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. 07, 2022

Filed:

Dec. 06, 2019
Applicant:

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

Inventors:

Pamela Bhattacharya, Redmond, WA (US);

Barun Patra, Seattle, WA (US);

Charles Yin-Che Lee, Mercer Island, WA (US);

Vishwas Suryanarayanan, Los Angeles, CA (US);

Chala Fekadu Fufa, Seattle, WA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 15/16 (2006.01); G06F 40/205 (2020.01); G06N 3/04 (2006.01); G06Q 10/10 (2012.01); H04L 51/04 (2022.01); H04L 51/02 (2022.01);
U.S. Cl.
CPC ...
G06F 40/205 (2020.01); G06N 3/049 (2013.01); G06Q 10/107 (2013.01); H04L 51/02 (2013.01); H04L 51/04 (2013.01);
Abstract

In non-limiting examples of the present disclosure, systems, methods and devices for identifying relevant content in a natural language input are presented. An email may be received. A machine learning model may be applied to the email. The machine learning model may have been trained to rank sentences based on their relevance to a schedule meeting task. The machine learning model may comprise: an embedding layer for generating an embedding for each word in the email; a distinct sentence aggregation layer for aggregating the embeddings for each word in the email into a distinct embedding for each of the sentences in the email; a contextual aggregation layer for aggregating each distinct embedding for each of the sentences into a contextual embedding for each of the sentences; and a scoring layer for scoring and ranking each of the sentences based on their relevance to the schedule meeting task.


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