公开(公告)号：US20240031631A1

公开(公告)日：2024-01-25

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: H04N21/2668 ,  H04N21/25

CPC classification number: H04N21/2668 ,  H04N21/252

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：US20230342799A1

公开(公告)日：2023-10-26

申请号：US17660544

申请日：2022-04-25

Applicant: Adobe Inc.

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

IPC: G06N3/08 ,  G06Q30/02 ,  G06N3/04

CPC classification number: G06Q30/0204 ,  G06N3/0454 ,  G06N3/08 ,  G06Q30/0202

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for incorporating unobserved behaviors when generating user segments or predictions of future user actions. In particular, in one or more embodiments, the disclosed systems utilize a deep learning-based clustering algorithm that segments the behavioral history of users based on a future outcome. Further, the disclosed systems recognize that users may exhibit behaviors that represent two or more segments and allow for targeted marketing to users based on the user’s inclusion in multiple segments.

公开(公告)号：US20230306033A1

公开(公告)日：2023-09-28

申请号：US17693811

申请日：2022-03-14

Applicant: ADOBE INC.

Inventor： Arpit Ajay Narechania ,  Fan Du ,  Atanu R. Sinha ,  Ryan A. Rossi ,  Jane Elizabeth Hoffswell ,  Shunan Guo ,  Eunyee Koh ,  John Anderson ,  Sonali Surange ,  Saurabh Mahapatra ,  Vasanthi Holtcamp

IPC: G06F16/2457 ,  G06F16/25 ,  G06F16/215

CPC classification number: G06F16/24575 ,  G06F16/215 ,  G06F16/254

Abstract: Embodiments provide systems, methods, and computer storage media for management, assessment, navigation, and/or discovery of data based on data quality, consumption, and/or utility metrics. Data may be assessed using attribute-level and/or record-level metrics that quantify data: “quality” - the condition of data (e.g., presence of incorrect or incomplete values), its “consumption” - the tracked usage of data in downstream applications (e.g., utilization of attributes in dashboard widgets or customer segmentation rules), and/or its “utility” - a quantifiable impact resulting from the consumption of data (e.g., revenue or number of visits resulting from marketing campaigns that use particular datasets, storage costs of data). This data assessment may be performed at different stages of a data intake, preparation, and/or modeling lifecycle. For example, current and historical data metrics may be periodically aggregated, persisted, and/or monitored to facilitate discovery and removal of less effective data from a data lake.

公开(公告)号：US11769100B2

公开(公告)日：2023-09-26

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: G06Q10/0639 ,  G06F18/214 ,  G06F18/2321

CPC classification number: G06Q10/06393 ,  G06F18/214 ,  G06F18/2321

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：US20230289839A1

公开(公告)日：2023-09-14

申请号：US17693799

申请日：2022-03-14

Applicant: ADOBE INC.

Inventor： Arpit Ajay Narechania ,  Fan Du ,  Atanu R. Sinha ,  Ryan A. Rossi ,  Jane Elizabeth Hoffswell ,  Shunan Guo ,  Eunyee Koh ,  John Anderson ,  Sonali Surange ,  Saurabh Mahapatra ,  Vasanthi Holtcamp

IPC: G06Q30/02

CPC classification number: G06Q30/0204

Abstract: Embodiments provide systems, methods, and computer storage media for management, assessment, navigation, and/or discovery of data based on data quality, consumption, and/or utility metrics. Data may be assessed using attribute-level and/or record-level metrics that quantify data: “quality”—the condition of data (e.g., presence of incorrect or incomplete values), its “consumption”—the tracked usage of data in downstream applications (e.g., utilization of attributes in dashboard widgets or customer segmentation rules), and/or its “utility”—a quantifiable impact resulting from the consumption of data (e.g., revenue or number of visits resulting from marketing campaigns that use particular datasets, storage costs of data). This data assessment may be performed at different stages of a data intake, preparation, and/or modeling lifecycle. For example, a data selection interface may filter based on consumption and/or quality metrics to facilitate discovery of more effective data for machine learning model training, data visualization, or marketing campaigns.