Marlborough, MA, United States of America

Arunima Gautam

This inventor holds 3 USPTO granted patents. Top assignee: Quantiphi, Inc. Active years: 2025-2026.


% Patents Active = 66.7

Average Co-Inventor Count = 5.0

ph-index = 1


Company Filing History:


Years Active: 2025-2026

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3 patents (USPTO):Explore Patents

Title: Arunima Gautam: Innovator in Document Validation and Data Extraction

Introduction

Arunima Gautam is a notable inventor based in Marlborough, MA (US). He has made significant contributions to the fields of document validation and data extraction, holding 3 patents that showcase his innovative approach to technology.

Latest Patents

One of Arunima's latest patents is titled "Validation system and method for concurrent visual validation of two or more electronic documents." This validation system enables concurrent visual validation of multiple electronic documents. A processor generates a custom user interface (UI) framework comprising two sections. The first section displays document previews, while the second holds extracted data, including identifiers for document and page IDs, along with entity positions and associated values across the documents. Upon user input on a specific document entity in the second section, the validation system concurrently loads document previews in the first section, displaying corresponding data values from the multiple documents. The updated visualization allows for validation and review operations across the documents, utilizing the received user input and loaded information.

Another significant patent is "System and method for data extraction and standardization using AI based workflow automation." This invention discloses methods, systems, and computer program products for generating standardized structured data from unstructured and semi-structured images of document pages. The embodiments include a training framework where boundaries of one or more instances of a first and a second set-of-fields are detected from images of document pages and tagged using unique labels. Individual fields within the set-of-fields are identified and associated with each instance and the unique labels, to generate a large number of synthetically labelled documents. A neural network model is trained using the original document image and the large number of generated synthetically labelled documents. An inference framework receives as input scanned images of unstructured and semi-structured document pages. A custom object recognition module identifies different sets of fields, and an OCR module recognizes the text from the input images. The outputs of these modules are stitched together to create standardized structured data.

Career Highlights

Arunima Gautam is currently employed at Quantiphi, Inc., where he continues to develop innovative solutions in the realm of document processing and data management. His work has significantly impacted the efficiency and accuracy of data handling in various applications.

Collaborations

Arunima collaborates with talented professionals such as Bhaskar Kalita and Karthik Kumar Veldandi

Profile summary based on public USPTO records.
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