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. 09, 2026

Filed:

May. 28, 2024
Applicant:

Dell Products L.p., Round Rock, TX (US);

Inventors:

Zijia Wang, London, GB;

Zhen Jia, Shanghai, CN;

Tianyu MA, Shanghai, CN;

Guangliang Lei, Shanghai, CN;

Li Xie, Shanghai, CN;

Yan Chen, Shanghai, CN;

Assignee:

Dell Products L.P., Round Rock, TX (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 10/00 (2026.01); G06F 11/3668 (2025.01); G06F 40/30 (2020.01); G06Q 10/04 (2023.01); G06Q 10/0631 (2023.01);
U.S. Cl.
CPC ...
G06Q 10/06311 (2013.01); G06F 11/3684 (2013.01); G06F 40/30 (2020.01); G06Q 10/04 (2013.01);
Abstract

A method in an illustrative embodiment includes determining, using a language model, a plurality of quantitative representations of a plurality of demands described in a natural language, and classifying the plurality of demands by clustering into a plurality of tasks based on the plurality of quantitative representations. The method further includes determining, using the language model, a quantitative representation of each task among the plurality of tasks and a quantitative representation of each agent among a plurality of agents, and assigning the plurality of tasks respectively to corresponding agents among the plurality of agents based on the quantitative representation of each task and the quantitative representation of each agent. In embodiments of the present disclosure, demand analysis and task assignment are performed on natural language demands by using a language model, which can achieve efficient matching between tasks and agents.


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