Patent Application for Parsing Data Structure Ontologies in Databases

ChangeBridge: Patent Apps - Business Methods (G06Q)

Detected March 27th, 2026

Summary

The USPTO has published a patent application (US20260087547A1) filed by Truist Bank for a system and method to parse data structure ontologies in databases. The application utilizes AI and machine learning to organize data, identify patterns, and generate targeted actions for users.

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What changed

This document is a published patent application from the USPTO, filed by Truist Bank, detailing a system and method for parsing data structure ontologies within database environments. The application, identified as US20260087547A1, describes using artificial intelligence and machine learning to analyze user data, identify patterns, apply predefined rules, and generate ranked targeted actions sent to a user device. The filing date was September 25, 2024.

As this is a patent application, it does not impose new regulatory obligations or compliance deadlines on regulated entities. However, it signals potential technological advancements in data management and AI-driven financial services that compliance officers may wish to monitor for future industry trends or competitive landscape shifts. No immediate action is required from a compliance perspective.

Source document (simplified)

← USPTO Patent Applications

PARSING DATA STRUCTURE ONTOLOGIES IN DATABASE ENVIRONMENTS TO IDENTIFY, ORGANIZE AND INDEX DATA FILES

Application US20260087547A1 Kind: A1 Mar 26, 2026

Assignee

Truist Bank

Inventors

Cameron Denson

Abstract

A system and method for parsing data structure ontologies in a database environment to identify, organize and index data files include: a processor; a non-transitory memory operably connected to the processor; and a targeted actions application stored in the memory and including executable instructions that, when executed by the processor, cause the processor to: identify and organize data associated with a user, the data being stored in a users database in the memory; identify rules associated with the user stored in a rules database in the memory; use artificial intelligence machine learning to analyze the data and detect patterns in the data; apply the rules to the detected patterns to generate targeted actions; assign ranks to the targeted actions and select a one of the targeted actions having a highest rank; generate a message including the selected targeted action; and send the message to a user device of the user.