Robotics & Machine Learning Daily News2024,Issue(Jun.18) :127-130.

'Systems And Methods For Efficiently Exchanging End Effector Tools' in Patent Ap plication Approval Process (USPTO 20240173872)

专利申请批准过程中的“有效交换末端执行器工具的系统和方法”(USPTO 20240173872)

Robotics & Machine Learning Daily News2024,Issue(Jun.18) :127-130.

'Systems And Methods For Efficiently Exchanging End Effector Tools' in Patent Ap plication Approval Process (USPTO 20240173872)

专利申请批准过程中的“有效交换末端执行器工具的系统和方法”(USPTO 20240173872)

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摘要

由一名新闻记者-机器人和机器学习每日新闻的工作人员新闻编辑-发明者艾伦,托马斯(雷丁,马,美国);小修正,约翰理查德(贝尔蒙特,马,美国);法尔默,威廉(波顿,马,美国);高蒂埃,安德鲁(萨默维尔,马,美国);欣奇,V ictoria(温切斯特,马,韦克利,美国);马,塞特乌斯,马,根据NewsRx记者从华盛顿特区开始的新闻报道,Sa Muel(Medford,MA,US);WAGNER,Thomas(Concord,MA,US)于2023年10月30日提交,2024年5月30日在线提供。本专利申请未转让给公司或机构。以下引文由新闻编辑从发明人提供的背景信息中获得:“本发明一般涉及可编程运动系统,并且特别涉及用于诸如物体运动的物体处理的可编程运动装置(例如机器人系统)的末端执行器。”

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A patent application by the inventors ALLEN, Thomas (Reading, MA, US); AMEND, JR., John Richard (Belmont, MA, US); FAR MER, William (Bolton, MA, US); GAUTHIER, Andrew (Somerville, MA, US); HINCHEY, V ictoria (Winchester, MA, US); MARONEY, Kyle (North Attleboro, MA, US); MASON, Ma tthew T. (Pittsburgh, PA, US); MUSGRAVE, Richard (Sewickley, PA, US); NASEEF, Sa muel (Medford, MA, US); WAGNER, Thomas (Concord, MA, US), filed on October 30, 2 023, was made available online on May 30, 2024, according to news reporting orig inating 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: "The invention generally relates to programmable motion systems and relates in particular to end effectors for programmable moti on devices (e.g., robotic systems) for use in object processing such as object s ortation.

Key words

Emerging Technologies/Machine Learning/Patent Application/Robotics/Robots

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
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