公开(公告)号：US20220366299A1

公开(公告)日：2022-11-17

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

IPC: G06N20/00 ,  G06F3/0484 ,  H04L29/08

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：US12182524B2

公开(公告)日：2024-12-31

申请号：US17453562

申请日：2021-11-04

Applicant: ADOBE INC.

Inventor： Jianguo Zhang ,  Trung Huu Bui ,  Seunghyun Yoon ,  Xiang Chen ,  Quan Hung Tran ,  Walter W. Chang

IPC: G06F40/40 ,  G06F40/284 ,  G06F40/30 ,  G06V30/19

Abstract: Systems and methods for natural language processing are described. One or more aspects of a method, apparatus, and non-transitory computer readable medium include receiving a text phrase; encoding the text phrase using an encoder to obtain a hidden representation of the text phrase, wherein the encoder is trained during a first training phrase using self-supervised learning based on a first contrastive loss and during a second training phrase using supervised learning based on a second contrastive learning loss; identifying an intent of the text phrase from a predetermined set of intent labels using a classification network, wherein the classification network is jointly trained with the encoder in the second training phase; and generating a response to the text phrase based on the intent.

公开(公告)号：US20240273378A1

公开(公告)日：2024-08-15

申请号：US18163624

申请日：2023-02-02

Applicant: ADOBE INC.

Inventor： Saayan Mitra ,  Arash Givchi ,  Xiang Chen ,  Somdeb Sarkhel ,  Ryan A. Rossi ,  Zhao Song

IPC: G06N3/098 ,  G06N3/092

CPC classification number: G06N3/098 ,  G06N3/092

Abstract: Systems and methods for distributed machine learning are provided. According to one aspect, a method for distributed machine learning includes obtaining, by an edge device, a static machine learning model from a hub device, computing, by the edge device, an objective function for a dynamic machine learning model based on a relationship between the dynamic machine learning model and the static machine learning model, and updating, by the edge device, the dynamic machine learning model based on the objective function.

公开(公告)号：US20230022396A1

公开(公告)日：2023-01-26

申请号：US17367134

申请日：2021-07-02

Applicant: Adobe Inc.

Inventor： Saayan Mitra ,  Xiang Chen ,  Vahid Azizi

IPC: G06Q30/06 ,  G06K9/62

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize collaborative filtering and a reinforcement learning model having an actor-critic framework to provide digital content items across client devices. In particular, in one or more embodiments, the disclosed systems monitor interactions of a client device with one or more digital content items to generate item embeddings (e.g., utilizing a collaborative filtering model). The disclosed systems further utilize a reinforcement learning model to generate a recommendation (e.g., determine one or more additional digital content items to provide to the client device) based on the user interactions. In some implementations, the disclosed systems utilize the reinforcement learning model to analyze every negative and positive interaction observed when generating the recommendation. Further, the disclosed systems utilize the reinforcement learning model to analyze item embeddings, which encode the relationships among the digital content items, when generating the recommendation.

公开(公告)号：US20220245446A1

公开(公告)日：2022-08-04

申请号：US17164111

申请日：2021-02-01

Applicant: ADOBE INC.

Inventor： Saayan Mitra ,  Xiang Chen ,  Akangsha Sunil Bedmutha ,  Viswanathan Swaminathan ,  Omar Rahman ,  Camille Girabawe

IPC: G06N3/08 ,  G06N3/04

Abstract: An improved electronic communication system schedules transmission of electronic communications based on a predicted open time and click time. The open and click times are predicted from a machine learning model that is trained to optimize for both tasks. Additionally, when training the machine learning model, the loss used for adjusting the system to achieve a desired accuracy may be a biased loss determined from a function that penalizes overpredicting the open time. As such, the loss value may be determined by different set of rules depending on whether the predicted time is greater than the actual time or not.

公开(公告)号：US20210110432A1

公开(公告)日：2021-04-15

申请号：US16598933

申请日：2019-10-10

Applicant: Adobe Inc.

Inventor： Xiang Chen ,  Viswanathan Swaminathan ,  Somdeb Sarkhel

IPC: G06Q30/02 ,  G06F16/28 ,  G06F16/22

Abstract: Automatic item placement recommendation is described. An item placement configuration system receives an item for which a recommended placement is to be generated and identifies an entity associated with the item. The item placement configuration system then identifies a multi-domain taxonomy that describes relationships between different entities based on items associated with the different entities published among different domains. A representation of the entity associated with the item to be placed is then identified within the multi-domain taxonomy, along with a representation of at least one similar entity. Upon identifying a similar entity, historic item placement metrics for the similar entity are leveraged to generate a placement recommendation for the received item. In some implementations, the placement recommendation is output with a visual indication of a similar entity and associated performance metrics that were considered in generating the recommended placement.