracy and reliability improve, but the device complexity and initial costs increase
Solution Approach 1:
The patent replaces unreliable manual monitoring with a consistent automated sensor system that provides reliable, error-free tracking. The system uses cameras, convolutional neural networks, and object tracking algorithms to maintain consistent monitoring without human variability.
Solution Approach 2:
The patent introduces computing devices as intermediaries between sensors and the monitoring system, processing sensor data through convolutional neural networks and object tracking algorithms to produce reliable tracking results while managing system complexity.
3Productivity
If manual monitoring methods are used, then the system is easy to operate, but productivity and efficiency are low due to time-consuming processes
Solution Approach 1:
The patent implements continuous automated monitoring that operates without interruption, constantly tracking consumer goods as they move through the retail environment. This eliminates the intermittent nature of manual counting and maximizes monitoring efficiency.
Solution Approach 2:
The patent replaces time-consuming manual monitoring with automated sensor-based systems that process multiple items simultaneously, dramatically increasing productivity while the centralized computing system manages complexity.
4Loss of time
If physical counting by human beings is performed, then no additional equipment is needed, but loss of time occurs due to frequent manual interventions
Solution Approach 1:
The patent implements continuous automated monitoring that eliminates the need for periodic manual inventory checks. The system operates continuously, providing real-time tracking without requiring staff to stop other tasks for counting.
Solution Approach 2:
The system enables the monitoring function to serve itself through automated sensors and algorithms, eliminating the need for human capital allocation to inventory management tasks while reducing time loss.
Data Source
AI summary
In accordance with aspects of the present disclosure, a system of monitoring one or more consumer goods is provided. The system includes at least one monitoring device configured to capture and transmit data about the one or more consumer goods and at least one computing device configured to receive data from the at least one monitoring device, perform analysis on the captured data, and generate one or more notifications or triggers based on the analysis.