公开(公告)号：US20250147973A1

公开(公告)日：2025-05-08

申请号：US18504256

申请日：2023-11-08

Applicant: ADOBE INC.

Inventor： Tong Yu ,  Xiang Chen ,  Victor Soares Bursztyn ,  Uttaran Bhattacharya ,  Eunyee Koh ,  Saayan Mitra ,  Alexandru Ionut Hodorogea ,  Kenneth Russell ,  Saurabh Tripathy ,  Manas Garg

IPC: G06F16/2457 ,  G06F16/93 ,  G06N20/20

Abstract: A method, apparatus, non-transitory computer readable medium, and system for document retrieval include obtaining a query and a document. A prompt generator generates a prompt for a reasoning model based on the query and the document. The reasoning model generates a reasoning result based on the prompt. In some cases, the reasoning result indicates that the document answers the query. A machine learning model provides the document in response to the query based on the reasoning result.

公开(公告)号：US12050647B2

公开(公告)日：2024-07-30

申请号：US17877469

申请日：2022-07-29

Applicant: Adobe Inc.

Inventor： Somdeb Sarkhel ,  Xiang Chen ,  Viswanathan Swaminathan ,  Swapneel Mehta ,  Saayan Mitra ,  Ryan Rossi ,  Han Guo ,  Ali Aminian ,  Kshitiz Garg

IPC: G06F16/90 ,  G06F16/901 ,  G06N3/045 ,  G06Q50/00

CPC classification number: G06F16/9024 ,  G06N3/045 ,  G06Q50/01

Abstract: Techniques for recommending hashtags, including trending hashtags, are disclosed. An example method includes accessing a graph. The graph includes video nodes representing videos, historical hashtag nodes representing historical hashtags, and edges indicating associations among the video nodes and the historical hashtag nodes. A trending hashtag is identified. An edge is added to the graph between a historical hashtag node representing a historical hashtag and a trending hashtag node representing the trending hashtag, based on a semantic similarity between the historical hashtag and the trending hashtag. A new video node representing a new video is added to the video nodes of the graph. A graph neural network (GNN) is applied to the graph, and the GNN predicts a new edge between the trending hashtag node and the new video node. The trending hashtag is recommended for the new video based on prediction of the new edge.

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：US20250086495A1

公开(公告)日：2025-03-13

申请号：US18367393

申请日：2023-09-12

Applicant: Adobe Inc.

Inventor： Saayan Mitra ,  Xiang Chen ,  Sapthotharan Krishnan Nair ,  Renzhi Wu ,  Anup Rao

IPC: G06N20/00

Abstract: An edge node included in a decentralized edge computing network generates a federated partial-data aggregation machine learning model. The edge node learns one or more model parameters via machine learning techniques and receives one or more auxiliary model parameters from additional edge nodes in the decentralized edge computing network, such as from a neighbor node group. In some cases, a neighbor node is identified in response to determining that the neighbor node includes a model with a relatively high estimated relevance to the model of the edge node. The edge node modifies the model to include an aggregation of the learned model parameters and the received auxiliary parameters. Respective weights are learned for the learned model parameters and also for the received auxiliary parameters. During training to learn the respective weights, the edge node stabilizes the learned model parameters and the received auxiliary parameters.

公开(公告)号：US20240152605A1

公开(公告)日：2024-05-09

申请号：US17983687

申请日：2022-11-09

Applicant: Adobe Inc.

Inventor： Xiang Chen ,  Yifu Zheng ,  Viswanathan Swaminathan ,  Sreekanth Reddy ,  Saayan Mitra ,  Ritwik Sinha ,  Niranjan Kumbi ,  Alan Lai

IPC: G06F21/55

CPC classification number: G06F21/554 ,  G06F2221/034

Abstract: In some embodiments, techniques for identifying email events generated by bot activity are provided. For example, a process may involve applying bot detection patterns to identify bot activity among email response events.

公开(公告)号：US20240144307A1

公开(公告)日：2024-05-02

申请号：US18047421

申请日：2022-10-18

Applicant: ADOBE INC.

Inventor： Tung Mai ,  Ritwik Sinha ,  Trevor Hyrum Paulsen ,  Xiang Chen ,  William Brandon George ,  Nate Purser ,  Zhao Song

IPC: G06Q30/02

CPC classification number: G06Q30/0204

Abstract: One aspect of systems and methods for segment size estimation includes identifying a segment of users for a first time period based on time series data, wherein the time series data includes a series of interactions between users and a content channel and wherein the segment includes a portion of the users interacting with the content channel during the first time period; computing a segment return value for a second time period based on the time series data by computing a first subset and a second subset of the segment, wherein the first subset includes users that interact with the content channel greater than a threshold number of times during a range of the time series data and the second subset comprises a complement of the first subset with respect to the segment; and providing customized content to a user in the segment based on the segment return value.

公开(公告)号：US20240029107A1

公开(公告)日：2024-01-25

申请号：US18478856

申请日：2023-09-29

Applicant: Adobe Inc.

Inventor： Xiang Chen ,  Viswanathan Swaminathan ,  Somdeb Sarkhel

IPC: G06Q30/0251 ,  G06F16/28 ,  G06F16/22 ,  G06Q30/0241

CPC classification number: G06Q30/0254 ,  G06F16/285 ,  G06F16/2237 ,  G06Q30/0255 ,  G06Q30/0277 ,  G06Q30/0261 ,  G06Q30/0264 ,  G06F16/2264

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.

公开(公告)号：US11810152B2

公开(公告)日：2023-11-07

申请号：US16598933

申请日：2019-10-10

Applicant: Adobe Inc.

Inventor： Xiang Chen ,  Viswanathan Swaminathan ,  Somdeb Sarkhel

IPC: G06Q30/0251 ,  G06F16/28 ,  G06F16/22 ,  G06Q30/0241

CPC classification number: G06Q30/0254 ,  G06F16/2237 ,  G06F16/2264 ,  G06F16/285 ,  G06Q30/0255 ,  G06Q30/0261 ,  G06Q30/0264 ,  G06Q30/0277

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.

公开(公告)号：US20230136527A1

公开(公告)日：2023-05-04

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 ,  G06F40/30 ,  G06F40/284 ,  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：US20240037149A1

公开(公告)日：2024-02-01

申请号：US17877469

申请日：2022-07-29

Applicant: Adobe Inc.

Inventor： Somdeb Sarkhel ,  Xiang Chen ,  Viswanathan Swaminathan ,  Swapneel Mehta ,  Saayan Mitra ,  Ryan Rossi ,  Han Guo ,  Ali Aminian ,  Kshitiz Garg

IPC: G06F16/901 ,  G06N3/04 ,  G06Q50/00

CPC classification number: G06F16/9024 ,  G06N3/0454 ,  G06Q50/01

Abstract: Techniques for recommending hashtags, including trending hashtags, are disclosed. An example method includes accessing a graph. The graph includes video nodes representing videos, historical hashtag nodes representing historical hashtags, and edges indicating associations among the video nodes and the historical hashtag nodes. A trending hashtag is identified. An edge is added to the graph between a historical hashtag node representing a historical hashtag and a trending hashtag node representing the trending hashtag, based on a semantic similarity between the historical hashtag and the trending hashtag. A new video node representing a new video is added to the video nodes of the graph. A graph neural network (GNN) is applied to the graph, and the GNN predicts a new edge between the trending hashtag node and the new video node. The trending hashtag is recommended for the new video based on prediction of the new edge.