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:
Apr. 18, 2023

Filed:

Jun. 02, 2021
Applicant:

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

Inventors:

Royi Ronen, Tel Aviv, IL;

Yarin Kuper, Tel Aviv, IL;

Tomer Rosenthal, Kirkland, WA (US);

Abedelkader Asi, Kfar Bara, IL;

Erez Altus, Tel Aviv, IL;

Rona Shaanan, Tel Aviv, IL;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/166 (2020.01); G06F 40/117 (2020.01); G06F 40/284 (2020.01); G06N 20/00 (2019.01); G10L 15/26 (2006.01); G10L 15/22 (2006.01); G06F 16/34 (2019.01); G06F 40/279 (2020.01);
U.S. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 16/345 (2019.01); G06F 40/117 (2020.01); G06F 40/166 (2020.01); G06F 40/279 (2020.01); G06F 40/284 (2020.01); G06N 20/00 (2019.01); G10L 15/22 (2013.01); G10L 15/26 (2013.01);
Abstract

The disclosure herein describes determining topics of communication transcripts using trained summarization models. A first communication transcript associated with a first communication is obtained and divided into a first set of communication segments. A first set of topic descriptions is generated based on the first set of communication segments by analyzing each communication segment of the first set of communication segments with a generative language model. A summarization model is trained using the first set of communication segments and associated first set of topic descriptions as training data. The trained summarization model is then applied to a second communication transcript and, based on applying the trained summarization model to the second communication transcript, a second set of topic descriptions of the second communication transcript is generated. By training the summarization model based on output of the generative language model, it enables efficient, accurate generation of topic descriptions from communication transcripts.


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