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. 19, 2021

Filed:

Oct. 25, 2017
Applicant:

Tata Consultancy Services Limited, Mumbai, IN;

Inventors:

Robin Tommy, Trivandrum, IN;

Sarath Sivaprasad, Trivandrum, IN;

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 16/242 (2019.01); G06F 16/245 (2019.01); G06F 16/30 (2019.01); G06F 16/93 (2019.01); G06F 40/30 (2020.01); G06F 40/232 (2020.01); G06F 16/33 (2019.01); G06F 16/2455 (2019.01); G06F 16/2453 (2019.01); G06F 16/35 (2019.01); G06F 16/2457 (2019.01); G06N 3/02 (2006.01);
U.S. Cl.
CPC ...
G06F 16/243 (2019.01); G06F 16/245 (2019.01); G06F 16/2433 (2019.01); G06F 16/2448 (2019.01); G06F 16/2454 (2019.01); G06F 16/2455 (2019.01); G06F 16/30 (2019.01); G06F 16/3334 (2019.01); G06F 16/35 (2019.01); G06F 16/93 (2019.01); G06F 40/232 (2020.01); G06F 40/30 (2020.01); G06F 16/24575 (2019.01); G06N 3/02 (2013.01);
Abstract

Systems and methods for assessing quality of input text using recurrent neural networks is disclosed. The system obtains input text from user and performs a comparison of each word from input text with words from dictionary (or trained data) to determine a closest recommended word for each word in the input text. The input text is further analyzed to determine context of each word based on at least a portion of input text, and based on determined context, at least one of correct sentences, incorrect sentences, and/or complex sentences are determined from the input text. Each word is converted to a vector based on concept(s) by comparing each word across sentences of input text to generate vectors set, and quality of the input text is assessed based on vectors set, the comparison, determined context and at least one of correct sentences, incorrect sentences, complex sentences, or combinations thereof.


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