LLM Data Analysis Interface for Test Engineering Workflows
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Solution Overview
Problem
Test engineers face high overhead costs in time, training, and expertise due to the need to interact with multiple disparate software systems for various roles in developing a test process for a device under test (DUT), leading to extended time to market for products.
Innovation Solution
A generative AI-based system, utilizing a large language model (LLM), allows end users to interact via a chat-style interface to analyze measurement data, generating plots, figures, and tables, and suggesting additional analysis, reducing the need for multiple software systems and specialized expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple disparate software systems are used to support various test engineer roles, then comprehensive test process development capability is achieved, but overhead costs in time, training, and expertise increase significantly
Solution Approach 1:
The patent combines multiple disparate software systems into a single integrated AI assistant that provides comprehensive test process development capabilities. The AI assistant consolidates functions previously requiring separate tools for test design, validation, and production testing into one unified system, eliminating the need for engineers to switch between multiple applications and reducing training requirements.
Solution Approach 2:
The AI assistant is designed as a universal tool that performs multiple test engineering functions across different roles and phases. It can assist design engineers, validation engineers, and production test engineers with a single interface, providing adaptive support for test specification, test case generation, data analysis, and report creation without requiring role-specific software.
2Reliability
If multiple specialized tools are leveraged for different test engineering roles, then comprehensive test coverage is achieved, but time to market extends due to high overhead costs
Solution Approach 1:
The patent merges multiple specialized test engineering tools into a single AI-powered platform that maintains comprehensive test coverage. By integrating test design, validation, and production testing capabilities into one system, the patent eliminates the time losses associated with switching between tools while preserving the thoroughness of multi-role test engineering approaches.
Solution Approach 2:
The AI assistant autonomously performs complex test engineering tasks without requiring engineers to manually operate multiple specialized tools. It automatically generates test cases, analyzes test data, creates reports, and provides recommendations, enabling engineers to focus on high-level decision-making rather than tedious manual operations across different software systems.
3Ease of operation
If various tools providing high-level test support and test sequence generation are used, then test development capability is enhanced, but the need for multiple software systems increases complexity
Solution Approach 1:
The patent combines multiple test development tools into a single AI assistant interface. Functions for high-level test support, test sequence generation, and detailed test case creation are integrated into one unified system, reducing the complexity of managing multiple disparate software applications while maintaining enhanced test development capabilities.
Data Source
AI summary
Apparatuses, systems, and methods for artificial intelligence (AI) augmented data analysis can include providing a large language model (LLM), access to a data set. For example, an end user can interact with a user interface on a user device to provide instructions to the LLM to access the data set. In addition, the LLM can be interacted with via the user interface using natural language instructions and LLM output, based on the LLM analysis of the data set, can also be interacted with via the user interface. The LLM output can include one or more visualizations of the data set.