System and method for computer vision driven applications within an environment
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Solution Overview
Problem
Current inventory management systems are prone to errors and inefficiencies, particularly in tracking inventory in retail environments, as they rely on manual counting and traditional point of sale systems, which can lead to long lines and dissatisfaction among customers, and fail to provide real-time, accurate data on inventory status and product interactions.
Innovation Solution
A computer vision-driven system and method that utilizes an environmental object graph (EOG) to monitor and track objects within an environment, enabling automatic inventory management and checkout processes by analyzing image data from a network of imaging devices to update object states and associations across time and space, even in imperfect or obscured conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual counting of inventory is used, then operational processes can be performed with traditional tools, but errors increase and productivity decreases
Solution Approach 1:
The patent replaces manual mechanical counting processes with automated computer vision systems using imaging devices and machine learning algorithms. The system captures images of inventory items, uses object detection models to identify and count items automatically, and updates inventory records without human intervention, thereby eliminating human error and improving both accuracy and efficiency.
Solution Approach 2:
The inventory management system performs self-monitoring through continuously capturing images and automatically processing them to detect item locations, quantities, and states. The system autonomously updates inventory databases and triggers restocking alerts without requiring manual operation, enabling the inventory to effectively monitor and manage itself.
2Ease of operation
If traditional point of sale systems are used, then charging processes can be performed, but customer wait time increases and satisfaction decreases
Solution Approach 1:
The system continuously monitors and tracks items as customers place them in carts or bags throughout the shopping experience, pre-processing the checkout information in advance. By the time customers reach the payment point, the system has already identified all items, calculated totals, and prepared transaction data, eliminating traditional checkout waiting time while maintaining an easy payment process.
3Loss of information
If periodic inventory views are provided, then operational monitoring can be performed, but real-time accuracy is lost and responsiveness decreases
Solution Approach 1:
The system implements continuous image capture and processing operations, with imaging devices constantly monitoring inventory areas and the processing system continuously analyzing new images to detect item movements, additions, or removals. This continuous operation provides real-time inventory status updates without periodic interruptions, maintaining both information accuracy and fast response to inventory changes.
Data Source
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AI summary
A system and method for computer vision driven applications in an environment that can include collecting image data across an environment; maintaining an environmental object graph from the image data whereby maintaining the environmental object graph is an iterative process that includes: classifying objects, tracking object locations, detecting interaction events, instantiating object associations in the environmental object graph, and updating the environmental object graph by propagating change in at least one object instance across object associations; and inspecting object state for at least one object instance in the environmental object graph and executing an action associated with the object state. The system and method can be applied to automatic checkout, inventory management, and/or other system integrations.