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. 02, 2026

Filed:

Jun. 30, 2023
Applicant:

Microsoft Technology Licensing, Llc, Redmond, WA (US);

Inventors:

Xavier Amatriain-Rubio, Los Gatos, CA (US);

Christopher M. Bremer, Santa Barbara, CA (US);

Carlos H. Lopez, Westfield, NJ (US);

Pierre Y. Monestie, Half Moon Bay, CA (US);

Laura Teclemariam, Hayward, CA (US);

Yamini Kasera, San Francisco, CA (US);

Michaeel Kazi, Foster City, CA (US);

Zhoutong Fu, Milpitas, CA (US);

Muchen Wu, Mountain View, CA (US);

Winnie Narang, San Carlos, CA (US);

Yiyuan Tu, Milpitas, CA (US);

Jaime Munoz Alcalde, Brooklyn, NY (US);

Nitin Pasumarthy, Sunnyvale, CA (US);

Thao Bach, Mountain View, CA (US);

David Williams, Seattle, WA (US);

Priyanka Gariba, San Francisco, CA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/35 (2020.01); G06F 9/445 (2018.01); G06Q 10/0631 (2023.01); G06Q 10/1053 (2023.01);
U.S. Cl.
CPC ...
G06F 40/35 (2020.01); G06F 9/445 (2013.01); G06Q 10/063112 (2013.01); G06Q 10/1053 (2013.01);
Abstract

Embodiments of the disclosed technologies include generating a first thread classification prompt based on a first thread portion of an online dialog involving a user of a computing device, sending the first thread classification prompt to a first large language model, receiving a first thread classification generated and output by the first large language model based on the first thread classification prompt, formulating a plan execution prompt based on the first thread classification, sending the plan execution prompt to a second large language model, receiving a second thread portion generated and output by the second large language model based on the plan execution prompt and the online dialog, and generating a label for a third thread portion of the online dialog.


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