公开(公告)号：US20240232702A1

公开(公告)日：2024-07-11

申请号：US18152879

申请日：2023-01-11

Applicant: ADOBE INC.

Inventor： Aurghya Maiti ,  Iftikhar Ahamath Burhanuddin ,  Atanu R. Sinha ,  Saurabh Mahapatra ,  Fan Du

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: One aspect of a method for data processing includes identifying target time series data for a target metric and candidate time series data for a plurality of indicators predictive of the target metric; training a machine learning model to predict the target time series data based on the candidate time series data; computing first through third predictivity values based on the machine learning model, wherein the first predictivity value indicates that a source indicator from the plurality of indicators is predictive of the target metric, the second predictivity value indicates that an intermediate indicator from the plurality of indicators is predictive of the target metric, and the third predictivity value indicates that the source indicator is predictive of the intermediate indicator; and displaying a portion of the candidate time series data corresponding to the intermediate indicator and the source indicator based on the first through third predictivity values.

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：US20220383224A1

公开(公告)日：2022-12-01

申请号：US17329934

申请日：2021-05-25

Applicant: ADOBE INC.

Inventor： Atanu Sinha ,  Manoj Kilaru ,  Iftikhar Ahamath Burhanuddin ,  Aneesh Shetty ,  Titas Chakraborty ,  Rachit Bansal ,  Tirupati Saketh Chandra ,  Fan Du ,  Aurghya Maiti ,  Saurabh Mahapatra

IPC: G06Q10/06 ,  G06K9/62

Abstract: Systems and methods for data analytics are described. One or more embodiments of the present disclosure receive target time series data and candidate time series data, where the candidate time series data includes data corresponding to each of a plurality of candidate metrics, train a prediction network to predict the target time series data based on the candidate time series data by setting temporal attention weights corresponding to a plurality of rolling time windows and by setting candidate attention weights corresponding to the plurality of candidate metrics, identify a leading indicator metric for the target time series data from the plurality of candidate metrics based on the temporal attention weights and the candidate attention weights, and signal the leading indicator metric for the target time series data.

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.

公开(公告)号：US20230144357A1

公开(公告)日：2023-05-11

申请号：US17520403

申请日：2021-11-05

Applicant: ADOBE INC.

Inventor： Gaurav Sinha ,  Aurghya Maiti

IPC: G06Q10/06

CPC classification number: G06Q10/0637

Abstract: A treatment effect system estimates treatment effects by trading off between observational samples and interventional samples to maintain within a budget while providing high confidence. The treatment effect system determines whether to perform interventions by comparing the cost of interventional samples with metrics regarding the joint probability distribution of treatments and their parents in a first set of observational samples. If it is determined to not perform interventions, the treatment effect for each treatment is determined using an estimator that uses the first set of observational samples independent of a second set of observational samples. If it is determined to perform interventions, each treatment is identified as a reliable or unreliable treatment. The treatment effect for reliable treatments is estimated using an estimator that uses the first set of observational samples split into two portions. The treatment effect for unreliable treatments is estimated using interventional samples generated from interventions.

公开(公告)号：US12206925B2

公开(公告)日：2025-01-21

申请号：US17813622

申请日：2022-07-20

Applicant: ADOBE INC.

Inventor： Atanu R. Sinha ,  Aurghya Maiti ,  Atishay Ganesh ,  Saili Myana ,  Harshita Chopra ,  Sarthak Kapoor ,  Saurabh Mahapatra

IPC: H04N21/25 ,  H04N21/2668

Abstract: Systems and methods for content customization are provided. One aspect of the systems and methods includes receiving dynamic characteristics for a plurality of users, wherein the dynamic characteristics include interactions between the plurality of users and a digital content channel; clustering the plurality of users in a plurality of segments based on the dynamic characteristics using a machine learning model; assigning a user to a segment of the plurality of segments based on static characteristics of the user; and providing customized digital content for the user based on the segment.

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：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：US20250061488A1

公开(公告)日：2025-02-20

申请号：US18451590

申请日：2023-08-17

Applicant: ADOBE INC.

Inventor： Atanu R. Sinha ,  Ryan A. Rossi ,  Sunav Choudhary ,  Harshita Chopra ,  Paavan Indela ,  Veda Pranav Parwatala ,  Srinjayee Paul ,  Saurabh Mahapatra ,  Aurghya Maiti

IPC: G06Q30/0251 ,  G06N20/00 ,  G06Q30/0204

Abstract: Systems and methods for delivery aware audience segmentation and subsequent delivery of content are described. Embodiments are configured to obtain activity data for a user, assign the user to a user segment based on the activity data using a machine learning model, generate a reach prediction for the user segment, select a media channel for communicating with the user based on the user segment and the reach prediction, and provide targeted content to the user via the selected media channel. According to some aspects, the machine learning model is trained based on content reach data.