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:
Jul. 11, 2023

Filed:

Jun. 28, 2017
Applicant:

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

Inventors:

Haim Somech, Herzliya, IL;

Shira Weinberg, Herzliya, IL;

Adi Miller, Herzliya, IL;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/00 (2020.01); G06F 40/103 (2020.01); G06F 40/30 (2020.01); H04L 67/14 (2022.01); H04L 51/043 (2022.01); H04L 67/306 (2022.01); G06Q 10/10 (2023.01); H04L 51/02 (2022.01); H04L 51/56 (2022.01); H04L 67/50 (2022.01); G06F 16/242 (2019.01); G06F 16/2455 (2019.01); G06F 16/2457 (2019.01); G06F 16/9535 (2019.01); G06F 16/248 (2019.01);
U.S. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 16/243 (2019.01); G06F 16/248 (2019.01); G06F 16/2455 (2019.01); G06F 16/24575 (2019.01); G06F 16/9535 (2019.01); G06Q 10/10 (2013.01); H04L 51/02 (2013.01); H04L 51/043 (2013.01); H04L 51/56 (2022.05); H04L 67/14 (2013.01); H04L 67/306 (2013.01); H04L 67/535 (2022.05);
Abstract

Methods for providing enhanced services to users participating in communication sessions (CS), via a virtual assistant, are disclosed. One method receives content that is exchanged by users participating in the CS. The content includes natural language expressions that encode a conversation carried out by users. The method determines content features based on natural language models. The content features indicate intended semantics of the natural language expressions. The method determines a relevance of the content and identifies portions of the content that are likely relevant to the user. Determining the relevance is based on the content features, a context of the CS, a user-interest model, and a content-relevance model of the natural language models. Identifying the likely relevant content is based on the determined relevance of the content and a relevance threshold. A summary of the CS is automatically generated from summarized versions of the likely relevant portions of the content.


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