公开(公告)号：US20230030228A1

公开(公告)日：2023-02-02

申请号：US17934001

申请日：2022-09-21

申请人： NASDAQ, INC.

发明人： Xuyang LIN ,  Tudor MOROSAN ,  Douglas HAMILTON ,  Shihui CHEN ,  Hyunsoo JEONG ,  Jonathan RIVERS ,  Leonid ROSENFELD

IPC分类号： G06F16/23 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08

摘要： A computer system is provided for monitoring and detecting changes in a data generating processes, which may be under a multi-dimensional and unsupervised setting. A target dataset is split into paired subgroups by a separator and one or more detectors are applied to detect changes, anomalies, inconsistencies, and the like between the paired subgroups. Metrics may be generated by the detector(s), which are then passed to an evaluating system.

公开(公告)号：US20240086737A1

公开(公告)日：2024-03-14

申请号：US18514735

申请日：2023-11-20

申请人： Nasdaq, Inc.

发明人： Douglas HAMILTON ,  Michael O'ROURKE ,  Xuyang LIN ,  Hyunsoo JEONG ,  William DAGUE ,  Tudor MOROSAN

IPC分类号： G06N5/022 ,  G06F16/25 ,  G06F18/2113 ,  G06F18/214 ,  G06N5/01 ,  G06N20/00

CPC分类号： G06N5/022 ,  G06F16/254 ,  G06F18/2113 ,  G06F18/2148 ,  G06N5/01 ,  G06N20/00

摘要： A computer system is provided that is programmed to select feature sets from a large number of features. Features for a set are selected based on metagradient information returned from a machine learning process that has been performed on an earlier selected feature set. The process can iterate until a selected feature set converges or otherwise meets or exceeds a given threshold.

公开(公告)号：US20240004862A1

公开(公告)日：2024-01-04

申请号：US18469273

申请日：2023-09-18

申请人： NASDAQ, INC.

发明人： Xuyang LIN ,  Tudor MOROSAN ,  Douglas HAMILTON ,  Shihui CHEN ,  Hyunsoo JEONG ,  Jonathan RIVERS ,  Leonid ROSENFELD

IPC分类号： G06F16/23 ,  G06N3/088 ,  G06F18/214 ,  G06N3/045

CPC分类号： G06F16/2358 ,  G06N3/088 ,  G06F18/2155 ,  G06N3/045

摘要： A computer system is provided for monitoring and detecting changes in a data generating processes, which may be under a multi-dimensional and unsupervised setting. A target dataset is split into paired subgroups by a separator and one or more detectors are applied to detect changes, anomalies, inconsistencies, and the like between the paired subgroups. Metrics may be generated by the detector(s), which are then passed to an evaluating system.

公开(公告)号：US20230142808A1

公开(公告)日：2023-05-11

申请号：US18151094

申请日：2023-01-06

申请人： Nasdaq, Inc.

发明人： Douglas HAMILTON ,  Michael O'ROURKE ,  Xuyang LIN ,  Hyunsoo JEONG ,  William DAGUE ,  Tudor MOROSAN

IPC分类号： G06F18/214 ,  G06F16/25 ,  G06N20/00 ,  G06F18/2113 ,  G06N5/01

CPC分类号： G06F18/2148 ,  G06F16/254 ,  G06N20/00 ,  G06F18/2113 ,  G06N5/01

摘要： A computer system is provided that is programmed to select feature sets from a large number of features. Features for a set are selected based on metagradient information returned from a machine learning process that has been performed on an earlier selected feature set. The process can iterate until a selected feature set converges or otherwise meets or exceeds a given threshold.

公开(公告)号：US20200265032A1

公开(公告)日：2020-08-20

申请号：US16794011

申请日：2020-02-18

申请人： NASDAQ, INC.

发明人： Xuyang LIN ,  Tudor Morosan ,  Douglas Hamilton ,  Shihui Chen ,  Hyunsoo Jeong ,  Jonathan Rivers ,  Leonid Rosenfeld

IPC分类号： G06F16/23 ,  G06N3/08 ,  G06N3/04 ,  G06K9/62

摘要： A computer system is provided for monitoring and detecting changes in a data generating processes, which may be under a multi-dimensional and unsupervised setting. A target dataset is split into paired subgroups by a separator and one or more detectors are applied to detect changes, anomalies, inconsistencies, and the like between the paired subgroups. Metrics may be generated by the detector(s), which are then passed to an evaluating system.

公开(公告)号：US20190303719A1

公开(公告)日：2019-10-03

申请号：US16368804

申请日：2019-03-28

申请人： Nasdaq, Inc.

发明人： Douglas HAMILTON ,  Michael O'ROURKE ,  Xuyang LIN ,  Hyunsoo JEONG ,  William DAGUE ,  Tudor MOROSAN

IPC分类号： G06K9/62 ,  G06N20/00 ,  G06F16/25 ,  G06N5/00

摘要： A computer system is provided that is programmed to select feature sets from a large number of features. Features for a set are selected based on metagradient information returned from a machine learning process that has been performed on an earlier selected feature set. The process can iterate until a selected feature set converges or otherwise meets or exceeds a given threshold.