Dynamic Validation Code Generation for Compliance Review
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Solution Overview
Problem
Current compliance validation processes are manual, error-prone, and time-consuming, leading to potential non-compliance issues that can result in penalties and reputational damage due to the lack of efficient and accurate assessment methods.
Innovation Solution
An automated system generates dynamic validation code using machine learning models and natural language processing to parse compliance specifications, extract requirements, and create validation code that evaluates deliverables for compliance, providing rapid and accurate validation results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual compliance review processes are used, then compliance validation can be performed, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated computer-based system that uses natural language processing, machine learning models, and dynamic code generation to perform compliance validation. This substitution eliminates human error and dramatically reduces review time while maintaining or improving accuracy.
Solution Approach 2:
The system enables compliance validation to be performed automatically without requiring manual intervention. The computer-based system self-generates validation code, executes it against deliverables, and produces compliance assessments independently, freeing human reviewers from tedious manual tasks.
2Reliability
If manual compliance review processes are used, then compliance validation can be performed, but the process requires significant effort and resources
Solution Approach 1:
The patent replaces manual compliance review processes with an automated computer-based system that uses natural language processing, machine learning models, and dynamic code generation to perform compliance validation. This substitution eliminates human error and dramatically reduces review time while maintaining or improving accuracy.
Solution Approach 2:
The system dynamically changes parameters by generating validation code tailored to specific compliance specifications and deliverables. The machine learning models adapt to different compliance requirements by adjusting their analysis parameters and generating appropriate validation logic automatically.
3Productivity
If automated validation code generation is implemented, then compliance validation speed increases, but system complexity increases
Solution Approach 1:
The patent introduces natural language processing and machine learning models as intermediaries between compliance specifications and validation code generation. These intermediaries automatically translate regulatory requirements into executable validation logic, managing system complexity by handling the transformation process automatically rather than requiring direct manual coding.
Solution Approach 2:
The system performs preliminary actions by pre-processing compliance specifications to extract requirements and pre-generating validation code templates before actual compliance validation is needed. This preparation work is done in advance using machine learning models, reducing the complexity and time required during actual validation execution.
Data Source
AI summary
Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support automatic compliance validation using a dynamically generated set of validation code. The compliance validation process may begin by extracting requirements from a compliance specification. Once extracted, the requirements may be tokenized and vectorized to produce vectorized data. The vectorized data may be labeled using a multi-label classifier to produce a set of labeled data (e.g., labeled vectors representing the requirements extracted from the compliance specification). The set of labeled data may be fed to a machine learning model configured to map the labeled data to pieces of code stored in one or more code libraries. A set of validation code is generated based on the pieces of code mapped to the labeled data and the set of validation code may be applied to a deliverable to evaluate compliance of the deliverable with the requirements.