AI Logic Engine for Personalized Nutritional Dosing
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Solution Overview
Problem
Current nutritional management systems lack a predictive element that considers a patient's disease state and nutritional state, failing to effectively address nutritional abnormalities and provide personalized dosing recommendations, especially for patients with chronic conditions.
Innovation Solution
A method and logic engine that collect and analyze patient data, including electronic medical records and nutritional intake, to determine optimized doses of food and nutrients, incorporating outside data and AI algorithms to suggest dietary alterations and correct nutritional imbalances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI algorithms and outside data are incorporated to provide personalized nutritional dosing, then nutritional management effectiveness is improved, but system complexity increases
Solution Approach 1:
The system segments nutritional management into distinct functional modules: data collection module, AI analysis module, dosing recommendation module, and monitoring module. Each module performs a specific function, making the complex system manageable and maintainable while achieving reliable personalized nutritional management through coordinated operation of these specialized components.
Solution Approach 2:
The patent introduces an intermediary AI logic engine that acts as a mediator between raw patient data and nutritional dosing recommendations. This intermediary processes and interprets complex multi-source data (electronic medical records, nutritional intake data, outside data) using AI algorithms, transforming unstructured information into actionable dosing guidance without requiring direct complex interactions between all system components.
2Measurement precision
If comprehensive patient data collection is performed to enable personalized dosing, then dosing accuracy is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting and organizing patient data from multiple sources (electronic medical records, nutritional intake data) before dosing decisions are needed. Outside data and AI models are pre-trained and prepared in advance, so when a dosing recommendation is required, the system can quickly process the pre-organized data without time-consuming data gathering or model training at the moment of decision-making.
Solution Approach 2:
The patent replaces manual data processing and analysis with automated AI algorithms and machine learning models. Instead of relying on manual review of comprehensive patient data by nutritionists or doctors, the AI system automatically processes large volumes of structured and unstructured data, extracting relevant features and generating dosing recommendations rapidly while maintaining high accuracy.
3Adaptability or versatility
If AI algorithms are used to analyze patient data and determine dosing, then personalized nutrition planning is improved, but implementation difficulty increases
Solution Approach 1:
The AI-powered system enables self-service personalized nutrition planning where the algorithm automatically analyzes patient data, determines nutritional requirements, and generates customized dosing recommendations without requiring extensive manual intervention. The system adapts to individual patient characteristics and automatically adjusts recommendations based on feedback and changing conditions, making personalized nutrition planning accessible and easy to implement.
Solution Approach 2:
The patent implements adaptability through dynamic parameter adjustment in the AI models. The system changes key parameters such as nutrient requirements, dosing schedules, and food recommendations based on individual patient parameters (age, weight, disease state, nutritional status). This parameter-based approach allows the system to provide personalized nutrition planning by simply adjusting numerical values and weights in the AI algorithms rather than requiring completely different treatment protocols for each patient.
Data Source
AI summary
This application presents a method and logic engine for personalized dosing of food and nutrients to enhance an individual's overall well-being. The method involves collecting comprehensive data, including medical records, nutritional habits, and individual traits. Utilizing AI algorithms, the system analyzes the data in conjunction with external criteria, establishing optimal dosing parameters. The logic engine employs techniques such as K-nearest neighbor analysis and expert rules to determine precise dosages that maximize therapeutic effects while minimizing adverse outcomes. The system generates practitioner-readable reports and can interface with medical devices for dose administration. Additionally, a personalized health assessment system adapts nutrition plans based on real-time data and user feedback. A dedicated social support platform encourages engagement and information exchange among users with similar health conditions.