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:
Mar. 02, 2021

Filed:

Mar. 14, 2019
Applicant:

Tata Consultancy Services Limited, Mumbai, IN;

Inventors:

Lovekesh Vig, Gurgaon, IN;

Gautam Shroff, Gurgaon, IN;

Arindam Chowdhury, Gurgaon, IN;

Rohit Rahul, Gurgaon, IN;

Gunjan Sehgal, Gurgaon, IN;

Vishwanath Doreswamy, Gurgaon, IN;

Monika Sharma, Gurgaon, IN;

Ashwin Srinivasan, Sancoale, IN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/2452 (2019.01); G06F 16/2455 (2019.01); G06F 16/28 (2019.01); G06F 40/295 (2020.01); G06F 40/30 (2020.01); G06K 9/00 (2006.01); G06K 9/46 (2006.01); G06K 9/62 (2006.01); G06N 3/08 (2006.01); G06T 5/00 (2006.01);
U.S. Cl.
CPC ...
G06K 9/46 (2013.01); G06F 16/2455 (2019.01); G06F 16/24522 (2019.01); G06F 16/284 (2019.01); G06F 40/295 (2020.01); G06F 40/30 (2020.01); G06K 9/00442 (2013.01); G06K 9/6262 (2013.01); G06N 3/08 (2013.01); G06T 5/002 (2013.01); G06K 2209/01 (2013.01);
Abstract

Various methods are using SQL based data extraction for extracting relevant information from images. These are rule based methods of generating SQL-Query from NL, if any new English sentences are to be handled then manual intervention is required. Further becomes difficult for non-technical user. A system and method for extracting relevant from the images using a conversational interface and database querying have been provided. The system eliminates noisy effects, identifying the type of documents and detect various entities for diagrams. Further a schema is designed which allows an easy to understand abstraction of the entities detected by the deep vision models and the relationships between them. Relevant information and fields can then be extracted from the document by writing SQL queries on top of the relationship tables. A natural language based interface is added so that a non-technical user, specifying the queries in natural language, can fetch the information effortlessly.


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