IP Library Granted Patent US 12,468,668
Granted Patent B2
US 12,468,668 · App. 18/408,659 · Granted Nov 11, 2025

System for intelligent database modelling

Inventors: Raja Arumugam Maharaja (Tamil Nadu, IN); Ankit Singh Negi (Gurugram, IN)
Assignee: BANK OF AMERICA CORPORATION
G06F16/21G06F8/61G06N20/00G06T11/00G06V30/18G06V30/422G06T2200/24
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,468,668
App. No.
18/408,659
Granted
Nov 11, 2025
Kind
B2
Abstract

Systems, computer program products, and methods are described herein for intelligent database modelling. The present invention is configured to capture, using the augmented reality application, a drawing made by the user on a medium; initiate a first machine learning model on the drawing; determine that the user is attempting to design a database model; extract at least a first entity from the drawing; determine, using the first machine learning model, one or more attributes for the first entity; initiate, via the augmented reality application, a first push notification for display on the computing device of the user; electronically receive, via the augmented reality application, a user selection of a subset of attributes from the one or more attributes; generate, using the augmented reality application, a conceptual database model based on at least the first entity and the subset of attributes.

Claims (91)

1 . A system for intelligent database modelling, the system comprising:

at least one non-transitory storage device; and

at least one processing device coupled to the at least one non-transitory storage device, wherein the at least one processing device is configured to:

provide an augmented reality application for installation on a computing device of a user;

capture, using the augmented reality application, a drawing made by the user on a medium;

periodically initiate a first machine learning model on the drawing until the first machine learning model determines that the user is beginning to design a database model;

determine, using the first machine learning model, that the user is beginning to design the database model;

enhance, using the augmented reality the drawing using computer-generated perceptual information;

extract at least a first entity from the enhanced drawing based on at least determining that the user is beginning to the design the database model, wherein the first entity comprises an employee;

determine, using the first machine learning model, one or more attributes for the first entity, wherein each attribute is associated with a name and a data source for a value associated therewith, wherein, in an instance in which the first entity is an employee, the one or more attributes comprises at least employee information;

initiate, via the augmented reality application, a first push notification for display on the computing device of the user, wherein the first push notification comprises at least the one or more attributes;

electronically receive, via the augmented reality application, a user selection of a subset of attributes from the one or more attributes;

generate, using the augmented reality application, a conceptual database model based on at least the first entity and the subset of attributes;

automatically generate, using the first machine learning model, a relationship between the first entity and the subset of attributes;

record the relationship in the conceptual database model;

determine, using the first machine learning model, one or more database schema based on at least the conceptual database model;

initiate, via the augmented reality application, a second push notification for display on the computing device of the user, wherein the second push notification comprises at least the one or more database schema;

electronically receive, via the augmented reality application, a user selection of a database schema from the one or more database schema;

in response, generate a physical database model using the database schema, wherein generating further comprises converting each entity into corresponding tables, each relationship into corresponding foreign keys, and the subset of attributes into corresponding columns based on physical constraints and requirements of the database schema selected by the user; and

automatically generate an entity relationship diagram (ERD) based on the physical database model illustrating a logical structure of the database model.

2 . The system of claim 1 , wherein the at least one processing device is further configured to:

retrieve one or more database models from a data repository;

extract one or more entities and one or more attributes from the one or more database models;

initiate a machine learning algorithm on one or more database models, and the one or more entities and the one or more attributes; and

train, using the machine learning algorithm, the first machine learning model, wherein training further comprises determining one or more classification parameters for the first machine learning model.

3 . The system of claim 2 , wherein the at least one processing device is further configured to:

determine, using the classification parameters, the one or more attributes for the first entity.

4 . The system of claim 1 , wherein the at least one processing device is further configured to:

automatically generate, using the first machine learning model, a relationship between the first entity and the subset of attributes; and

record the relationship in the conceptual database model.

5 . The system of claim 4 , wherein the at least one processing device is further configured to:

transmit control signals configured to cause the computing device of the user to display the conceptual database model for user approval; and

electronically receive, via the computing device of the user, the user approval for the conceptual database model.

6 . The system of claim 1 , wherein the at least one processing device is further configured to:

initiate a second machine learning model on one or more data sources and the physical database model; and

determine, using the second machine learning model, at least one data source that is likely to be triggered when populating a database created using the physical database model.

7 . The system of claim 1 , wherein broadcasting the new transaction object further comprises:

receive, from a computing device associated with the at least one data source, an indication validating the physical database model; and

authorize a generation of a database using the physical database model based on at least receiving the validation.

