Computational Probability System for Food Trigger Identification
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Solution Overview
Problem
Current methods for identifying and quantifying the causal relationship between health conditions and trigger foods consumed by individuals are inefficient and time-consuming, often requiring manual elimination diets that struggle with complex combinations of trigger substances and individual susceptibility factors.
Innovation Solution
A system utilizing processors and computer-readable storage devices to calculate the probability of causation of health conditions by identifying trigger substances, foods containing these substances, and associated risk factors, providing a systematic approach to rank the significance of trigger foods in relation to specific health conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual elimination diets are used to identify trigger foods, then the process can identify causal relationships between foods and health conditions, but the process becomes extremely time-consuming and complex when multiple symptoms and multiple trigger substances are involved
Solution Approach 1:
The patent replaces the manual mechanical process of elimination diets with an automated computational system. The system uses processors to execute algorithms that calculate probability scores, automatically analyzing food consumption data, health condition data, and trigger substance information to identify causal relationships without manual intervention.
Solution Approach 2:
The patent creates a computational model that replicates and automates the elimination diet process. Instead of manually tracking and analyzing food journals, the system uses software to copy and process this information, applying statistical algorithms to determine trigger foods and their probability of causation.
2Measurement precision
If comprehensive analysis of multiple trigger substances and risk factors is performed, then the accuracy of causation probability calculation is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex analysis into distinct modular components: data collection modules for food consumption and health conditions, trigger substance identification modules, risk factor analysis modules, and probability calculation modules. Each module handles a specific aspect of the analysis independently, reducing overall system complexity while maintaining comprehensive analysis capability.
Solution Approach 2:
The patent introduces computational algorithms and probability models as intermediaries between the input data (food consumption, health conditions) and the output (causation probability). These intermediary computational layers process and integrate multiple variables systematically, managing complexity through structured mathematical relationships.
3Measurement precision
If detailed food journals and consumption tracking are required, then the data accuracy for identifying trigger substances is improved, but the ease of operation and patient compliance decrease
Solution Approach 1:
The patent enables automated data collection where the system itself gathers food consumption information through various means (electronic tracking, database integration, or simplified user input). This reduces the burden on patients compared to traditional manual journaling, as the system performs much of the data collection and processing automatically.
Solution Approach 2:
The patent pre-processes and organizes data before full analysis is required. Food consumption data, trigger substance information, and risk factors are collected and structured in advance, so that when analysis is needed, the system can quickly process pre-organized information rather than requiring patients to maintain detailed journals during the analysis phase.
Data Source
AI summary
A method includes the steps of receiving data indicative of a selected health condition; identifying one or more trigger substances associated with the selected health condition; identifying one or more foods containing the identified one or more trigger substances, including the concentration of the trigger substance; receiving data indicative of a selection of the one or more identified foods, including the amount of the food consumed within a specified time interval; identifying one or more risk factors associated with the one or more trigger substances contained in the selected one or more foods; receiving data indicative of a selection of the one or more identified risk factors; and calculating a probability of causation of the selected health condition based on the selected foods, weight values associated with the identified trigger factors contained in the selected foods indicative of the relative significance of the substance as a trigger of the selected health condition, and the selected risk factors.