AI Waveform Assistant for Synthetic Test Signal Generation
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Solution Overview
Problem
The lack of large data sets for emerging standards and hardware unavailability poses challenges in machine learning environments for test and measurement instruments, particularly in creating waveforms for devices under test.
Innovation Solution
Employing generative AI models, such as large language models, with retrieval augmentation generation and vector databases to create domain-specific waveforms using APIs that interact with test and measurement instruments, allowing users to define parameters and generate unique waveforms based on statistical definitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If machine learning systems use existing data sets for testing, then testing can be performed, but the lack of large data sets for emerging standards and hardware limits the ability to create comprehensive test waveforms
Solution Approach 1:
The patent uses generative AI models to create synthetic waveform data that copies and mimics real-world signal characteristics. The AI models generate realistic test waveforms by learning patterns from existing data and standards documentation, enabling comprehensive test set creation without requiring physical access to emerging hardware platforms.
Solution Approach 2:
The patent introduces AI models as intermediary systems between test engineers and emerging hardware. The AI acts as a mediator that translates standard specifications and existing waveform patterns into synthetic test data, bridging the gap between theoretical standards and practical testing capabilities when direct hardware access is unavailable.
2Measurement precision
If physical access to devices under test is required for waveform creation, then accurate real-world signal data can be obtained, but hardware unavailability prevents testing of emerging standards
Solution Approach 1:
The generative AI models create synthetic waveform copies that replicate the statistical and temporal characteristics of real signals. These synthetic waveforms maintain measurement precision by preserving signal patterns, impedance characteristics, and error profiles, while eliminating the need for physical device access.
Solution Approach 2:
The system performs preliminary waveform generation using AI models before physical testing is required. By pre-generating comprehensive test waveforms from standard specifications and existing data patterns, the system prepares test data in advance, enabling rapid deployment of tests even when hardware becomes unavailable or inaccessible.
3Productivity
If traditional waveform generation methods are used, then existing hardware can be tested, but the process is time-consuming and cannot keep pace with rapid standard evolution
Solution Approach 1:
The patent replaces traditional mechanical/manual waveform generation processes with AI-based automated systems. Instead of manually programming waveforms or using conventional signal generators, the system uses generative AI models that automatically create test waveforms by processing standard documentation and existing data patterns, dramatically increasing generation speed.
Solution Approach 2:
The AI models dynamically adjust waveform parameters such as signal amplitude, frequency, timing, and error characteristics based on changing standard requirements. This parameter transformation capability allows the system to rapidly adapt test waveforms to emerging standards without requiring physical hardware changes or manual reconfiguration.
Data Source
AI summary
A computing device includes one or more memories including test and measurement knowledge, a generative artificial intelligence (AI) model having access to the one or more memories, a display, user controls to allow the user to provide inputs, and one or more processors configured to execute that code that causes the one or more processors to: access an application programming interface (API) of an AI assistant for the generative AI model to allow the user to interact with the AI assistant, receive one or more user inputs through one or more of the API or a user interface, the user inputs providing a description of one or more waveforms to be generated, use the AI assistant to develop each waveform definition from the description and access the generative AI model, receive one or more waveforms from the AI assistant, and store the one or more waveforms.