公开(公告)号：US20230261966A1

公开(公告)日：2023-08-17

申请号：US17671075

申请日：2022-02-14

Applicant: ADOBE INC.

Inventor： Georgios Theocharous ,  Kai Wang ,  Zhao Song ,  Sridhar Mahadevan

IPC: H04L45/00 ,  H04L41/147

CPC classification number: H04L45/08 ,  H04L41/147

Abstract: A control system facilitates active management of a streaming data system. Given historical data traffic for each data stream processed by a streaming data system, the control system uses a machine learning model to predict future data traffic for each data stream. The control system selects a matching between data streams and servers for a future time that minimizes a cost comprising a switching cost and a server imbalance cost based on the predicted data traffic for the future time. In some configurations, the matching is selected using a planning window comprising a number of future time steps dynamically selected based on uncertainty associated with the predicted data traffic. Given the selected matching, the control system may manage the streaming data system by causing data streams to be moved between servers based on the matching.

公开(公告)号：US12047273B2

公开(公告)日：2024-07-23

申请号：US17671075

申请日：2022-02-14

Applicant: ADOBE INC.

Inventor： Georgios Theocharous ,  Kai Wang ,  Zhao Song ,  Sridhar Mahadevan

IPC: H04L45/02 ,  H04L41/147

CPC classification number: H04L45/08 ,  H04L41/147

Abstract: A control system facilitates active management of a streaming data system. Given historical data traffic for each data stream processed by a streaming data system, the control system uses a machine learning model to predict future data traffic for each data stream. The control system selects a matching between data streams and servers for a future time that minimizes a cost comprising a switching cost and a server imbalance cost based on the predicted data traffic for the future time. In some configurations, the matching is selected using a planning window comprising a number of future time steps dynamically selected based on uncertainty associated with the predicted data traffic. Given the selected matching, the control system may manage the streaming data system by causing data streams to be moved between servers based on the matching.

公开(公告)号：US12001520B2

公开(公告)日：2024-06-04

申请号：US17485780

申请日：2021-09-27

Applicant: Adobe Inc.

Inventor： Ritwik Sinha ,  Sridhar Mahadevan ,  Moumita Sinha ,  Md Mehrab Tanjim ,  Krishna Kumar Singh ,  David Arbour

IPC: G06K9/00 ,  G06F18/214 ,  G06F18/28 ,  G06N3/045

CPC classification number: G06F18/28 ,  G06F18/2148 ,  G06N3/045

Abstract: Methods and systems disclosed herein relate generally to systems and methods for generating simulated images for enhancing socio-demographic diversity. An image-generating application receives a request that includes a set of target socio-demographic attributes. The set of target socio-demographic attributes can define a gender, age, and/or race of a subject that are non-stereotypical for a particular occupation. The image-generating application applies the a machine-learning model to the set of target socio-demographic attributes. The machine-learning model generates a simulated image depicts a subject having visual characteristics that are defined by the set of target socio-demographic attributes.

公开(公告)号：US12159482B2

公开(公告)日：2024-12-03

申请号：US17652026

申请日：2022-02-22

Applicant: ADOBE INC.

Inventor： Md Mehrab Tanjim ,  Ritwik Sinha ,  Moumita Sinha ,  David Thomas Arbour ,  Sridhar Mahadevan

IPC: G06V40/16

Abstract: Systems and methods for diversity auditing are described. The systems and methods include identifying a plurality of images; detecting a face in each of the plurality of images using a face detection network; classifying the face in each of the plurality of images based on a sensitive attribute using an image classification network; generating a distribution of the sensitive attribute in the plurality of images based on the classification; and computing a diversity score for the plurality of images based on the distribution.

公开(公告)号：US20220121968A1

公开(公告)日：2022-04-21

申请号：US17072868

申请日：2020-10-16

Applicant: Adobe Inc.

Inventor： Yash Chandak ,  Georgios Theocharous ,  Sridhar Mahadevan

IPC: G06N5/04 ,  G06Q10/06 ,  G06Q10/10

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that determine target policy parameters that enable target policies to provide improved future performance, even in circumstances where the underlying environment is non-stationary. For example, in one or more embodiments, the disclosed systems utilize counter-factual reasoning to estimate what the performance of the target policy would have been if implemented during past episodes of action-selection. Based on the estimates, the disclosed systems forecast a performance of the target policy for one or more future decision episodes. In some implementations, the disclosed systems further determine a performance gradient for the forecasted performance with respect to varying a target policy parameter for the target policy. In some cases, the disclosed systems use the performance gradient to efficiently modify the target policy parameter, without undergoing the computational expense of expressly modeling variations in underlying environmental functions.

