AI Descriptor Trail for Diagnostic Data Trust
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Solution Overview
Problem
Acquiring trust in computing systems is challenging due to difficulties in accurately analyzing and utilizing large data quantities, often leading to frustration and lack of trustworthiness.
Innovation Solution
A system and method using artificial intelligence to generate and update a descriptor trail, which includes diagnostic and machine-learning data, by receiving user and advisor inputs, to provide a graphical user interface with prognostic and ameliorative outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If large quantities of data are analyzed using computing systems, then more comprehensive diagnostic and machine-learning data can be obtained, but accuracy and trustworthiness of the analysis deteriorate
Solution Approach 1:
The system segments the analysis process into multiple stages: collecting diagnostic data from multiple sources, generating intermediate machine-learning data through AI processing, and producing final descriptor trails. This segmentation allows manageable processing of large data quantities while maintaining accuracy at each stage through specialized AI algorithms.
Solution Approach 2:
The patent introduces an artificial intelligence system as an intermediary between raw diagnostic data and final analysis results. The AI processes large quantities of diagnostic data through machine-learning algorithms, generating intermediate machine-learning data that bridges the gap between voluminous input data and accurate output insights, thereby preserving measurement precision despite large data quantities.
2Reliability
If traditional computing systems analyze data, then processing can be performed, but trustworthiness and user confidence deteriorate due to inaccurate utilization
Solution Approach 1:
The system implements feedback mechanisms where the AI continuously processes user inputs and advisor inputs, generating updated descriptor trails that reflect accumulated knowledge. This feedback loop enhances trustworthiness by demonstrating consistent, accurate data utilization over time, while the structured feedback process manages complexity through standardized processing protocols.
Solution Approach 2:
The artificial intelligence system performs self-service by autonomously processing diagnostic data, generating machine-learning data, and creating descriptor trails without requiring complex human intervention at each step. This self-service capability enhances reliability while managing complexity through automated AI processes that handle data analysis consistently and accurately.
3Measurement precision
If AI processes user and advisor inputs to generate descriptor trails, then accurate data utilization is achieved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-processing diagnostic data through AI to generate machine-learning data and initial descriptor trails before final analysis is needed. This preliminary processing of user constitutional data and advisor inputs establishes a foundation of accurate intermediate results, reducing the time required for final accurate analysis while maintaining measurement precision.
Data Source
AI summary
A system for updating a descriptor trail using artificial intelligence. The system is configured to display on a graphical user interface operating on a processor connected to a memory an element of diagnostic data. The system is configured to receive from a user client device an element of user constitutional data. The system is configured to display on a graphical user interface the element of user constitutional data. The system is configured to prompt an advisor input on a graphical user interface. The system is configured to receive from an advisor client device an advisor input containing an element of advisory data. The system is configured to generate an updated descriptor trail as a function of the advisor input. The system is configured to display the updated descriptor trail on a graphical user interface.