US 12,339,905 B1
Systems and methods for a graph database
Heather Marie Gavlak, Delaware, OH (US); Richard Gregory Barker, Gibsonia, PA (US); Evan Michael Sorger, Berea, OH (US); and Kevin William Black, Pittsburgh, PA (US)
Assigned to The PNC Financial Services Group, Inc., Pittsburgh, PA (US)
Filed by The PNC Financial Services Group, Inc, Pittsburgh, PA (US)
Filed on Oct. 19, 2023, as Appl. No. 18/381,990.
Claims priority of provisional application 63/489,143, filed on Mar. 8, 2023.
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 16/00 (2019.01); G06F 16/20 (2019.01); G06F 16/353 (2025.01); G06F 16/901 (2019.01); G06F 16/904 (2019.01); G06F 16/9535 (2019.01); G06N 20/00 (2019.01); G06Q 20/10 (2012.01); G06Q 30/0226 (2023.01)
CPC G06F 16/9024 (2019.01) [G06F 16/20 (2019.01); G06F 16/353 (2019.01); G06F 16/904 (2019.01); G06F 16/9535 (2019.01); G06N 20/00 (2019.01); G06Q 20/10 (2013.01); G06Q 30/0229 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A computer system for categorizing data in one or more domains for storage in a database, comprising:
a storage device that stores instructions; and
one or more processors that execute the instructions to:
receive the one or more data domains from a source system, wherein each of the one or more data domains is configured to be distributed, using a data warehouse, between one or more applications and each of the data domains is categorized according to a logical grouping of the data domains based on instruments within the source system for storage within the database;
store the categorized data for each of the one or more data domains within the database, the categorized data including user profile data and content associated with architectural analytics;
further categorize each of the one or more data domains as having a data velocity selected from a range of data velocities, the range of data velocities including a low data velocity, a medium data velocity, and a high data velocity, each data velocity being representative of a frequency with which data of the data domain changes, the low data velocity corresponding to data changes that occur below a threshold frequency;
analyze, using one or more machine learning algorithms including an anomaly detection algorithm, the categorized data of the data domains to detect inconsistencies;
identify different patterns of user behavior for assessing decisions of a user; and
predict one or more faults in the database's architectural framework.