Test run time optimization achieved by artificial intelligence and intelligent test director

By optimizing test runtime through generative AI models and utilizing prerequisites for parallel operations, the problem of excessively long test times in existing technologies is solved, resulting in more efficient test throughput and reduced hardware changes.

CN122240457APending Publication Date: 2026-06-19TEKTRONIX INC
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
TEKTRONIX INC
Filing Date
2025-12-11
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing technologies have failed to effectively optimize test run times on testing equipment, resulting in excessively long test times on the production line and impacting throughput.

Method used

Generative AI models are used to read test specifications, generate test code, and provide a pre-request list. Test suites are optimized to reduce the number of tests and data analysis. Parallel operations are performed using prerequisites, and test environment configuration and data collection are optimized.

Benefits of technology

Significantly reduce test time, increase test throughput, reduce the number of hardware changes, and achieve faster test runs and higher test efficiency.

✦ Generated by Eureka AI based on patent content.

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Abstract

A test and measurement system includes test and measurement instruments with one or more ports for connecting to one or more devices under test (DUTs), a generative AI model, a user interface allowing users to provide input to the system, and one or more processors for receiving user input identifying test requirement specifications, providing test requirement specifications to the generative AI model, receiving a set of prerequisites from the generative AI model, using the set of prerequisites to generate a sequence of tests for the DUT to minimize hardware changes for testing and minimize data collection instances from the DUT, executing the set of tests in sequence to collect data from the DUT, and analyzing the data collected from the DUT to determine whether the DUT has passed one or more tests.
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