公开(公告)号：US12056469B2

公开(公告)日：2024-08-06

申请号：US17377556

申请日：2021-07-16

申请人： International Business Machines Corporation

发明人： Filipe Joao Afonso Barroso ,  Chester Lee ,  Silvia Lopez ,  Yuhong Yin ,  Lakshmanan Annamalai

IPC分类号： G06F8/35 ,  G06F16/11 ,  G06N20/00 ,  G06Q10/0637 ,  G06Q10/10 ,  G06Q40/06 ,  G06Q40/08 ,  G06Q50/26

CPC分类号： G06F8/35 ,  G06F16/128 ,  G06N20/00 ,  G06Q10/06375 ,  G06Q10/103 ,  G06Q40/06 ,  G06Q40/08 ,  G06Q50/26

摘要： Methods and systems for generating a computer program for a governance, risk, compliance (GRC) system are described. In an example, a processor may detect a change among a plurality of objects in the GRC system. The processor may generate a snapshot of the plurality of objects in response to the detected change. The snapshot may include the detected change among the plurality of objects. The processor may train a machine learning model using the snapshot. The trained machine learning model may indicate relationships among a set of related objects, where the related objects may be among the plurality of objects. The processor may generate a computer program based on the trained machine learning model. The computer program may include a set of instructions for setting a field value of an object among the set of related objects.

公开(公告)号：US20230018159A1

公开(公告)日：2023-01-19

申请号：US17377556

申请日：2021-07-16

申请人： International Business Machines Corporation

发明人： Filipe Joao Afonso Barroso ,  Chester Lee ,  Silvia Lopez ,  Yuhong Yin ,  LAKSHMANAN Annamalai

IPC分类号： G06F8/35 ,  G06N20/00 ,  G06F16/11

摘要： Methods and systems for generating a computer program for a governance, risk, compliance (GRC) system are described. In an example, a processor may detect a change among a plurality of objects in the GRC system. The processor may generate a snapshot of the plurality of objects in response to the detected change. The snapshot may include the detected change among the plurality of objects. The processor may train a machine learning model using the snapshot. The trained machine learning model may indicate relationships among a set of related objects, where the related objects may be among the plurality of objects. The processor may generate a computer program based on the trained machine learning model. The computer program may include a set of instructions for setting a field value of an object among the set of related objects.