8 . A computer program product for intelligent database modelling, the computer program product comprising a non-transitory computer-readable medium comprising code causing a first apparatus to:

provide an augmented reality application for installation on a computing device of a user;

capture, using the augmented reality application, a drawing made by the user on a medium;

periodically initiate a first machine learning model on the drawing until the first machine learning model determines that the user is beginning to design a database model;

determine, using the first machine learning model, that the user is beginning to design the database model;

enhance, using the augmented reality the drawing using computer-generated perceptual information;

extract at least a first entity from the enhanced drawing based on at least determining that the user is beginning to the design the database model, wherein the first entity comprises an employee;

determine, using the first machine learning model, one or more attributes for the first entity, wherein each attribute is associated with a name and a data source for a value associated therewith, wherein, in an instance in which the first entity is an employee, the one or more attributes comprises at least employee information;

initiate, via the augmented reality application, a first push notification for display on the computing device of the user, wherein the first push notification comprises at least the one or more attributes;

electronically receive, via the augmented reality application, a user selection of a subset of attributes from the one or more attributes;

generate, using the augmented reality application, a conceptual database model based on at least the first entity and the subset of attributes;

automatically generate, using the first machine learning model, a relationship between the first entity and the subset of attributes;

record the relationship in the conceptual database model;

determine, using the first machine learning model, one or more database schema based on at least the conceptual database model;

initiate, via the augmented reality application, a second push notification for display on the computing device of the user, wherein the second push notification comprises at least the one or more database schema;

electronically receive, via the augmented reality application, a user selection of a database schema from the one or more database schema;

in response, generate a physical database model using the database schema, wherein generating further comprises converting each entity into corresponding tables, each relationship into corresponding foreign keys, and the subset of attributes into corresponding columns based on physical constraints and requirements of the database schema selected by the user; and

automatically generate an entity relationship diagram (ERD) based on the physical database model illustrating a logical structure of the database model.

9 . The computer program product of claim 8 , wherein the first apparatus is further configured to:

retrieve one or more database models from a data repository;

extract one or more entities and one or more attributes from the one or more database models;

initiate a machine learning algorithm on one or more database models, and the one or more entities and the one or more attributes; and

train, using the machine learning algorithm, the first machine learning model, wherein training further comprises determining one or more classification parameters for the first machine learning model.

10 . The computer program product of claim 9 , wherein the first apparatus is further configured to:

determine, using the classification parameters, the one or more attributes for the first entity.

11 . The computer program product of claim 8 , wherein the first apparatus is further configured to:

automatically generate, using the first machine learning model, a relationship between the first entity and the subset of attributes; and

record the relationship in the conceptual database model.

12 . The computer program product of claim 11 , wherein the first apparatus is further configured to:

transmit control signals configured to cause the computing device of the user to display the conceptual database model for user approval; and

electronically receive, via the computing device of the user, the user approval for the conceptual database model.

13 . The computer program product of claim 8 , wherein the first apparatus is further configured to:

initiate a second machine learning model on the one or more data sources and the physical database model; and

determine, using the second machine learning model, at least one data source that is likely to be triggered when populating a database created using the physical database model.

14 . A method for intelligent database modelling, the method comprising:

providing an augmented reality application for installation on a computing device of a user;

capturing, using the augmented reality application, a drawing made by the user on a medium;

periodically initiating a first machine learning model on the drawing until the first machine learning model determines that the user is beginning to design a database model;

determining, using the first machine learning model, that the user is beginning to design the database model;

enhancing, using the augmented reality the drawing using computer-generated perceptual information;

extracting at least a first entity from the enhanced drawing based on at least determining that the user is beginning to the design the database model, wherein the first entity comprises an employee;

determining, using the first machine learning model, one or more attributes for the first entity, wherein each attribute is associated with a name and a data source for a value associated therewith, wherein, in an instance in which the first entity is an employee, the one or more attributes comprises at least employee information;

initiating, via the augmented reality application, a first push notification for display on the computing device of the user, wherein the first push notification comprises at least the one or more attributes;

electronically receiving, via the augmented reality application, a user selection of a subset of attributes from the one or more attributes;

generating, using the augmented reality application, a conceptual database model based on at least the first entity and the subset of attributes;

automatically generating, using the first machine learning model, a relationship between the first entity and the subset of attributes;

recording the relationship in the conceptual database model;

determining, using the first machine learning model, one or more database schema based on at least the conceptual database model;

initiating, via the augmented reality application, a second push notification for display on the computing device of the user, wherein the second push notification comprises at least the one or more database schema;

electronically receiving, via the augmented reality application, a user selection of a database schema from the one or more database schema;

in response, generating a physical database model using the database schema, wherein generating further comprises converting each entity into corresponding tables, each relationship into corresponding foreign keys, and the subset of attributes into corresponding columns based on physical constraints and requirements of the database schema selected by the user; and

