Ul LLC

Mahmood Tabaddor

Mario Xerri

Fan He

Anthony Tassone

John C Jones

Hai Jiang

Priyansu Panda

Pravinray D Gandhi

Scot Webster

Systems and methods for using machine learning to assess eligibility for product certification are disclosed. According to certain aspects, a server computer may train a set of machine learning models using a set of training data, where the set of machine learning models may be specific to products and certifications for the products. The server computer may access product specifications associated with a set of products sought to be certified, and may analyze the product specifications using the an appropriate machine learning model(s), the output of which may indicate whether the set of products is eligible for certification, the set of products is ineligible for certification, or the product specifications need further review.

Technologies for using machine learning to determine product certification eligibility

Systems and methods for using machine learning models to predict an outcome of a product test are described. According to certain aspects, an electronic device may generate a machine learning model using training data indicating a set of results of a set of products tested according to a specific product test. The electronic device may access a set of inputs comprising a set of additional results of at least one additional product tested according to a modified product test, and input the set of inputs into the machine learning model. Based on an output of the machine learning model, the electronic device may predict whether the at least one additional product would pass or comply with the original specific product test.

Using machine learning to virtualize product tests

A test apparatus for testing of a stop mechanism on a saw includes a support arm movable relative to a blade of the saw from a first position to a second position. The test apparatus also includes a simulation member coupled to the support arm. In the first position, the first end of the simulation member is offset from a portion of the blade of the saw, and in the second position, the first end of the simulation member is adapted to be in contact with the portion of the blade of the saw. The test apparatus further includes circuitry operatively connected to the simulation member to replicate an electrical property of a human finger such that contact between the blade and the first end of the simulation member triggers the stop mechanism of the saw.

Method and apparatus to test an automatic stop safety feature of a rotary saw

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Rochester, MI, United States of America

Mahmood Tabaddor