Cook Completion Prediction Using Non-Linear Temperature Profiles
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Solution Overview
Problem
Existing food cook time estimation systems rely on imprecise linear estimation formulas, failing to harness robust data dimensionality for enhanced precision in cook session planning.
Innovation Solution
A system and method that utilize non-linear prior cook profiles based on food temperature data to estimate cook completion time, incorporating higher data dimensionality and machine learning techniques for accurate cook time prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If linear estimation formulas are used for cook time prediction, then the system is simple to operate, but the measurement precision is insufficient
Solution Approach 1:
The patent transitions from linear one-dimensional estimation to non-linear multi-dimensional analysis by incorporating temperature data collected at multiple time points throughout the cooking process. This dimensional expansion enables the system to capture the complex non-linear relationship between temperature evolution and cook completion, significantly improving precision while managing complexity through automated sensor-based data collection.
Solution Approach 2:
The patent replaces simple linear calculation mechanisms with machine learning-based non-linear prediction models. These models automatically analyze temperature progression patterns and predict cook completion time without requiring complex user intervention, thereby improving measurement precision while maintaining ease of operation through automated processing.
2Measurement precision
If non-linear prior cook profiles with higher data dimensionality are used, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The system implements self-service through automated temperature monitoring and profile matching. The temperature sensor continuously collects data, the system automatically compares current temperature profiles against stored non-linear prior cook profiles, and the machine learning model autonomously predicts cook completion time. This automation handles the increased data processing complexity internally while keeping the user interface simple.
Solution Approach 2:
The patent employs preliminary action by pre-storing multiple non-linear prior cook profiles in the database before actual cooking occurs. These pre-computed profiles serve as reference patterns that the system can quickly match against current cooking data, reducing real-time computational complexity while maintaining high prediction precision through sophisticated pre-processing.
3Measurement precision
If machine learning techniques are implemented for cook time prediction, then the measurement precision is enhanced, but the ease of operation decreases
Solution Approach 1:
The machine learning system operates autonomously without requiring user expertise in data science or complex parameters. The model automatically processes temperature data, selects appropriate prior profiles, and generates predictions. This self-service capability masks the underlying complexity of machine learning operations, maintaining ease of operation while delivering enhanced prediction accuracy.
Solution Approach 2:
The patent introduces an intermediary layer between the simple temperature sensor input and the complex machine learning model. This intermediary automatically pre-processes raw temperature data, normalizes it against stored profiles, and feeds it to the prediction algorithm. This abstraction layer shields users from complexity while enabling sophisticated analysis, thereby maintaining ease of operation with enhanced precision.
Data Source
AI summary
A computer-implemented method for food cook completion estimation that includes: acquiring food temperature data from a first food item, the food temperature data comprising a plurality of food temperatures detected at a plurality of different times and reflecting a change in food temperature over time; selecting, based on the change in food temperature, a first non-linear prior cook profile from a plurality of non-linear prior cook profiles stored in a database that includes the first non-linear prior cook profile; and estimating a cook completion time based on the first non-linear prior cook profile.