公开(公告)号：US11861464B2

公开(公告)日：2024-01-02

申请号：US16670543

申请日：2019-10-31

Applicant: Adobe Inc.

Inventor： Ritwik Sinha ,  Sunny Dhamnani

IPC: G06N20/00 ,  G06F30/20 ,  G06F18/21 ,  G06N7/01 ,  G06F18/2113

CPC classification number: G06N20/00 ,  G06F18/2113 ,  G06F30/20 ,  G06N7/01

Abstract: This disclosure involves generating graph data structures that model inter-feature dependencies for use with machine-learning models to predict end-user behavior. For example, a processing device receives an input dataset and a request to modify a first input feature of the input dataset. The processing device uses a graph data structure that models the inter-feature dependencies to modify the input dataset by propagating the modification of the first input feature to a second input feature dependent on the first input feature. The modification to the second input feature is a function of at least (a) the value of the first input feature and (b) a weight assigned to an edge linking the first input feature to the second input feature within the directed graph. The processing device then applies a trained machine-learning model to the modified input dataset to generate a prediction of an outcome.

公开(公告)号：US11669755B2

公开(公告)日：2023-06-06

申请号：US16921202

申请日：2020-07-06

Applicant: Adobe Inc.

Inventor： Atanu R Sinha ,  Tanay Asija ,  Sunny Dhamnani ,  Raja Kumar Dubey ,  Navita Goyal ,  Kaarthik Raja Meenakshi Viswanathan ,  Georgios Theocharous

IPC: G06N5/04 ,  G06N20/00 ,  G06F9/451

CPC classification number: G06N5/04 ,  G06F9/451 ,  G06N20/00

Abstract: The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining a cognitive, action-selection bias of a user that influences how the user will select a sequence of digital actions for execution of a task. For example, the disclosed systems can identify, from a digital behavior log of a user, a set of digital action sequences that correspond to a set of sessions for a task previously executed by the user. The disclosed systems can utilize a machine learning model to analyze the set of sessions to generate session weights. The session weights can correspond to an action-selection bias that indicates an extent to which a future session for the task executed by the user is predicted to be influenced by the set of sessions. The disclosed systems can provide a visual indication of the action-selection bias of the user for display on a graphical user interface.

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：US10785318B2

公开(公告)日：2020-09-22

申请号：US15793001

申请日：2017-10-25

Applicant: Adobe Inc.

Inventor： Sunny Dhamnani ,  Vishwa Vinay ,  Lilly Kumari ,  Ritwik Sinha

IPC: G06F15/173 ,  H04L29/08 ,  H04L29/06 ,  G06K9/62

Abstract: A session identification system classifies network sessions with a network application as either human-generated or generated by a non-human, such as by a bot. In an embodiment, the session identification system receives a set of unlabeled network sessions, and determines a label for a single class of the unlabeled network sessions. Based on the one-class labeling information, the session identification system determines multiple subsets of the unlabeled network sessions. Multiple classifiers included in the session identification system generate probabilities describing each of the unlabeled network sessions. The session identification system classifies each of the unlabeled network sessions based on a combination of the generated probabilities.

公开(公告)号：US20200293836A1

公开(公告)日：2020-09-17

申请号：US16353076

申请日：2019-03-14

Applicant: Adobe Inc.

Inventor： Sunny Dhamnani ,  Dhruv Singal ,  Ritwik Sinha

IPC: G06K9/62 ,  G06N5/02 ,  G06N20/00 ,  G06F16/901

Abstract: An IDS generator determines multiple classes for electronic data items. The IDS generator determines, for each class, a class-specific candidate ruleset. The IDS generator performs a differential analysis of each class-specific candidate ruleset. The differential analysis is based on differences between result values of a scoring objective function. In some cases, the differential analysis determines at least one of the differences based on additional data structures, such as an augmented frequent-pattern tree. A probability function based on the differences is compared to a threshold probability At least one testing ruleset is modified based on the comparison. The IDS generator determines, for each class, a class-specific optimized ruleset based on the differential analysis of each class-specific candidate ruleset. The IDS generator creates an optimized interpretable decision set based on combined class-specific optimized rulesets for the multiple classes.

公开(公告)号：US20200245009A1

公开(公告)日：2020-07-30

申请号：US16257571

申请日：2019-01-25

Applicant: Adobe Inc.

