Autoimmune Nutrient Delivery System with Trigger Pattern Detection
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Solution Overview
Problem
Current alimentary design systems are inefficient in modifying nutritional delivery for users with autoimmune disorders, leading to poor nutrient delivery and ineffective edible programs.
Innovation Solution
A system and method using a computing device to identify user-specific autoimmune disorders, detect trigger patterns, and determine an aliment instruction set through machine-learning processes to provide optimized nutrient delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current alimentary design systems are used, then the system structure is simple, but the nutrient delivery effectiveness is poor
Solution Approach 1:
The system performs preliminary identification of autoimmune disorders, detection of trigger patterns, and determination of aliment instruction sets before nutrient delivery. This advance preparation ensures that the nutrient delivery is tailored to the user's specific condition, improving effectiveness without requiring complex real-time adjustments during delivery
Solution Approach 2:
The patent replaces traditional mechanical or manual alimentary design systems with a machine-learning-based computational system. The computing device automatically identifies disorders, detects patterns, and generates personalized aliment instructions, substituting complex computational processes for simpler traditional methods while achieving superior nutrient delivery effectiveness
2Adaptability or versatility
If personalized aliment instruction sets are generated through machine learning, then the adaptability to user needs improves, but the computational complexity increases
Solution Approach 1:
The system segments the complex task of personalized nutrient delivery into distinct functional modules: identifying autoimmune disorders, detecting trigger patterns, determining probable events, and generating aliment instruction sets. Each module handles a specific aspect of the analysis, making the overall system more manageable and adaptable while reducing the complexity burden of any single component
Solution Approach 2:
The machine-learning process incorporates feedback mechanisms where the system continuously analyzes user data, identifies patterns, and refines aliment instruction sets based on detected trigger patterns and probable events. This feedback loop enables the system to adapt to individual user needs dynamically while using computational algorithms that become more efficient with each iteration
Data Source
AI summary
A system for representing an arranged list of provider aliment possibilities, the system including a computing device designed and configured to receive an input representing an autoimmune disorder; identify a marker of the user relating to the autoimmune disorder; detect a trigger pattern as a function of the marker; determine, as a function of the trigger pattern, an aliment instruction set, wherein determining includes identifying at least a probable event as a function of the trigger pattern; and determining the aliment instruction set as a function of the at least a probable event; and represent the aliment instruction set on a display.