Data aggregation and personalization for remotely controlled cooking devices
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Solution Overview
Problem
Cooking technologies lack the ability to accurately and efficiently guide users in achieving desired textures and tastes for food products, as existing methods rely heavily on user expertise and do not account for individual preferences or real-time cooking data.
Innovation Solution
A processor-based food preparation guidance system that receives and analyzes cooking parameters from various sources, including user interactions and real-time measurements from cooking appliances, to modify cooking programs and provide personalized cooking guidance and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If cooking instructions rely on user expertise and general recipes, then the system is simple to operate, but the cooking precision and ability to achieve desired textures and tastes deteriorates
Solution Approach 1:
The system continuously monitors cooking parameters (temperature, time, humidity) and compares them against ideal ranges for desired outcomes. Real-time feedback is provided to users through the interface, allowing adjustments to achieve precise cooking results without requiring expert knowledge.
Solution Approach 2:
The system automatically adjusts cooking parameters based on sensor data and pre-programmed recipes. The cooking appliance self-regulates temperature, timing, and other parameters to achieve desired textures and tastes, eliminating the need for user expertise while maintaining high precision.
2Adaptability or versatility
If the system collects and analyzes cooking parameters from multiple sources, then the personalization and cooking guidance quality improves, but the data processing complexity and time increases
Solution Approach 1:
The data processing system is divided into modular components: data collection from sensors and user input, data storage in structured formats, analysis algorithms for pattern recognition, and output generation for personalized guidance. This segmentation allows complex processing to be managed systematically without overwhelming the system.
Solution Approach 2:
Cooking programs and parameter ranges are pre-programmed into the system based on extensive testing and culinary expertise. This preliminary preparation allows the system to quickly personalize guidance by matching user inputs against pre-analyzed data, reducing real-time processing complexity while maintaining high adaptability.
3Manufacturing precision
If real-time cooking data is monitored and used to modify programs, then the cooking outcome quality improves, but the system complexity and computational requirements increase
Solution Approach 1:
Sensors continuously monitor cooking parameters and feed this data back to the control system. The system compares real-time data against target ranges and automatically adjusts cooking parameters or provides guidance to users, achieving high outcome quality through closed-loop control without requiring excessive computational complexity.
Solution Approach 2:
Manual monitoring and adjustment by expert cooks is replaced with automated electronic sensors and control algorithms. This substitution achieves precise cooking outcomes through electronic measurement and control, reducing the need for complex human expertise while maintaining or improving precision.
Data Source
AI summary
Systems, methods, and articles for gathering and utilizing individual and aggregate data from connected cooking devices, online recipe databases and/or mobile applications. Cooking instructions may be stored as processor-readable cooking programs that use mutable real time status updates as input. Such programs may be chosen via a user computing device and may be further parameterized by numeric, textual or camera-based inputs. Various methods may be used to determine what foods a user is preparing, and what equipment and techniques are used. This data may be supplemented with feedback to verify what was cooked, how the result compared to visual representation in advance of cooking, and satisfaction level. The data may be used to reproduce past cooking results, adjust future recipes, suggest recipes or products, or connecting users to online communities of users. Cooking data may be used to offer just-in-time problem solving, products, or connection to other users.