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:
Sep. 03, 2024

Filed:

Jul. 30, 2021
Applicant:

Intuit Inc., Mountain View, CA (US);

Inventors:

Adi Shalev, Herzliya, IL;

Nitzan Gado, Hod Sharon, IL;

Talia Tron, Hod Hasharon, IL;

Alexander Zhicharevich, Petah Tikva, IL;

Assignee:

Intuit Inc., Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/907 (2019.01); G06F 9/30 (2018.01); G06F 18/214 (2023.01); G06F 40/00 (2020.01); G06F 40/126 (2020.01); G10L 25/30 (2013.01);
U.S. Cl.
CPC ...
G06F 9/30156 (2013.01); G06F 9/30036 (2013.01); G06F 16/907 (2019.01); G06F 18/2155 (2023.01); G06F 40/00 (2020.01); G06F 40/126 (2020.01); G10L 25/30 (2013.01);
Abstract

A method of score prediction uses hierarchical attention. Word features, positioning features, participant embedding features, and metadata are extracted from a transcript of a conversation. A word encoder vector is formed by multiplying weights of a word encoder layer to one or more word features. A sentence vector is formed by multiplying weights of a word attention layer to word encoder vectors. An utterance encoder vector is formed by multiplying weights of an utterance encoder layer to the sentence vector. A conversation vector is formed by multiplying weights of an utterance attention layer to utterance encoder vectors. The utterance encoder vector is combined with one or more positioning features and one or more participant embedding features. A predicted net promoter score is generated by multiplying weights of an output layer to the conversation vector combined with the metadata. The predicted net promoter score is presented in a list of conversations.


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