Inventor： Shiv Kumar Saini ,  Sunny Dhamnani ,  Prithviraj Abasaheb Chavan ,  AS Akil Arif Ibrahim ,  Aakash Srinivasan

IPC: H04N21/25 ,  G06N20/20 ,  H04N21/81 ,  H04N21/6379 ,  G06N7/00

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for training and utilizing a generative machine learning model to select one or more treatments for a client device from a set of treatments based on digital characteristics corresponding to the client device. In particular, the disclosed systems can train and apply a variational autoencoder with a task embedding layer that generates estimated effects for treatment combinations. For example, the disclosed systems receive, as input, digital characteristics corresponding to the client device and various treatment combinations. The disclosed systems apply the trained generative machine learning model with the task embedding layer to the digital characteristics to generate effect estimations for the various treatment combinations. Based on the effect estimations for the treatment combinations, the disclosed systems select one or more treatments to provide to the client device.

公开(公告)号：US11704591B2

公开(公告)日：2023-07-18

申请号：US16353076

申请日：2019-03-14

Applicant: Adobe Inc.

Inventor： Sunny Dhamnani ,  Dhruv Singal ,  Ritwik Sinha

IPC: G06N20/00 ,  G06N5/025 ,  G06F16/901 ,  G06F18/243 ,  G06F18/2115 ,  G06N7/01

CPC classification number: G06N20/00 ,  G06F16/9014 ,  G06F18/2115 ,  G06F18/24323 ,  G06N5/025 ,  G06N7/01

Abstract: An IDS generator determines multiple classes for electronic data items. The IDS generator determines, for each class, a class-specific candidate ruleset. The IDS generator performs a differential analysis of each class-specific candidate ruleset. The differential analysis is based on differences between result values of a scoring objective function. In some cases, the differential analysis determines at least one of the differences based on additional data structures, such as an augmented frequent-pattern tree. A probability function based on the differences is compared to a threshold probability At least one testing ruleset is modified based on the comparison. The IDS generator determines, for each class, a class-specific optimized ruleset based on the differential analysis of each class-specific candidate ruleset. The IDS generator creates an optimized interpretable decision set based on combined class-specific optimized rulesets for the multiple classes.

公开(公告)号：US20220108334A1

公开(公告)日：2022-04-07

申请号：US17060723

申请日：2020-10-01

Applicant: ADOBE INC.

Inventor： AYUSH CHAUHAN ,  Aditya Anand ,  Sunny Dhamnani ,  Shaddy Garg ,  Shiv Kumar Saini

IPC: G06Q30/02 ,  G06N3/04 ,  G06N3/08 ,  G06Q10/06

Abstract: Systems and methods for data analytics are described. The systems and methods include receiving attribute data for at least one user, identifying a plurality of precursor events causally related to an observable target interaction with the at least one user, wherein at least one of the precursor events comprises a marketing event, predicting a probability for each of the precursor events based on the attribute data using a neural network trained with a first loss function comparing individual level training data for the observable target interaction, and performing the marketing event directed to the at least one user based at least in part on the predicted probabilities.

公开(公告)号：US11109083B2

公开(公告)日：2021-08-31

申请号：US16257571

申请日：2019-01-25

Applicant: Adobe Inc.

Inventor： Shiv Kumar Saini ,  Sunny Dhamnani ,  Prithviraj Abasaheb Chavan ,  A S Akil Arif Ibrahim ,  Aakash Srinivasan

IPC: H04N21/25 ,  G06N20/20 ,  G06N7/00 ,  H04N21/6379 ,  H04N21/81

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for training and utilizing a generative machine learning model to select one or more treatments for a client device from a set of treatments based on digital characteristics corresponding to the client device. In particular, the disclosed systems can train and apply a variational autoencoder with a task embedding layer that generates estimated effects for treatment combinations. For example, the disclosed systems receive, as input, digital characteristics corresponding to the client device and various treatment combinations. The disclosed systems apply the trained generative machine learning model with the task embedding layer to the digital characteristics to generate effect estimations for the various treatment combinations. Based on the effect estimations for the treatment combinations, the disclosed systems select one or more treatments to provide to the client device.

公开(公告)号：US11995520B2

公开(公告)日：2024-05-28

申请号：US16520645

申请日：2019-07-24

Applicant: Adobe Inc.

Inventor： Ritwik Sinha ,  Sunny Dhamnani ,  Moumita Sinha

IPC: G06N20/00 ,  G06F16/904 ,  G06N5/045 ,  G06N20/20

CPC classification number: G06N20/00 ,  G06F16/904 ,  G06N5/045 ,  G06N20/20

Abstract: The present disclosure relates to a feature contribution system that accurately and efficiently provides the influence of features utilized in machine-learning models with respect to observed model results. In particular, the feature contribution system can utilize an observed model result, initial contribution values, and historical feature values to determine a contribution value correction factor. Further, the feature contribution system can apply the correction factor to the initial contribution values to determine correction-factor adjusted contribution values of each feature of the model with respect to the observed model result.