公开(公告)号：US20250036858A1

公开(公告)日：2025-01-30

申请号：US18225906

申请日：2023-07-25

Applicant: Adobe Inc.

Inventor： Ryan Rossi ,  Ryan Aponte ,  Shunan Guo ,  Nedim Lipka ,  Jane Hoffswell ,  Chang Xiao ,  Eunyee Koh ,  Yeuk-yin Chan

IPC: G06F40/154 ,  G06F40/117 ,  G06F40/143

Abstract: Techniques discussed herein generally relate to applying machine-learning techniques to design documents to determine relationships among the different style elements within the document. In one example, hypergraph model is trained on a corpus of hypertext markup language (HTML) documents. The trained model is utilized to identifying one or more candidate style elements for a candidate fragment and/or a candidate fragment. Each of the candidates are scored, and at least a portion of the scored candidates are presented as design options for generating a new document.

公开(公告)号：US11709690B2

公开(公告)日：2023-07-25

申请号：US16812962

申请日：2020-03-09

Applicant: Adobe Inc.

Inventor： Nedim Lipka ,  Doo Soon Kim

IPC: G06F3/0484 ,  G06N3/04 ,  G06F40/279 ,  G06F9/451

CPC classification number: G06F9/453 ,  G06F3/0484 ,  G06F40/279 ,  G06N3/04

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating coachmarks and concise instructions based on operation descriptions for performing application operations. For example, the disclosed systems can utilize a multi-task summarization neural network to analyze an operation description and generate a coachmark and a concise instruction corresponding to the operation description. In addition, the disclosed systems can provide a coachmark and a concise instruction for display within a user interface to, directly within a client application, guide a user to perform an operation by interacting with a particular user interface element.

公开(公告)号：US20230030341A1

公开(公告)日：2023-02-02

申请号：US17383114

申请日：2021-07-22

Applicant: Adobe Inc.

Inventor： Eunyee Koh ,  Tak Yeon Lee ,  Andrew Thomson ,  Vasanthi Holtcamp ,  Ryan Rossi ,  Fan Du ,  Caroline Kim ,  Tong Yu ,  Shunan Guo ,  Nedim Lipka ,  Shriram Venkatesh Shet Revankar ,  Nikhil Belsare

IPC: G06N3/08 ,  H04L12/26 ,  G06F40/186 ,  G06N3/04

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a dynamic user interface and machine learning tools to generate data-driven digital content and multivariate testing recommendations for distributing digital content across computer networks. In particular, in one or more embodiments, the disclosed systems utilize machine learning models to generate digital recommendations at multiple development stages of digital communications that are targeted on particular performance metrics. For example, the disclosed systems utilize historical information and recipient profile data to generate recommendations for digital communication templates, fragment variants of content fragments, and content variants of digital content items. Ultimately, the disclosed systems generate multivariate testing recommendations incorporating selected fragment variants to intelligently narrow multivariate testing candidates and generate more meaningful and statistically significant multivariate testing results.

公开(公告)号：US11210470B2

公开(公告)日：2021-12-28

申请号：US16368334

申请日：2019-03-28

Applicant: ADOBE INC.

Inventor： Seokhwan Kim ,  Walter W. Chang ,  Nedim Lipka ,  Franck Dernoncourt ,  Chan Young Park

IPC: G06F40/30 ,  G06N3/08

Abstract: Methods and systems are provided for identifying subparts of a text. A neural network system can receive a set of sentences that includes context sentences and target sentences that indicate a decision point in a text. The neural network system can generate context vector sentences and target sentence vectors by encoding context from the set of sentences. These context sentence vectors can be weighted to focus on relevant information. The weighted context sentence vectors and the target sentence vectors can then be used to output a label for the decision point in the text.

公开(公告)号：US20240311406A1

公开(公告)日：2024-09-19

申请号：US18482754

申请日：2023-10-06

Applicant: ADOBE INC.

Inventor： Arpit Narechania ,  Fan Du ,  Atanu Sinha ,  Nedim Lipka ,  Alexa F. Siu ,  Jane Elizabeth Hoffswell ,  Eunyee Koh ,  Vasanthi Holtcamp

IPC: G06F16/332 ,  G06F16/338 ,  G06F40/205

CPC classification number: G06F16/3329 ,  G06F16/338 ,  G06F40/205

Abstract: Aspects of a method, apparatus, non-transitory computer readable medium, and system include obtaining a document and a query. A plurality of data elements are identified from the document by locating a plurality of corresponding flexible anchor elements. Then, the data elements are extracted based on the plurality of flexible anchor elements. Content including an analysis of the extracted data elements based on the query is generated.

公开(公告)号：US20240161529A1

公开(公告)日：2024-05-16

申请号：US18055752

申请日：2022-11-15

Applicant: Adobe Inc.

