The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 17, 2025

Filed:

Feb. 28, 2022
Applicant:

Strong Force Vcn Portfolio 2019, Llc, Fort Lauderdale, FL (US);

Inventors:

Charles H. Cella, Pembroke, MA (US);

Brad Kell, Seattle, WA (US);

Teymour S. El-Tahry, Detroit, MI (US);

Andrew Cardno, San Diego, CA (US);

Leon Fortin, Jr., Providence, RI (US);

Assignee:

STRONG FORCE VCN PORTFOLIO 2019, LLC, Fort Lauderdale, FL (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 10/06 (2023.01); B25J 9/16 (2006.01); B29C 64/386 (2017.01); B29C 64/393 (2017.01); B33Y 10/00 (2015.01); B33Y 50/00 (2015.01); B33Y 50/02 (2015.01); G02B 3/14 (2006.01); G02B 26/00 (2006.01); G05B 13/02 (2006.01); G05B 13/04 (2006.01); G05B 17/02 (2006.01); G05B 19/402 (2006.01); G05B 19/4099 (2006.01); G05D 1/00 (2006.01); G06F 30/27 (2020.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06Q 10/0631 (2023.01); G06Q 10/0633 (2023.01); G06Q 10/0831 (2023.01); G06Q 10/0833 (2023.01); G06Q 10/087 (2023.01); G06Q 20/14 (2012.01); G06Q 30/0201 (2023.01); G06T 7/70 (2017.01); H04L 9/00 (2022.01); H04L 9/32 (2006.01); G06F 113/10 (2020.01);
U.S. Cl.
CPC ...
G05B 19/4099 (2013.01); B25J 9/161 (2013.01); B25J 9/163 (2013.01); B25J 9/1653 (2013.01); B25J 9/1661 (2013.01); B25J 9/1671 (2013.01); B25J 9/1682 (2013.01); B25J 9/1697 (2013.01); B29C 64/386 (2017.08); B29C 64/393 (2017.08); B33Y 10/00 (2014.12); B33Y 50/00 (2014.12); B33Y 50/02 (2014.12); G02B 3/14 (2013.01); G02B 26/00 (2013.01); G05B 13/0265 (2013.01); G05B 13/042 (2013.01); G05B 17/02 (2013.01); G05B 19/402 (2013.01); G05D 1/0027 (2013.01); G05D 1/0297 (2013.01); G06F 30/27 (2020.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06Q 10/06 (2013.01); G06Q 10/0631 (2013.01); G06Q 10/06311 (2013.01); G06Q 10/06313 (2013.01); G06Q 10/0633 (2013.01); G06Q 10/0831 (2013.01); G06Q 10/0833 (2013.01); G06Q 10/087 (2013.01); G06Q 20/14 (2013.01); G06Q 30/0201 (2013.01); G06T 7/70 (2017.01); H04L 9/3239 (2013.01); H04L 9/50 (2022.05); G05B 2219/32015 (2013.01); G05B 2219/49023 (2013.01); G06F 2113/10 (2020.01); G06T 2207/20081 (2013.01);
Abstract

A robot fleet management platform includes a job configuration system that determines tasks to be performed by robots of a robot fleet based on a job request and a first fleet objective. A proxy service applies fleet configuration services to the tasks to produce a data structure. An intelligence layer activates intelligence services to produce a robot task and associated contextual information that facilitates robot selection and task ordering. A job workflow system generates a workflow defining a performance order of the tasks. A workflow simulation system simulates performance of the job request based on the workflow to recursively redefine the tasks, the data structure, or the workflow until the simulation result satisfies a second fleet objective. In response to the simulation result satisfying the set of fleet objectives, a plan generator generates a job execution plan based on the set of robot tasks, the data structure, and the workflow.


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