公开(公告)号：US11886964B2

公开(公告)日：2024-01-30

申请号：US17322108

申请日：2021-05-17

Applicant: Adobe Inc.

Inventor： Atanu R. Sinha ,  Xiang Chen ,  Sungchul Kim ,  Omar Rahman ,  Jean Bernard Hishamunda ,  Goutham Srivatsav Arra ,  Shiv Kumar Saini

IPC: G06N20/00 ,  G06F3/0484 ,  H04L67/50

CPC classification number: G06N20/00 ,  G06F3/0484 ,  H04L67/535

Abstract: Methods and systems disclosed herein relate generally to systems and methods for using a machine-learning model to predict user-engagement levels of users in response to presentation of future interactive content. A content provider system accesses a machine-learning model, which was trained using a training dataset including previous user-device actions performed by a plurality of users in response to previous interactive content. The content provider system receives user-activity data of a particular user and applies the machine-learning model to the user-activity data, in which the user-activity data includes user-device actions performed by the particular user in response to interactive content. The machine-learning model generates an output including a categorical value that represents a predicted user-engagement level of the particular user in response to a presentation of the future interactive content.

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：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：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.

公开(公告)号：US20230153448A1

公开(公告)日：2023-05-18

申请号：US17525744

申请日：2021-11-12

Applicant: ADOBE INC.

Inventor： Subrata Mitra ,  Sunny Dhamnani ,  Piyush Bagad ,  Raunak Gautam ,  Haresh Khanna ,  Atanu R. Sinha

IPC: G06F21/62 ,  G06K9/62 ,  G06N3/04

CPC classification number: G06F21/62 ,  G06K9/6256 ,  G06N3/0454

Abstract: Methods and systems are provided for facilitating generation of representative datasets. In embodiments, an original dataset for which a data representation is to be generated is obtained. A data generation model is trained to generate a representative dataset that represents the original dataset. The data generation model is trained based on the original dataset, a set of privacy settings indicating privacy of data associated with the original dataset, and a set of value settings indicating value of data associated with the original dataset. A representative dataset that represents the original dataset is generated via the trained data generation model. The generated representative dataset maintains a set of desired statistical properties of the original dataset, maintains an extent of data privacy of the set of original data, and maintains an extent of data value of the set of original data.

公开(公告)号：US11574266B2

公开(公告)日：2023-02-07

申请号：US16986673

申请日：2020-08-06

Applicant: Adobe Inc.

Inventor： Atanu R. Sinha ,  Ishita Sunity Kumar Chakraborty

IPC: G06Q10/06

Abstract: A human interaction replacement evaluation system analyzes actions taken by a user with an application on a client device that provides features to replace human interaction services with computer-based services. The results of the action provide an indication of the success of a particular action supported by the application (e.g., whether the action has a positive or negative effect on a key performance indicator) or an indication of how likely the user is to be ready to adopt a particular computer-based service. Recommendations are then provided to the user of the application or a manager of the application indicating actions to use, actions that have negative or positive effects on a key performance indicator, and so forth.

公开(公告)号：US11551239B2

公开(公告)日：2023-01-10

申请号：US16162023

申请日：2018-10-16

Applicant: Adobe Inc.

Inventor： Deepali Jain ,  Atanu R. Sinha ,  Deepali Gupta ,  Nikhil Sheoran ,  Sopan Khosla ,  Reshmi Naduparambil Sasidharan

IPC: G06Q30/02 ,  G06Q30/00 ,  G06F17/18 ,  G06F3/0484

Abstract: There is described a method and system in an interactive computing environment modified with user experience values based on behavior logs. An experience valuation system determines an experience value and an estimated experience value. The experience value is based on a current state of interaction data from a user session, based on a history of past events, and an estimation function defined by parameters to model the user experience values. The estimated experience value is determined based on, in addition to the current state and the estimation function, next states associated with the current state, and a reward function. The parameters of the estimation function are updated based on a comparison of the expected experience value and the estimated experience value. For another aspect, the method and system may further include a state prediction system to determine probabilities of transitioning that may be applied to determine the estimated experience value.

公开(公告)号：US20200236023A1

公开(公告)日：2020-07-23

申请号：US16253467

申请日：2019-01-22

Applicant: Adobe Inc.

Inventor： Pranav Ravindra Maneriker ,  Reshmi Sasidharan ,  Atanu R. Sinha

IPC: H04L12/26 ,  H04L12/24

Abstract: In some embodiments, an intervention evaluation system estimates counterfactual metric for a focal online platform based on an assessment model built using performance data of the focal online platform and control online platforms. The intervention evaluation system accesses performance data of the focal online platform that has been subject to a temporary intervention and performance data of control online platforms that are not subject to the temporary intervention. The intervention evaluation system determines estimation weights for these control online platforms based on the performance data in a pre-intervention period. Based on the estimation weights, the intervention evaluation system computes a counterfactual metric indicating the performance of the focal online platform in a post-intervention period in the absence of the temporary intervention. The counterfactual metric is transmitted to the focal online platform, where the counterfactual metric is usable for modifying an interactive computing environment provided by the focal online platform.

公开(公告)号：US20190311279A1

公开(公告)日：2019-10-10

申请号：US15946884

申请日：2018-04-06

Applicant: Adobe Inc.

Inventor： Atanu R. Sinha ,  Deepali Jain ,  Nikhil Sheoran ,  Deepali Gupta ,  Sopan Khosla

IPC: G06N7/00 ,  G06K9/62 ,  G06N99/00 ,  G06F17/11

Abstract: In some embodiments, a computing system computes, with a state prediction model, probabilities of transitioning from a click state represented by interaction data to various predicted next states. The computing system computes an interface experience metric for the click with an experience valuation model. To do so, the computing system identifies base values for the click state and the predicted next states. The computing system computes value differentials for between the click state's base value and each predicted next state's base value. Value differentials indicate qualities of interface experience. The computing system determines the interface experience metric from a summation that includes the current click state's base value and the value differentials weighted with the predicted next states' probabilities. The computing system transmits the interface experience metric to an online platform, which can cause interface elements of the online platform to be modified based on the interface experience metric.