公开(公告)号：US20230281680A1

公开(公告)日：2023-09-07

申请号：US17652939

申请日：2022-03-01

Applicant: ADOBE INC.

Inventor： Michail Mamakos ,  Sridhar Mahadevan ,  Viswanathan Swaminathan ,  Mariette Philippe Souppe ,  Ritwik Sinha ,  Saayan Mitra ,  Zhao Song

IPC: G06Q30/02 ,  G06Q10/06 ,  G06F9/50

CPC classification number: G06Q30/0283 ,  G06Q10/06313 ,  G06F9/5033

Abstract: Systems and methods for resource allocation are described. The systems and methods include receiving utilization data for computing resources shared by a plurality of users, updating a pricing agent using a reinforcement learning model based on the utilization data, identifying resource pricing information using the pricing agent, and allocating the computing resources to the plurality of users based on the resource pricing information.

公开(公告)号：US20230267764A1

公开(公告)日：2023-08-24

申请号：US17652026

申请日：2022-02-22

Applicant: ADOBE INC.

Inventor： Md Mehrab Tanjim ,  Ritwik Sinha ,  Moumita Sinha ,  David Thomas Arbour ,  Sridhar Mahadevan

IPC: G06V40/16

CPC classification number: G06V40/172

Abstract: Systems and methods for diversity auditing are described. The systems and methods include identifying a plurality of images; detecting a face in each of the plurality of images using a face detection network; classifying the face in each of the plurality of images based on a sensitive attribute using an image classification network; generating a distribution of the sensitive attribute in the plurality of images based on the classification; and computing a diversity score for the plurality of images based on the distribution.

公开(公告)号：US11270369B2

公开(公告)日：2022-03-08

申请号：US16779074

申请日：2020-01-31

Applicant: Adobe Inc.

Inventor： Georgios Theocharous ,  Sridhar Mahadevan ,  Anup Bandigadi Rao

IPC: G06Q30/00 ,  G06Q30/06 ,  H04L67/50 ,  G06F16/9535

Abstract: In implementations of systems for generating recommendations, a computing device implements a recommendation system to receive prior interaction data describing prior interactions of entities with items. The recommendation system processes the prior interaction data and segments the entities into a first set and a second set. The entities included in the first set have greater numbers of prior interactions with the items than the entities included in the second set. The recommendation system then generates subset data describing a subset of the entities in the first set. This subset excludes entities having numbers of the prior interactions with the items below a threshold. The recommendation system forms a recommendation model based on the subset data and the system uses the recommendation model to generate a recommendation for display in a user interface.

公开(公告)号：US20210241346A1

公开(公告)日：2021-08-05

申请号：US16779074

申请日：2020-01-31

Applicant: Adobe Inc.

Inventor： Georgios Theocharous ,  Sridhar Mahadevan ,  Anup Bandigadi Rao

IPC: G06Q30/06 ,  H04L29/08 ,  G06F16/9535

Abstract: In implementations of systems for generating recommendations, a computing device implements a recommendation system to receive prior interaction data describing prior interactions of entities with items. The recommendation system processes the prior interaction data and segments the entities into a first set and a second set. The entities included in the first set have greater numbers of prior interactions with the items than the entities included in the second set. The recommendation system then generates subset data describing a subset of the entities in the first set. This subset excludes entities having numbers of the prior interactions with the items below a threshold. The recommendation system forms a recommendation model based on the subset data and the system uses the recommendation model to generate a recommendation for display in a user interface.

公开(公告)号：US20230281642A1

公开(公告)日：2023-09-07

申请号：US17653157

申请日：2022-03-02

Applicant: ADOBE INC.

Inventor： Shiv Shankar ,  Sridhar Mahadevan ,  Moumita Sinha ,  Ritwik Sinha ,  Saayan Mitra ,  Viswanathan Swaminathan ,  Erin Davis

IPC: G06Q30/02

CPC classification number: G06Q30/0201

Abstract: A system and method for content distribution without tracking is described. The system and method includes determining that device identifiers are not available for a first digital content channel; identifying a first cluster of users and a second cluster of users based on the determination that device identifiers are not available; providing first content and second content via the first digital content channel; monitoring user interactions on the first digital content channel to obtain a first conversion rate for users in the first cluster that receive the first content and a second conversion rate for users in the second cluster that receive the second content; computing a cross-cluster treatment effect based on the first conversion rate and the second conversion rate; computing a treatment effect for the first content based on the cross-cluster treatment effect; and providing the first content to a subsequent user based on the treatment effect.