公开(公告)号：US12073348B2

公开(公告)日：2024-08-27

申请号：US17013499

申请日：2020-09-04

申请人： ROYAL BANK OF CANADA

发明人： Kanika Vij ,  Vincent Chiu-Hua Huang ,  Preet Kanwal Singh

IPC分类号： G06F16/2458 ,  G06F16/22 ,  G06F16/26 ,  G06F17/18 ,  G06F40/205 ,  G06Q10/0635

CPC分类号： G06Q10/0635 ,  G06F16/2246 ,  G06F16/2474 ,  G06F16/26 ,  G06F17/18 ,  G06F40/205

摘要： Systems for dynamically updating a priority queue for scheduling data operations associated with data records. A method includes: detecting event data meeting a threshold value for identifying a shift in data operation relevance and traversing a cascading data structure to identify an auditable entity corresponding to one or more data records associated with the event data, the one or more data records corresponding to at least one branch of the cascading data structure. The method includes generating an updated priority queue for scheduling data operations based on priority weights associated with branches of the cascading data structure, the updated priority queue based on event data corresponding to the identified auditable entity relative to event data associated with one or more records corresponding to other auditable entities and transmitting a signal for dynamically communicating the updated priority queue for scheduling data operations associated with data records of respective auditable entities.

公开(公告)号：US11727015B2

公开(公告)日：2023-08-15

申请号：US17127637

申请日：2020-12-18

申请人： ROYAL BANK OF CANADA

发明人： Kanika Vij ,  Igor Reshynsky ,  Hameet Jassal ,  Emma Hu ,  Min Liang ,  Adam Lazure ,  Esther Choi ,  Rowan Comish ,  Jingyi Gao ,  Gladys Leung ,  Diane Fenton

IPC分类号： G06F16/00 ,  G06F16/2457 ,  G06F16/2455 ,  G06F16/248

CPC分类号： G06F16/24573 ,  G06F16/248 ,  G06F16/24568

摘要： Systems and methods of monitoring for anomalous data records. The system conducts a method including: receiving a data record associated with at least one meta attribute to determine whether subsequent processing of the data record is warranted; generating an anomaly prediction for the data record based on a detection model and the at least one meta attribute associated with the data record, the detection model defined by a plurality of score distribution representations based on quantile bins and a dynamic quantile weight for providing an interim anomaly measure corresponding to respective score distribution representations, wherein the anomaly prediction is generated based on a combination of interim anomaly measures associated with respective meta attributes associated with the data record; and transmitting a signal representing the anomaly prediction for presentation at a user device for identifying one or more data records for subsequent data processes.