AI Descriptor Trail for Biological Data Trustworthiness
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Solution Overview
Problem
Acquiring trust in computing systems is challenging due to difficulties in accurately analyzing and utilizing large data sets, leading to potential mistrustworthiness.
Innovation Solution
A system and method using artificial intelligence that generates a descriptor trail by receiving biological extractions, selecting appropriate machine-learning processes, and recording these processes in a descriptor trail data structure to produce prognostic and ameliorative outputs, incorporating diagnostic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large quantities of data are analyzed using traditional computing methods, then data processing capability is improved, but accuracy and trustworthiness deteriorate due to difficulties in ensuring accurate utilization
Solution Approach 1:
The patent introduces an AI system as an intermediary between raw biological data and diagnostic conclusions. The AI descriptor trail generator acts as a mediator that processes complex biological extractions through multiple machine learning processes, creating an interpretable trail of reasoning that enhances trustworthiness while maintaining high data processing capability.
Solution Approach 2:
The system implements feedback mechanisms by generating descriptor trails that document the AI's reasoning process. This feedback loop allows verification of the AI's decision-making, enabling users to trace how conclusions were reached from raw data, thereby improving reliability without sacrificing processing speed.
2Measurement precision
If complex machine-learning processes are used to analyze biological data, then diagnostic accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex diagnostic process into distinct components: data reception, multiple specialized machine learning processes (prognostic, ameliorative, diagnostic), and descriptor trail generation. Each segment handles a specific aspect of analysis, improving diagnostic accuracy through specialized processing while making the overall system complexity manageable through modular architecture.
Solution Approach 2:
The system adds a new dimension to diagnostic systems by generating descriptor trails that document the reasoning process. This additional layer of interpretability transforms the system from a black-box complex model into one where the complexity is visible and verifiable, maintaining high diagnostic accuracy while reducing the effective complexity burden on users.
3Reliability
If multiple machine-learning processes are recorded in a descriptor trail, then transparency and trustworthiness are improved, but data processing time increases
Solution Approach 1:
The system performs preliminary action by generating descriptor trails concurrently with the machine learning processes rather than as a separate post-processing step. The trail generation is integrated into the data flow, capturing reasoning information as decisions are made, which maintains transparency while minimizing additional processing time.
Data Source
AI summary
A system for generating a descriptor trail using artificial intelligence. The system includes at least a server configured to receive at least a biological extraction. At least a server is configured to generate a prognostic output as a function of at least a biological extraction. At least a server is configured to generate an ameliorative output as a function of a prognostic output. The system includes a descriptor generator module operating on at least a server. A descriptor generator module is configured to generate at least a descriptor trail from a descriptor trail data structure wherein the descriptor trail further comprises at least an element of diagnostic data.