Smart refrigerator
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Solution Overview
Problem
Food waste is prevalent due to the difficulty in tracking items stored in refrigerators, especially in larger units where items are easily forgotten.
Innovation Solution
A smart refrigerator equipped with multiple cameras that capture images of its contents, transmitting them to a server for item identification using a neural network, allowing users to categorize and locate items, and providing feedback for improved identification accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of stationary object
If the refrigerator size is increased to provide more storage space, then the storage capacity is improved, but the ease of tracking items deteriorates
Solution Approach 1:
The system performs preliminary actions by automatically capturing images of stored items and pre-processing them through neural network identification. This allows items to be tracked and categorized before the user needs to search for them, eliminating the need for manual tracking in large storage spaces.
Solution Approach 2:
The patent creates visual copies of physical items through camera images. These digital copies are then processed by neural networks to identify and categorize items. This copying mechanism allows users to track items remotely through smartphone notifications without physically searching through large refrigerator compartments.
2Measurement precision
If multiple cameras are added to capture all refrigerator contents, then the measurement completeness is improved, but the device complexity increases
Solution Approach 1:
The patent divides the refrigerator interior into multiple zones (door trays, shelves, drawers) and assigns specific cameras to capture each zone. This segmentation allows comprehensive coverage while managing complexity through modular camera placement rather than a single complex system.
Solution Approach 2:
The captured images serve multiple functions: they are used for neural network training, item identification, freshness monitoring, and generating user notifications. This multi-functionality justifies the camera system complexity by maximizing the utility of each captured image across different application areas.
3Measurement precision
If user feedback is collected and used to train the neural network, then the identification accuracy is improved, but the time required for system setup increases
Solution Approach 1:
The system implements continuous feedback loops where user corrections to item identifications are collected and automatically used to retrain the neural network. This ongoing feedback process progressively improves identification accuracy without requiring manual intervention for each new item, as the system learns from accumulated user feedback over time.
Solution Approach 2:
The neural network training operates continuously in the background as users interact with the system, rather than requiring a separate setup phase. User feedback is immediately incorporated into training cycles, allowing the system to improve accuracy continuously while maintaining normal operation, thus minimizing perceived setup time.
Data Source
AI summary
A smart refrigerator has a body having a shelf and a drawer, a door coupled to the body and has a plurality of trays. The shelf has a corresponding camera configured to capture an image of items on the shelf. The drawer has a corresponding camera configured to capture an image of items in the drawer. The trays have a corresponding camera configured to capture an image of items in the trays. The images captured by the cameras are processed to identify the type of the item and the location of the item within the smart refrigerator.