Object-Oriented Data Synchronization for Validated Quality Systems
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Solution Overview
Problem
Traditional software development and quality management systems lack the flexibility and integration needed to seamlessly connect various aspects of software development, leading to silos of information and challenges in ensuring regulatory compliance, particularly in highly regulated industries like healthcare and pharmaceuticals, where the dynamic nature of software changes demands robust control mechanisms to manage risk and ensure reliability.
Innovation Solution
A computer-implemented method that integrates data from multiple remote systems into an object-oriented data model, utilizing parsers, mapping, and synchronization to manage quality procedures and change management, leveraging AI for automated compliance checks and traceability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional document-centric approaches are used for quality management, then ease of operation is maintained through simple documentation, but information integration and traceability are lost due to siloed data storage
Solution Approach 1:
The patent merges data from multiple remote systems (requirements, specifications, tests, risks, complaints, change requests) into a single centralized database with a unified data model. This consolidation eliminates information silos and enables comprehensive traceability across the entire software development lifecycle while maintaining manageable system complexity through standardized data structures.
Solution Approach 2:
The centralized database serves multiple functions simultaneously: storing requirements, specifications, test cases, risk assessments, complaint data, and change requests. It provides a universal platform that supports traceability, compliance tracking, quality analysis, and change management, replacing multiple separate documentation systems with one multi-functional solution.
2Reliability
If ad hoc processes are used for quality control, then flexibility in documentation is maintained, but compliance assurance and granularity are insufficient for regulated industries
Solution Approach 1:
The system establishes predetermined quality procedures, validation rules, and compliance criteria before software development begins. These pre-defined controls include mandatory traceability relationships, required documentation standards, and automated validation checks that ensure compliance is built into the process from the start rather than added later through ad hoc measures.
Solution Approach 2:
The system implements automated feedback mechanisms that continuously monitor compliance status, traceability completeness, and quality metrics. The centralized database enables real-time tracking of compliance requirements and automatically generates reports, providing continuous feedback on the system's adherence to regulatory standards without requiring complex manual auditing processes.
3Manufacturing precision
If extensive documentation is used to detail requirements and testing protocols, then completeness of quality procedures is achieved, but efficiency of software development cycles is reduced
Solution Approach 1:
The patent replaces manual documentation processes with automated computational systems. The centralized database automatically captures, stores, and links quality procedure data from multiple sources, eliminating the need for extensive manual documentation creation and management. This substitution maintains complete quality procedure records while dramatically improving development cycle efficiency through automation.
Solution Approach 2:
The system creates structured digital copies of quality procedure data from various remote systems and stores them in a standardized format in the centralized database. These digital copies enable efficient retrieval, analysis, and traceability without requiring the creation of extensive separate documentation, thereby maintaining completeness while improving productivity.
4Adaptability or versatility
If multiple remote systems are used for different development aspects, then specialization and functionality are improved, but data synchronization and holistic view are compromised
Solution Approach 1:
The centralized database acts as an intermediary between multiple specialized remote systems (requirements management, specification tools, test management, risk assessment systems, complaint tracking, change management). It receives data from these diverse systems, standardizes the information according to a unified data model, and maintains synchronized relationships, enabling a holistic view while preserving the functionality of each specialized system.
Data Source
AI summary
A computer-implemented method integrates data from systems into a model to manage quality, generating artifacts. A computing system receives data for quality management, creating artifacts. A computer-readable medium stores instructions for data integration and artifact generation.