Compliance Analysis System for Chromatography Data Correlation
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Solution Overview
Problem
Analyzing chromatography data across an enterprise or supply chain to identify compliance risks is challenging, especially when working with outside partners, as it requires access to third-party data and practices, which may not be available or reliable.
Innovation Solution
A computer-implemented method that utilizes visualization and advanced data science techniques to analyze chromatography data and metadata, identifying correlations and patterns that indicate potential user errors and compliance risks. This method combines metadata from analytical systems with other data sources, employing supervised and unsupervised machine learning techniques to recognize compliance issues and generate notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If third-party chromatography data is accessed for compliance analysis, then compliance risk identification capability is improved, but data availability and access reliability deteriorate
Solution Approach 1:
The patent introduces an intermediary compliance analysis system that receives and processes chromatography data from multiple sources including third parties. This intermediary system standardizes data formats, handles access protocols, and performs unified compliance analysis, thereby maintaining measurement precision while managing data availability challenges through centralized coordination
Solution Approach 2:
The compliance analysis system is segmented into modular components: data acquisition modules that handle different third-party sources independently, data processing modules that standardize various data formats, and analysis modules that perform compliance checks. This segmentation allows the system to work with partial data from individual sources while maintaining overall compliance identification capability
2Adaptability or versatility
If comprehensive chromatography data is collected across enterprise and supply chain, then compliance monitoring coverage is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal compliance analysis system that can process multiple types of chromatography data (LC, GC, LC-MS, GC-MS) from diverse sources (internal laboratories, third-party suppliers, different geographic locations) through a single integrated platform. The system uses standardized data interfaces and unified analysis algorithms, enabling broad compliance monitoring coverage without proportionally increasing system complexity
Solution Approach 2:
The system dynamically adjusts analysis parameters and compliance criteria based on data source characteristics, instrument types, and regulatory requirements. By automatically modifying processing parameters rather than requiring separate systems for each scenario, the patent achieves high adaptability while managing complexity through parameterization rather than structural multiplication
3Adaptability or versatility
If manual compliance analysis is performed, then flexibility in handling diverse data formats is improved, but analysis time and resource consumption increase
Solution Approach 1:
The patent implements preliminary automated actions including automatic data format detection, pre-processing of chromatography data, and preliminary compliance rule application before full analysis. This preliminary automation handles routine format conversions and basic compliance checks, reducing the time required for manual intervention while maintaining flexibility for complex cases
Solution Approach 2:
The system incorporates feedback mechanisms where automated analysis results are reviewed and refined through iterative processes. The feedback loop allows the system to learn from manual analysis corrections and improve automated format handling over time, progressively reducing analysis time while maintaining adaptability through continuous refinement of processing algorithms
Data Source
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AI summary
Exemplary embodiments provide methods, mediums, and systems for visualization and advanced data science on information collected in an analytical data system. Embodiments identify correlations and patterns in chromatography metadata around areas of potential user error. Correlations between these data sources may point to compliance risk areas. Metadata from the analytical system may be combined with other data sources and/or analytical data to correlate an analytical outcome with compliance artifacts. Supervised and/or unsupervised machine learning techniques may be used to combine these data source and learn correlations between them and compliance risks. The results of these analyses may be displayed on a dashboard, allowing a user to visualize compliance risks across an entire enterprise or supply chain. Automatic notifications of compliance risks may be generated and presented on a user interface. A system may also use pattern recognition to provide insights around potential compliance risks that have not yet occurred.