Statistical Causal Model for Biological Sample Error Diagnosis
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Solution Overview
Problem
In laboratory settings, identifying the source of errors in biological sample processing workflows is challenging due to the complexity of the workflows, which involves multiple equipment and reagents, leading to time-consuming manual investigations and resource wastage in diagnosing and resolving issues.
Innovation Solution
A method and system using a computer hardware processor to obtain data on biological samples, determine quality metrics, and apply statistical models representing causal relationships among physical components and workflow processes to identify sources of error, enabling precise diagnosis of error sources within the sample processing workflow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual investigation methods are used to identify error sources in complex workflows, then flexibility and adaptability are maintained, but time consumption and resource wastage increase significantly
Solution Approach 1:
The patent replaces manual mechanical investigation with an automated computational system that uses statistical models and causal inference algorithms to automatically identify error sources. The system processes workflow data, applies probabilistic models to determine causal relationships, and outputs diagnostic results without human intervention, thereby reducing time loss while handling complexity.
Solution Approach 2:
The patent creates a virtual replica of the physical workflow system through computational models. This digital copy includes all workflow steps, physical components, and their interrelationships, allowing automated analysis and simulation of error scenarios without affecting the actual laboratory operations, thus enabling rapid diagnosis without time loss.
2Measurement precision
If comprehensive monitoring of all physical components and workflow processes is implemented, then measurement precision of error sources improves, but device complexity and implementation cost increase
Solution Approach 1:
The patent applies local quality by focusing monitoring and analysis resources on specific critical components and workflow steps identified through statistical models, rather than uniformly monitoring everything. The system dynamically determines which components require attention based on their contribution to error probability, optimizing precision while managing complexity through targeted rather than comprehensive monitoring.
Solution Approach 2:
The patent performs preliminary actions by pre-establishing statistical models and causal relationship frameworks before errors occur. These pre-configured models enable rapid analysis when errors happen, avoiding the need for complex real-time monitoring of all components while still achieving high measurement precision through pre-prepared analytical structures.
3Productivity
If statistical models representing causal relationships are applied to identify error sources, then productivity of sample processing increases, but difficulty of detecting and measuring causal relationships increases
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors workflow outcomes, compares them against predicted results from causal models, and uses this feedback to refine and update the statistical models. This iterative process enables the system to detect and adapt to changing causal relationships in real-time, maintaining high productivity while managing the complexity of causal detection through continuous learning.
Data Source
AI summary
Techniques for identifying sources of error occurring during processing of biological samples in a laboratory environment. The techniques may include obtaining data about the biological samples, where the data is generated by processing the biological samples in accordance with a sample processing workflow. The sample processing workflow is performed using physical component(s) and/or workflow process(es). The techniques further include determining values of quality metric(s) associated with the sample processing workflow for the biological samples, identifying source(s) of error for the data by using the values of the quality metric(s) and statistical model representing causal relationships among the physical component(s) and the workflow process(es), and outputting information indicative of the identified source(s) of error.