Automated Diagnostic System Using Dynamic Dialogue Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing diagnostic systems for complex problems require extensive and predetermined sets of questions, which can lead to user fatigue, duplication of questions, and inadequate coverage of the problem domain.
Innovation Solution
A computer-implemented method that uses machine learning models for automated diagnostics, processing input data from users to generate dynamic dialogue system outputs, and iteratively refining diagnoses based on user responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive predetermined sets of questions are used to ensure comprehensive problem coverage, then diagnostic accuracy is improved, but user fatigue increases and the number of questions needed increases
Solution Approach 1:
The diagnostic system dynamically generates and adapts question sets based on user responses and preliminary diagnoses, rather than using fixed predetermined question sequences. The machine learning models adjust which questions are asked next based on the evolving diagnostic state, making the process adaptive to each user's specific situation while maintaining comprehensive coverage.
Solution Approach 2:
The system implements feedback loops where user responses to questions are processed by machine learning models that generate preliminary diagnoses, which then inform the selection of subsequent questions. This feedback mechanism ensures that questions are asked strategically to maximize diagnostic accuracy while minimizing the total number of questions needed.
2Adaptability or versatility
If predetermined question sets are used to cover all possible problems, then problem domain coverage is improved, but question duplication increases
Solution Approach 1:
The system extracts only the necessary questions from the full problem domain based on preliminary analysis of user input. Machine learning models identify which specific questions are relevant to the user's particular symptoms and situation, extracting a minimized subset of questions from the complete question bank that would otherwise contain many redundant or unnecessary items.
Solution Approach 2:
The system changes the parameters of question selection dynamically based on user responses and diagnostic progress. Rather than asking a static set of all possible questions, the system adjusts which questions are asked by changing selection parameters based on real-time diagnostic information, ensuring comprehensive coverage with fewer questions.
3Productivity
If machine learning models process user input dynamically, then the number of questions needed is reduced, but system complexity increases
Solution Approach 1:
The system segments the diagnostic process into distinct functional modules: input processing modules that handle user responses, preliminary diagnosis models that generate initial assessments, and question selection modules that determine subsequent questions. This segmentation allows each component to be optimized independently while working together to achieve efficient dynamic diagnostics.
Data Source
AI summary
A method for performing automated diagnostics, comprising processing first input data relating to a speech or text input signal to generate a representation of the first input data and generating a first output based at least in part on the representation of the input data; determining a preliminary diagnosis output comprising at least one preliminary diagnosis of the problem by processing, using a preliminary diagnosis machine learning model, the first output; determining, at the one or more processors and based at least in part on the preliminary diagnosis output, at least one dialogue system output; outputting, by way of an output of the diagnostics system, the dialogue system output.