Certification Argument Automation for Complex Compliance Data
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Solution Overview
Problem
Traditional methods for managing product certification are inadequate due to their complexity, error-proneness, and inefficiency in handling large volumes of interdependent certification data, leading to substantial delays and increased costs.
Innovation Solution
A computer-implemented method and system that structures and analyzes certification data using AI processors to identify relationships, generate certification arguments, and automate the certification process, incorporating classifiers and knowledge graphs to link certification goals, evidence, and rationale.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional document-centric methods are used for managing certification, then certification processes can be performed manually, but the process becomes error-prone, time-consuming, and difficult to reproduce
Solution Approach 1:
The patent replaces manual document-centric certification processes with an automated computer-based system that uses processors to execute certification rules, analyze product data, and generate certification decisions. This substitution eliminates human error and cognitive biases while maintaining operational capability through automated workflows.
Solution Approach 2:
The certification system enables self-service by allowing the automated processor to independently evaluate product data against certification rules without requiring manual intervention for each certification decision. The system autonomously tracks dependencies, retrieves relevant documents, and determines certification outcomes.
2Adaptability or versatility
If manual certification management is used, then flexibility in handling complex interdependencies is maintained, but productivity and speed of certification are severely reduced
Solution Approach 1:
The patent segments the complex certification process into distinct modular components including certification rules, product data elements, dependency relationships, and evaluation logic. This segmentation allows the automated system to efficiently process each component independently while maintaining the ability to handle complex interdependencies through structured data relationships.
Solution Approach 2:
The system transforms unstructured certification data into structured parameters that can be automatically processed. By converting product specifications, test results, and regulatory requirements into standardized data parameters, the system enables rapid automated evaluation while preserving the complexity of interdependencies through parameter relationships.
3Reliability
If comprehensive certification tracking is implemented to ensure accuracy, then reliability of certification is improved, but the complexity of the certification management system increases
Solution Approach 1:
The patent implements a universal certification management system that handles multiple certification types, product categories, and regulatory frameworks through a single integrated platform. The system uses standardized data structures and rule templates that can be configured for different certification domains, reducing overall system complexity while maintaining comprehensive tracking capabilities.
Solution Approach 2:
The system introduces structured data models and intermediate processing layers that mediate between raw product data and certification decisions. These intermediaries organize complex information into manageable representations, enabling accurate tracking without proportionally increasing system complexity.
4Reliability
If extensive manual review processes are used to ensure compliance, then certification accuracy is maintained, but time and costs of certification efforts increase substantially
Solution Approach 1:
The patent performs preliminary automated evaluation of product data against certification rules before human review. The system pre-identifies compliance issues, retrieves relevant documentation, and prepares certification recommendations in advance, reducing the time required for manual review while maintaining compliance accuracy through structured preliminary analysis.
Data Source
AI summary
A method includes creating a certification argument with one or more certification goals; linking the one or more certification goals to one or more evidences that support the one or more certification goals; linking the one or more evidences to one or more rationales that demonstrate a sufficiency of the one or more evidences; linking one or more contexts, for the one or more certification goals, to the certification argument; determining one or more relationships between the one or more certification goals, the one or more evidences, the one or more rationales, and the one or more contexts; and evaluating a certification effort with the certification argument where at least one of creating the certification argument, linking the one or more certification goals, linking the one or more evidences, linking the one or more contexts, determining the one or more relationships, and evaluating the certification argument by an automated processor.