End-to-end identification of erroneous data using machine learning and similarity analysis

US12386796B2 · kind B2 · utility

Assignee

• ACCENTURE GLOBAL SOLUTIONS LIMITED · IE

Inventors

• Prakash Ghatage · Bengaluru, IN
• Nirav Sampat · Mumbai, IN
• Kumar Viswanathan · San Jose, US
• Harsh Kumar Katiyar · Kanchinakote, IN
• Billy D. Hart · Cedar Rapids, US
• Amit PORWAL · Kanchinakote, IN

Key dates

• Filing date: Jan 24, 2023
• Grant date: Aug 12, 2025
• Expiry date: Oct 12, 2043

Classification

• Technology area (CPC G): Physics
• CPC primary: G06Q20/4016
• WIPO field: IT methods for management
• WIPO sector: Electrical engineering

Abstract

A device may process data associated with a first entity and a second entity, using one or more machine learning techniques, to identify a set of trends associated with a set of documents. The device may receive new documents associated with the first entity and the second entity. The device may generate, based on the set of trends, a first set of exceptions indicating that a new document is associated with a first type of error. The device may generate, using a similarity analysis technique, a second set of exceptions indicating that a new document is associated with a second type of error. The device may communicate with one or more systems to perform one or more actions associated with correction or prevention of processing errors relating to the new document based a claim relating to the first set of exceptions or the second set of exceptions.