AI Physiological State Guidance Using Diagnostic and Vector Outputs

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Solution Overview

Problem

Accurate and personalized recommendations for a user's physiological state are challenging due to the lack of individualized approaches, which can sometimes be harmful if not tailored to the user's specific needs.

Innovation Solution

A system and method using artificial intelligence that calculates a diagnostic output through biological extraction, classifies it using a physiological classifier and classification algorithm, generates a vector output for the user's physiological state, updates it based on user input, and identifies personalized recommendations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If personalized recommendations are implemented, then the effectiveness of health guidance is improved, but the system complexity and data processing requirements increase

Engineering Contradiction:

Improveeffectiveness of health recommendationsVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system segments the complex task of personalized health recommendation into multiple independent modules: biological extraction module, diagnostic output generation module, vector output generation module, and recommendation identification module. Each module processes specific aspects of the data independently, reducing overall system complexity while maintaining personalized recommendation effectiveness.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces intermediate data structures (diagnostic output, vector output) that mediate between raw biological extraction and final recommendations. These intermediaries organize and structure data in manageable formats, allowing complex personalized analysis without overwhelming system complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If accurate physiological state assessment is performed, then the precision of health recommendations is improved, but the measurement and data processing difficulty increases

Engineering Contradiction:

Improveaccuracy of physiological state assessmentVSAvoiddata processing difficulty

Core Design Contradiction:

Measurement precisionVSDifficulty of detecting and measuring

Solution Approach 1:

The system replaces manual physiological assessment with automated machine learning processes. The physiological classifier and clustering algorithms automatically analyze biological extraction data to determine physiological states, eliminating the need for manual measurement and interpretation while maintaining high accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system transforms complex biological data into simplified parameter representations through vector output generation. By converting detailed biological extraction data into condensed vector formats, the system maintains measurement precision while reducing processing difficulty for subsequent analysis stages.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If dynamic updates based on user input are implemented, then the adaptability of recommendations is improved, but the processing time and system responsiveness requirements increase

Engineering Contradiction:

Improveadaptability of recommendationsVSAvoidprocessing time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary classification of user input using the physiological classifier before full processing. This preliminary action quickly identifies the nature and relevance of user feedback, allowing the system to prioritize and process only the most critical updates, thereby reducing overall processing time while maintaining adaptability.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements periodic updates rather than continuous processing. By updating recommendations at specific intervals triggered by user input thresholds or time-based schedules, the system maintains adaptability to user needs while avoiding constant processing that would increase time loss.

Inventive Principle:

Principle #19Periodic action

Data Source

PatentUS12073942B2Methods and systems for dynamic constitutional guidance using artificial intelligence

Publication Date: 2024.08.27 KPN INNOVATIONS LLC

AI summary

A system for dynamic conditional guidance using artificial intelligence. The system includes a computing device, designed and configured to c calculate a diagnostic output using a biological extraction related to a user, and a first machine-learning process, wherein the diagnostic output identifies a prognostic label and an ameliorative label; classify, using a physiological classifier and a first classification algorithm, the diagnostic output to a physiological state for the user; generate a vector output for the physiological state for the user, using a clustering algorithm; receive a user input generated in response to the diagnostic output; update the vector output using the user input; and identify a recommendation for the user, utilizing the updated vector output.