automatically generating an entity relationship diagram (ERD) based on the physical database model illustrating a logical structure of the database model.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 10, 2024
From: MAHARAJA, RAJA ARUMUGAM; NEGI, ANKIT SINGH
To: BANK OF AMERICA CORPORATION
Reel/Frame 066076/0356 →
Continuity (2)
Continuation 17482779 · Sep 23, 2021
Related Publication 20250028690A1 · Jan 23, 2025
References Cited (74)
US 5970490A · Morgenstern · 1999 [cited by applicant]
US 6044374A · Nesamoney et al. · 2000 [cited by applicant]
US 6161139A · Win et al. · 2000 [cited by applicant]
US 6535868B1 · Galeazzi et al. · 2003 [cited by applicant]
US 6697808B1 · Hurwood et al. · 2004 [cited by applicant]
US 6957225B1 · Zait et al. · 2005 [cited by applicant]
US 7013310B2 · Messing et al. · 2006 [cited by applicant]
US 7373350B1 · Arone et al. · 2008 [cited by applicant]
US 7389314B2 · Kulkarni et al. · 2008 [cited by applicant]
US 7554542B1 · Ferraro et al. · 2009 [cited by applicant]
US 7634515B2 · Hunter et al. · 2009 [cited by applicant]
US 7647293B2 · Brown et al. · 2010 [cited by applicant]
US 7702636B1 · Sholtis et al. · 2010 [cited by applicant]
US 7774378B2 · Nelson · 2010 [cited by applicant]
US 7797319B2 · Piedmonte · 2010 [cited by applicant]
US 8180872B1 · Marinelli et al. · 2012 [cited by applicant]
US 8364955B1 · Sharma et al. · 2013 [cited by applicant]
US 10282360B2 · Krause et al. · 2019 [cited by applicant]
US 10552383B2 · Sharma et al. · 2020 [cited by applicant]
US 10699488B1 · Terrano · 2020 [cited by applicant]
US 10922493B1 · Das · 2021 [cited by applicant]
US 11915377B1 · Bhushan · 2024 [cited by applicant]
US 20040243458A1 · Barkan · 2004 [cited by applicant]
US 20050071359A1 · Elandassery et al. · 2005 [cited by applicant]
US 20060195492A1 · Clark et al. · 2006 [cited by applicant]
US 20060248083A1 · Sack et al. · 2006 [cited by applicant]
US 20070156792A1 · D'Souza et al. · 2007 [cited by applicant]
US 20070174308A1 · Rausch · 2007 [cited by applicant]
US 20070214179A1 · Hoang · 2007 [cited by applicant]
US 20070282784A1 · Modani et al. · 2007 [cited by applicant]
US 20080140696A1 · Mathuria · 2008 [cited by applicant]
US 20080222192A1 · Hughes · 2008 [cited by applicant]
US 20090083768A1 · Hatalkar et al. · 2009 [cited by applicant]
US 20090106242A1 · Mcgrew et al. · 2009 [cited by applicant]
US 20110251984A1 · Nie · 2011 [cited by examiner]
US 20120237022A1 · Berson et al. · 2012 [cited by applicant]
US 20140253590A1 · Needham · 2014 [cited by examiner]
US 20150067154A1 · Ly et al. · 2015 [cited by applicant]
US 20160275526A1 · Becanovic · 2016 [cited by applicant]
US 20160292672A1 · Fay et al. · 2016 [cited by applicant]
US 20160300234A1 · Moss-Pultz · 2016 [cited by applicant]
US 20170005804A1 · Zinder · 2017 [cited by applicant]
US 20170032249A1 · Chougule · 2017 [cited by examiner]
US 20170041296A1 · Darji et al. · 2017 [cited by applicant]
US 20170046651A1 · Lin et al. · 2017 [cited by applicant]
US 20170124487A1 · Szeto et al. · 2017 [cited by applicant]
US 20170124556A1 · Seger, II · 2017 [cited by applicant]
US 20170126702A1 · Krishnamurthy · 2017 [cited by applicant]
US 20170169103A1 · Juneja et al. · 2017 [cited by applicant]
US 20170236094A1 · Shah · 2017 [cited by applicant]
US 20170352027A1 · Zhang et al. · 2017 [cited by applicant]
US 20170353309A1 · Gray · 2017 [cited by applicant]
US 20170371876A1 · Bailey · 2017 [cited by examiner]
US 20180005186A1 · Hunn · 2018 [cited by applicant]
US 20180025181A1 · Barinov et al. · 2018 [cited by applicant]
US 20180227116A1 · Chapman et al. · 2018 [cited by applicant]
US 20180253452A1 · Callan et al. · 2018 [cited by applicant]
US 20190171975A1 · Kurian · 2019 [cited by examiner]
US 20190236598A1 · Padmanabhan · 2019 [cited by applicant]
US 20190325265A1 · Lecue · 2019 [cited by applicant]
US 20200066044A1 · Stahl · 2020 [cited by applicant]
US 20200082302A1 · Zoldi et al. · 2020 [cited by applicant]
US 20200184379A1 · Dong et al. · 2020 [cited by applicant]
US 20200293171A1 · Draper · 2020 [cited by applicant]
US 20200341951A1 · Oberhofer · 2020 [cited by examiner]
US 20200401576A1 · Yerli · 2020 [cited by applicant]
US 20210019215A1 · Neeter · 2021 [cited by applicant]
US 20210133850A1 · Ayush · 2021 [cited by applicant]
US 20210224695A1 · Stefanov · 2021 [cited by examiner]
US 20210390745A1 · Rykhliuk · 2021 [cited by applicant]
US 20220269702A1 · Portisch et al. · 2022 [cited by applicant]
US 20230004538A1 · Kumar · 2023 [cited by applicant]
US 20230018975A1 · Sreenivasan · 2023 [cited by examiner]
WO 2017098519A1 · 2017 [cited by applicant]