Fmr Corp.

Samsung Electronics Co., Ltd.

Rachna Saxena

Sundeep Krishnadasan

Hui Su

Punitha Chandrasekar

Vinay Iyengar

Mohamed Mahdi Alouane

Shyam Subramanian

Arindam Paul

Yogesh Somprakash Murarka

Angela Kontos

Santhosh Kolloju

Arijit Saha

Keerthan Ramnath

Aaditya Mathur

Pavan Mohan

Mohamed Asif Khan

Systems and methods for extracting data from electronic documents using optical character recognition (OCR) and non-OCR based text extraction. A server computing device initiates non-OCR based text extraction for each page of an electronic document. The server calculates a document text coverage percentage corresponding to the non-OCR based text extraction for the whole document and, in response to determining that the document text coverage percentage is below a first threshold, initiates OCR for the document. The server calculates a page text coverage percentage corresponding to the non-OCR based text extraction for one or more pages of the electronic document and, in response to determining that the page text coverage percentage is below a second threshold, initiates OCR for the pages. The server combines first text extracted from the electronic document using non-OCR based text extraction and second text extracted from the electronic document using OCR.

Systems and methods for automated end-to-end text extraction of electronic documents

Methods and apparatuses are described in which unstructured computer text is analyzed for generation of customized digital documents. A server tokenizes and encodes historical user interactions and historical digital documents into multidimensional vectors. The server trains an interaction classification model using the multidimensional vectors as input to generate a classification for an input user interaction, and trains a language generation model using the multidimensional vectors as input to generate a customized digital document based upon an input user interaction. The server receives a new user interaction and encodes the new user interaction into a new multidimensional vector. The server executes the trained interaction classification model using the new vector as input to generate a digital document classification. The server executes the trained language generation model using the new vector and the classification as input to generate a customized digital document.

Generating customized digital documents using artificial intelligence

A method and an apparatus that performs tile-based rendering. The method includes receiving a first drawcall used for rendering a first tile of a preceding frame; receiving a second drawcall used for rendering a second tile of a succeeding frame corresponding to a position of the first tile; generating a signature of the first drawcall and a signature of the second drawcall; comparing the signature of the first drawcall to the signature of the second drawcall. Based on a result of the comparison of the signature of the first drawcall and the signature of the second drawcall, rendering the second tile by using a result of rendering the first tile when a pre-set condition is satisfied.

Method and apparatus for performing tile-based rendering

Growing community of inventors

Bengaluru, India

Rachna Saxena