公开(公告)号：US20220139010A1

公开(公告)日：2022-05-05

申请号：US17083702

申请日：2020-10-29

Applicant: Adobe Inc.

Inventor： Fan Du ,  Xiao Xie ,  Shiv Kumar Saini ,  Gaurav Sinha ,  Ayush Chauhan

IPC: G06T11/20 ,  G06F16/22 ,  G06F16/901 ,  G06F16/26 ,  G06T3/40

Abstract: The present disclosure describes systems, methods, and non-transitory computer readable media for generating and providing a causal-graph interface that visually depicts causal relationships among dimensions and represents uncertainty metrics for such relationships as part of a streamlined visualization of a causal graph. The disclosed systems can determine causality among dimensions of multidimensional data and determine uncertainty metrics associated with individual causal relationships. Additionally, the disclosed system can generate a visual representation of a causal graph with nodes arranged in stratified layers and can connect the layered nodes with uncertainty-aware-causal edges to represent both the causality between the dimensions and the uncertainty metrics. Further, the disclosed systems can provide interactive tools for generating and visualizing predictions or causal relationships in intuitive user interfaces, such as visualizations for dimension-specific (or dimension-value-specific) interventions and/or attribution determinations.

公开(公告)号：US11321885B1

公开(公告)日：2022-05-03

申请号：US17083702

申请日：2020-10-29

Applicant: Adobe Inc.

Inventor： Fan Du ,  Xiao Xie ,  Shiv Kumar Saini ,  Gaurav Sinha ,  Ayush Chauhan

IPC: G06T11/20 ,  G06F16/22 ,  G06F16/26 ,  G06T3/40 ,  G06F16/901

Abstract: The present disclosure describes systems, methods, and non-transitory computer readable media for generating and providing a causal-graph interface that visually depicts causal relationships among dimensions and represents uncertainty metrics for such relationships as part of a streamlined visualization of a causal graph. The disclosed systems can determine causality among dimensions of multidimensional data and determine uncertainty metrics associated with individual causal relationships. Additionally, the disclosed system can generate a visual representation of a causal graph with nodes arranged in stratified layers and can connect the layered nodes with uncertainty-aware-causal edges to represent both the causality between the dimensions and the uncertainty metrics. Further, the disclosed systems can provide interactive tools for generating and visualizing predictions or causal relationships in intuitive user interfaces, such as visualizations for dimension-specific (or dimension-value-specific) interventions and/or attribution determinations.