公开(公告)号：US20240028669A1

公开(公告)日：2024-01-25

申请号：US17814394

申请日：2022-07-22

Applicant: Adobe Inc.

Inventor： Vibhor Porwal ,  Gaurav Sinha

IPC: G06K9/62 ,  G06N7/00

CPC classification number: G06K9/6297 ,  G06N7/005

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that verify causal graphs utilizing nodes from corresponding Markov equivalence classes. For instance, in one or more embodiments, the disclosed systems receive a causal graph to be validated and a Markov equivalence class that corresponds to the causal graph. Additionally, the disclosed systems determine an intervention set using the causal graph, the intervention set comprising nodes from the Markov equivalence class. Using a plurality of interventions on the nodes of the intervention set, the disclosed systems determine whether the causal graph is valid.

公开(公告)号：US20230385854A1

公开(公告)日：2023-11-30

申请号：US18362833

申请日：2023-07-31

Applicant: Adobe Inc.

Inventor： Ayush Chauhan ,  Vineet Malik ,  Sourav Suman ,  Siddharth Jain ,  Gaurav Sinha ,  Aayush Makharia

IPC: G06Q30/0201 ,  G06F16/901 ,  G06N20/00 ,  G06N7/01

CPC classification number: G06Q30/0201 ,  G06F16/9024 ,  G06N20/00 ,  G06N7/01

Abstract: Introduced here are approaches to determining causal relationships in mixed datasets containing data related to continuous variables and discrete variables. To accomplish this, a marketing insight and intelligence platform may employ a multi-phase approach in which dependency is established before the data related to continuous variables is discretized. Such an approach ensures that information regarding dependence is not lost through discretization.

公开(公告)号：US20230051416A1

公开(公告)日：2023-02-16

申请号：US17402788

申请日：2021-08-16

Applicant: Adobe Inc.

Inventor： Vibhor Porwal ,  Ayush Chauhan ,  Aurghya Maiti ,  Gaurav Sinha ,  Ruchi Sandeep Pandya

IPC: G06N7/00 ,  G06K9/62 ,  G06N5/04

Abstract: In implementations of systems for estimating terminal event likelihood, a computing device implements a termination system to receive observed data describing values of a treatment metric and indications of a terminal event. Values of the treatment metric are grouped into groups using a mixture model that represents the treatment metric as a mixture of distributions. Parameters of a distribution are estimated for each of the groups and mixing proportions are also estimated for each of the groups. In response to receiving a user input requesting an estimate of a likelihood of the terminal event for a particular value of the treatment metric, the termination system generates an indication of the estimate of the likelihood of the terminal event for the particular value based on a distribution density at the particular value for each of the groups and a probability of including the particular value in each of the groups.

公开(公告)号：US11763325B2

公开(公告)日：2023-09-19

申请号：US17097508

申请日：2020-11-13

Applicant: Adobe Inc.

Inventor： Ayush Chauhan ,  Vineet Malik ,  Sourav Suman ,  Siddharth Jain ,  Gaurav Sinha ,  Aayush Makharia

IPC: G06Q30/0201 ,  G06F16/901 ,  G06N20/00 ,  G06N7/01

CPC classification number: G06Q30/0201 ,  G06F16/9024 ,  G06N7/01 ,  G06N20/00

Abstract: Introduced here are approaches to determining causal relationships in mixed datasets containing data related to continuous variables and discrete variables. To accomplish this, a marketing insight and intelligence platform may employ a multi-phase approach in which dependency is established before the data related to continuous variables is discretized. Such an approach ensures that information regarding dependence is not lost through discretization.

公开(公告)号：US20220156759A1

公开(公告)日：2022-05-19

申请号：US17097508

申请日：2020-11-13

Applicant: Adobe Inc.

Inventor： Ayush Chauhan ,  Vineet Malik ,  Sourav Suman ,  Siddharth Jain ,  Gaurav Sinha ,  Aayush Makharia

IPC: G06Q30/02 ,  G06N7/00 ,  G06N20/00 ,  G06F16/901

Abstract: Introduced here are approaches to determining causal relationships in mixed datasets containing data related to continuous variables and discrete variables. To accomplish this, a marketing insight and intelligence platform may employ a multi-phase approach in which dependency is established before the data related to continuous variables is discretized. Such an approach ensures that information regarding dependence is not lost through discretization.

公开(公告)号：US12154042B2

公开(公告)日：2024-11-26

申请号：US17402788

申请日：2021-08-16

Applicant: Adobe Inc.

Inventor： Vibhor Porwal ,  Ayush Chauhan ,  Aurghya Maiti ,  Gaurav Sinha ,  Ruchi Sandeep Pandya

IPC: G06N7/01 ,  G06F18/214 ,  G06F18/2321 ,  G06F18/23213 ,  G06N5/046 ,  G06N20/00 ,  G06F9/54

Abstract: In implementations of systems for estimating terminal event likelihood, a computing device implements a termination system to receive observed data describing values of a treatment metric and indications of a terminal event. Values of the treatment metric are grouped into groups using a mixture model that represents the treatment metric as a mixture of distributions. Parameters of a distribution are estimated for each of the groups and mixing proportions are also estimated for each of the groups. In response to receiving a user input requesting an estimate of a likelihood of the terminal event for a particular value of the treatment metric, the termination system generates an indication of the estimate of the likelihood of the terminal event for the particular value based on a distribution density at the particular value for each of the groups and a probability of including the particular value in each of the groups.

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.

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.