公开(公告)号：US20210279230A1

公开(公告)日：2021-09-09

申请号：US16813424

申请日：2020-03-09

Applicant: Adobe Inc.

Inventor： Pulkit Goel ,  Naman Poddar ,  Gaurav Sinha ,  Ayush Chauhan ,  Aurghya Maiti

IPC: G06F16/23

Abstract: The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining causal contributions of dimension values to anomalous data based on causal effects of such dimension values on the occurrence of other dimension values from interventions performed in a causal graph. For example, the disclosed systems can identify an anomalous dimension value that reflects a threshold change in value between an anomalous time period and a reference time period. The disclosed systems can determine causal effects by traversing a causal network representing dependencies between different dimensions associated with the dimension values. Based on the causal effects, the disclosed systems can determine causal contributions of particular dimension values on the anomalous dimension value. Further, the disclosed systems can generate a causal-contribution ranking of the particular dimension values based on the determined causal contributions.

公开(公告)号：US20240061830A1

公开(公告)日：2024-02-22

申请号：US18492551

申请日：2023-10-23

Applicant: Adobe Inc.

Inventor： Pulkit Goel ,  Naman Poddar ,  Gaurav Sinha ,  Ayush Chauhan ,  Aurghya Maiti

IPC: G06F16/23

CPC classification number: G06F16/2365

Abstract: The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining causal contributions of dimension values to anomalous data based on causal effects of such dimension values on the occurrence of other dimension values from interventions performed in a causal graph. For example, the disclosed systems can identify an anomalous dimension value that reflects a threshold change in value between an anomalous time period and a reference time period. The disclosed systems can determine causal effects by traversing a causal network representing dependencies between different dimensions associated with the dimension values. Based on the causal effects, the disclosed systems can determine causal contributions of particular dimension values on the anomalous dimension value. Further, the disclosed systems can generate a causal-contribution ranking of the particular dimension values based on the determined causal contributions.

公开(公告)号：US11797515B2

公开(公告)日：2023-10-24

申请号：US16813424

申请日：2020-03-09

Applicant: Adobe Inc.

Inventor： Pulkit Goel ,  Naman Poddar ,  Gaurav Sinha ,  Ayush Chauhan ,  Aurghya Maiti

IPC: G06F16/23

CPC classification number: G06F16/2365

Abstract: The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining causal contributions of dimension values to anomalous data based on causal effects of such dimension values on the occurrence of other dimension values from interventions performed in a causal graph. For example, the disclosed systems can identify an anomalous dimension value that reflects a threshold change in value between an anomalous time period and a reference time period. The disclosed systems can determine causal effects by traversing a causal network representing dependencies between different dimensions associated with the dimension values. Based on the causal effects, the disclosed systems can determine causal contributions of particular dimension values on the anomalous dimension value. Further, the disclosed systems can generate a causal-contribution ranking of the particular dimension values based on the determined causal contributions.