Machine-Learning Compliance Testing for Regulation Matching
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Solution Overview
Problem
The increasing diversity of electronic devices and communication standards makes it difficult to select appropriate compliance tests, requiring highly skilled personnel for testing electronic devices to ensure adherence to regulations and standards.
Innovation Solution
A compliance testing system utilizing a machine-learning circuit that processes user input to determine applicable regulations and standards, automatically selecting compliance tests and generating test data, with optional user queries for missing information and feedback mechanisms to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If highly skilled personnel are used to select compliance tests, then testing accuracy is improved, but operational cost and time consumption increase
Solution Approach 1:
The system performs preliminary actions by pre-processing regulation documents, extracting keywords, and building a knowledge base before actual compliance testing. This preparation work is done in advance so that when a device needs compliance testing, the system can quickly match device characteristics with relevant regulations using the pre-built knowledge base, avoiding time-consuming manual analysis.
Solution Approach 2:
The system introduces an intermediary component - a machine learning model that acts as a mediator between the complex regulation documents and the compliance testing process. This intermediary automatically extracts relevant information, matches it with device characteristics, and generates compliance test selections, replacing the need for highly skilled personnel to manually perform this matching task.
2Adaptability or versatility
If manual selection of compliance tests is performed, then adaptability to complex regulations is improved, but ease of operation deteriorates
Solution Approach 1:
The system enables self-service by allowing users to input basic device characteristics through a simple interface, and the system automatically handles the complex task of matching these characteristics with relevant compliance regulations and standards. The machine learning model autonomously processes the matching without requiring users to have specialized knowledge of compliance regulations.
Solution Approach 2:
The system replaces the mechanical process of manual compliance test selection with an automated information processing system. Instead of skilled personnel manually reading and interpreting regulation documents, the system uses natural language processing and machine learning algorithms to automatically extract relevant requirements and match them with device characteristics, substituting human expertise with automated computational processes.
3Measurement precision
If comprehensive compliance testing data is stored, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system extracts only the essential and relevant information from comprehensive compliance testing data and regulation documents. Instead of storing and processing all available compliance data, the system identifies and extracts key parameters, keywords, and requirements that are actually needed for matching with device characteristics, reducing the complexity of data management while maintaining testing accuracy.
Solution Approach 2:
The system segments the comprehensive compliance testing data into structured components including device characteristics, regulation requirements, test parameters, and compliance criteria. This segmentation organizes the complex data into manageable modules that can be independently processed and matched, reducing system complexity while preserving the comprehensive nature of the compliance testing capability.
Data Source
AI summary
A compliance testing system includes a user interface, a machine-learning circuit, and a database. The database includes compliance testing data including information on regulations and standards for performing compliance tests on different devices under test (DUT). The user interface is configured to receive a user input including text and/or speech relating to the DUT to be tested. The machine-learning circuit is configured to determine which regulations and standards are applicable to the DUT to be tested based on the user input. The machine-learning circuit is configured to generate a set of rules describing the application of the applicable regulations and standards to the DUT that is to be tested. The machine-learning circuit is configured to generate test data based on the generated set of rules. The test data may include information on compliance tests to be performed on the DUT in view of the applicable regulations and standards.