ML Model Condition Detection Automation
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Solution Overview
Problem
Current diagnostic methods, such as medical and industrial diagnostics, heavily rely on manual processes and human expertise, which are costly and inefficient, necessitating the development of automated solutions for interpreting diagnostic data.
Innovation Solution
A machine learning model-based system that automatically segments unstructured data into normalized datasets for predicting conditions and properties, using multiple stages of machine learning models to improve flexibility and precision, applicable to various types of data including audio, video, and sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual diagnostic processes are used, then diagnostic accuracy can be maintained through human expertise, but operational costs increase and efficiency decreases
Solution Approach 1:
The system enables automated self-diagnosis through machine learning models that independently analyze diagnostic data without requiring human intervention. The ML models process sensor data, images, and test results autonomously to detect conditions and predict outcomes, allowing the diagnostic system to serve itself rather than relying on human operators.
Solution Approach 2:
The patent replaces manual human diagnostic processes with automated machine learning-based systems. Human expertise is substituted by ML models trained on diverse diagnostic data, transforming the mechanical/manual diagnostic process into an automated computational system that maintains accuracy while improving efficiency.
2Reliability
If manual expert review is used, then diagnostic reliability is maintained, but time consumption increases
Solution Approach 1:
The system performs preliminary automated analysis using machine learning models to pre-process and evaluate diagnostic data before final interpretation. The ML models conduct initial condition detection, data normalization, and pattern recognition in advance, filtering and preparing data so that expert review focuses only on critical cases, thereby reducing overall diagnostic time while maintaining reliability.
Solution Approach 2:
The diagnostic system performs self-assessment through automated ML-based condition detection and prediction. The system independently evaluates diagnostic data, identifies abnormalities, and generates preliminary diagnoses without requiring immediate human intervention, thereby reducing diagnostic time while maintaining reliability through automated analysis.
3Productivity
If automated solutions are implemented, then operational costs decrease and efficiency improves, but the need for human expertise in the process is reduced
Solution Approach 1:
The machine learning models are designed to perform multiple diagnostic functions across different data types and medical domains. The same ML infrastructure can analyze sensor data, process medical images, interpret test results, and detect various conditions, making the automated system universally applicable rather than requiring specialized human expertise for each specific diagnostic task.
Data Source
AI summary
A system for performing machine language (ML) model based condition and property detection includes a computing platform having processing hardware and a system memory storing a software code that includes a trained ML model. The processing hardware is configured to execute the software code to receive a dataset, and perform an analysis of the dataset, using a first stage of the trained ML model, to detect a presence of a predetermined data attribute. The processing hardware is further configured to execute the software code to predict, using a second stage of the trained ML model when the analysis of the dataset detects the presence of the predetermined data attribute, a probability that the predetermined data attribute is indicative of a condition or a property.