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:
Oct. 18, 2022

Filed:

Jul. 30, 2019
Applicant:

Adobe Inc., San Jose, CA (US);

Inventors:

Niyati Himanshu Chhaya, Hyderabad, IN;

Pranav Ravindra Manerikar, Bangalore, IN;

Sopan Khosla, Bengaluru, IN;

Assignee:

Adobe Inc., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/30 (2020.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06F 40/205 (2020.01); G06F 40/253 (2020.01);
U.S. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/205 (2020.01); G06F 40/253 (2020.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01);
Abstract

Techniques are disclosed for generating an output sentence from an input sentence by replacing an input tone of the input sentence with a target tone. For example, an input sentence is parsed to separate semantic meaning of the input sentence from the tone of the input sentence. The input tone is indicative of one or more characteristics of the input sentence, such as politeness, formality, humor, anger, etc. in the input sentence, and thus, a measure of the input tone is a measure of such characteristics of the input sentence. An output sentence is generated based on the semantic meaning of the input sentence and a target tone, such that the output sentence and the input sentence have similar semantic meaning, and the output sentence has the target tone that is different from the input tone of the input sentence. In an example, a neural network for parsing the input sentence and/or generating the output sentence is trained using non-parallel corpora of training data that includes a plurality of input sentences and corresponding plurality of assigned tones.


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