Automated Lab Instrument Rule Generation from Test Patterns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Laboratories face significant burdens in generating and implementing processing rules for automating decisions in biological sample testing workflows, which can be time-consuming and knowledge-intensive, leading to inconsistencies and inefficiencies.
Innovation Solution
A computer-implemented method and system that automatically generates processing rules using historical data and machine learning algorithms to derive patterns, reducing the need for manual rule creation and enhancing the accuracy and consistency of decision-making in biological sample analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual rule generation is used, then rules can be customized according to laboratory expertise, but it requires tremendous time and knowledge burden on laboratory staff
Solution Approach 1:
The system enables self-service by automatically generating processing rules using machine learning algorithms that analyze historical data and laboratory workflows. The automated rule generation system serves itself by learning from past data without requiring manual intervention, thereby reducing the time and knowledge burden on laboratory staff while maintaining rule quality through continuous learning and adaptation.
Solution Approach 2:
The patent replaces the manual mechanical process of rule creation with an automated computational system. Machine learning algorithms and data processing mechanisms substitute for human expertise in rule generation, transforming the manual knowledge-intensive task into an automated computational process that reduces time burden while preserving rule reliability through systematic analysis of historical data.
2Reliability
If numerous processing rules are established for automation, then decision-making accuracy improves, but the complexity of managing hundreds or thousands of rules increases
Solution Approach 1:
The system implements dynamic rule management where processing rules are continuously optimized based on performance feedback and changing laboratory conditions. The automated system adapts the ruleset over time, dynamically adjusting rule priorities, removing redundant rules, and optimizing the ruleset structure to maintain high decision-making accuracy while managing complexity through continuous adaptation rather than static accumulation.
Solution Approach 2:
The patent incorporates feedback mechanisms where the performance of processing rules is continuously monitored and evaluated. This feedback loop enables the system to identify and remove ineffective rules, optimize rule parameters, and maintain an efficient ruleset that achieves high decision-making accuracy without unnecessary complexity. The feedback-driven optimization ensures that only necessary rules are maintained.
3Reliability
If manual oversight is used for all samples, then decision accuracy is maintained, but laboratory staff burden increases significantly
Solution Approach 1:
The system applies partial automation where processing rules automatically verify only those samples that meet predefined criteria, while laboratory staff oversight is retained for samples that require additional review. This partial action approach maintains decision accuracy for routine samples through automated verification while concentrating human expertise on complex cases, thereby improving overall throughput without sacrificing reliability.
Solution Approach 2:
The automated processing rules enable samples to undergo self-verification through predefined criteria and algorithms. The system independently evaluates samples against established rules, automatically approving those that meet criteria without human intervention. This self-service capability increases productivity by handling routine samples autonomously while maintaining decision accuracy through systematic automated verification.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Disclosed herein are methods and systems for automatically generating processing rules to be used for automated decision-making when operating instruments to analyze and process biological samples (e.g., for the presence, absence, or concentration of analytes). For example, some automatically generated processing rules may set forth conditions and criteria in which some test results obtained from the biological samples can be automatically validated and sent out, while other test results are flagged for additional review. The processing rules can be generated based on patterns observed with the actions taken for historical test results associated with similar biological samples.