Robotics & Machine Learning Daily News2024,Issue(Apr.1) :138-141.

"Machine Learning enabled self-detection for truck rolls on enduser Satellite T erminals" in Patent Application Approval Process (USPTO 20240088994)

Robotics & Machine Learning Daily News2024,Issue(Apr.1) :138-141.

"Machine Learning enabled self-detection for truck rolls on enduser Satellite T erminals" in Patent Application Approval Process (USPTO 20240088994)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A patent application by the inventors ARORA, Amit (Clarksburg, MD, US); SHETH, Soham (Gaithersburg, MD, US); WHITEFIEL D, David (Germantown, MD, US), filed on September 14, 2022, was made available o nline on March 14, 2024, according to news reporting originating from Washington , D.C., by NewsRx correspondents. This patent application has not been assigned to a company or institution. The following quote was obtained by the news editors from the background informa tion supplied by the inventors: "End-user satellite terminals, part of a larger satellite ground system, reside at the customer site. When the network provider' s customer care gets called for support, there is often no practical way for cus tomer care to reach the remote Terminal such as due to its current issue to dete rmine whether there is a need for a service technician to travel to the customer site to diagnose the issue with the terminal (referred to as a "truck roll"). T his results in costly unnecessary/incorrect truck rolls. Moreover, the response can be delayed based on the availability of technicians to visit the site. "There are some manual methods devised to filter or categorize if terminals need a truck roll, but a manual approach is not scalable. Often the issue is a short -term transient issue that resolves itself automatically before the service tech nician arrives to resolve the issue when the visit is not cancelled in time."

Key words

Cyborgs/Emerging Technologies/Machine Learning/Patent Application

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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