Non-Linear Cook Time Estimation Using Prior Temperature Profiles
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Solution Overview
Problem
Existing food cook time estimation systems rely on linear estimation formulas that are imprecise and fail to harness robust data dimensionality for enhanced precision.
Innovation Solution
A system and method for non-linear food cook time estimation using a database of non-linear prior cook profiles, acquired from food temperature data, to estimate cook completion time based on selected profiles that match current cooking conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If linear estimation formulas are used for cook time planning, then the system is simple and easy to operate, but the measurement precision of cook time estimation is poor
Solution Approach 1:
The patent transitions from linear one-dimensional estimation to non-linear multi-dimensional analysis by incorporating multiple temperature data points collected at different times during cooking. This dimensional expansion allows the system to capture the complex thermal behavior of food, significantly improving cook time estimation precision while managing system complexity through automated profile matching.
Solution Approach 2:
The system pre-calculates and stores multiple non-linear cook profiles representing different cooking scenarios and food types before actual cooking begins. During cooking, the system simply matches current temperature data against these pre-computed profiles, avoiding real-time complex calculations and maintaining operational simplicity while achieving high precision through the richness of pre-analyzed data.
2Measurement precision
If linear estimation formulas are used, then the system structure is simple, but the estimation precision is insufficient and cannot harness robust data dimensionality
Solution Approach 1:
The patent explicitly addresses data dimensionality by collecting temperature measurements at multiple time points during cooking, transforming single-point linear estimates into multi-point non-linear profiles. This dimensional enrichment captures the dynamic thermal characteristics of different food types, enabling precise estimation while fully utilizing available data without information loss.
3Measurement precision
If non-linear prior cook profiles with high data dimensionality are used, then the measurement precision is enhanced, but the device complexity increases
Solution Approach 1:
The system performs complex non-linear analysis and profile generation in advance, before actual cooking sessions. By pre-processing and storing multiple dimensional cook profiles for various food types and cooking methods, the system eliminates the need for real-time complex calculations during cooking, maintaining high precision while reducing operational complexity to simple profile matching.
Solution Approach 2:
The system creates simplified copies of complex cooking processes by generating representative non-linear temperature profiles that capture essential cooking characteristics. These profile copies serve as lookup references during cooking, allowing the system to achieve high estimation precision through pattern matching rather than complex real-time computation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Provides enhanced precision in cook time estimation by leveraging higher data dimensionality and real-time, automated cook completion time predictions.
Implementation Method 1
acquire 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
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.