Inventor： Vlad Morariu ,  Puneet Mathur ,  Rajiv Jain ,  Ashutosh Mehra ,  Jiuxiang Gu ,  Franck Dernoncourt ,  Anandhavelu N ,  Quan Tran ,  Verena Kaynig-Fittkau ,  Nedim Lipka ,  Ani Nenkova

IPC: G06V30/413 ,  G06V10/82

CPC classification number: G06V30/413 ,  G06V10/82

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate a digital document hierarchy comprising layers of parent-child element relationships from the visual elements. For example, for a layer of the layers, the disclosed systems determine, from the visual elements, candidate parent visual elements and child visual elements. In addition, for the layer of the layers, the disclosed systems generate, from the feature embeddings utilizing a neural network, element classifications for the candidate parent visual elements and parent-child element link probabilities for the candidate parent visual elements and the child visual elements. Moreover, for the layer, the disclosed systems select parent visual elements from the candidate parent visual elements based on the parent-child element link probabilities. Further, the disclosed systems utilize the digital document hierarchy to generate an interactive digital document from the digital document image.

公开(公告)号：US20230368265A1

公开(公告)日：2023-11-16

申请号：US17743360

申请日：2022-05-12

Applicant: Adobe Inc.

Inventor： Ryan A. Rossi ,  Aravind Reddy Talla ,  Zhao Song ,  Anup Rao ,  Tung Mai ,  Nedim Lipka ,  Gang Wu ,  Anup Rao

IPC: G06Q30/06

CPC classification number: G06Q30/0631 ,  G06Q30/0629 ,  G06Q30/0643

Abstract: Embodiments provide systems, methods, and computer storage media for a Nonsymmetric Determinantal Point Process (NDPPs) for compatible set recommendations in a setting where data representing entities (e.g., items) arrives in a stream. A stream representing compatible sets of entities is received and used to update a latent representation of the entities and a compatibility distribution indicating likelihood of compatibility of subsets of the entities. The probability distribution is accessed in a single sequential pass to predict a compatible complete set of entities that completes an incomplete set of entities. The predicted complete compatible set is provided a recommendation for entities that complete the incomplete set of entities.

公开(公告)号：US11768869B2

公开(公告)日：2023-09-26

申请号：US17170520

申请日：2021-02-08

Applicant: ADOBE INC.

Inventor： Nedim Lipka ,  Seyedsaed Rezayidemne ,  Vishwa Vinay ,  Ryan Rossi ,  Franck Dernoncourt ,  Tracy Holloway King

IPC: G06F16/50 ,  G06N5/02 ,  G06F40/20 ,  G06F16/532 ,  G06F16/55 ,  G06F16/56

CPC classification number: G06F16/532 ,  G06F16/55 ,  G06F16/56 ,  G06F40/20 ,  G06N5/02

Abstract: The present disclosure describes systems and methods for information retrieval. Embodiments of the disclosure provide a retrieval network that leverages external knowledge to provide reformulated search query suggestions, enabling more efficient network searching and information retrieval. For example, a search query from a user (e.g., a query mention of a knowledge graph entity that is included in a search query from a user) may be added to a knowledge graph as a surrogate entity via entity linking. Embedding techniques are then invoked on the updated knowledge graph (e.g., the knowledge graph that includes additional edges between surrogate entities and other entities of the original knowledge graph), and entities neighboring the surrogate entity are retrieved based on the embedding (e.g., based on a computed distance between the surrogate entity and candidate entities in the embedding space). Search results can then be ranked and displayed based on relevance to the neighboring entity.

公开(公告)号：US20220309334A1

公开(公告)日：2022-09-29

申请号：US17210157

申请日：2021-03-23

Applicant: Adobe Inc.

Inventor： Ryan Rossi ,  Tung Mai ,  Nedim Lipka ,  Jiong Zhu ,  Anup Rao ,  Viswanathan Swaminathan

IPC: G06N3/08 ,  G06F16/901 ,  G06N5/02 ,  G06K9/62

Abstract: Techniques are provided for training graph neural networks with heterophily datasets and generating predictions for such datasets with heterophily. A computing device receives a dataset including a graph data structure and processes the dataset using a graph neural network. The graph neural network defines prior belief vectors respectively corresponding to nodes of the graph data structure, executes a compatibility-guided propagation from the set of prior belief vectors and using a compatibility matrix. The graph neural network predicts predicting a class label for a node of the graph data structure based on the compatibility-guided propagations and a characteristic of at least one node within a neighborhood of the node. The computing device outputs the graph data structure where it is usable by a software tool for modifying an operation of a computing environment.

公开(公告)号：US20220129498A1

公开(公告)日：2022-04-28

申请号：US17079945

申请日：2020-10-26

Applicant: Adobe Inc.

Inventor： Manoj Kilaru ,  Vishwa Vinay ,  Vidit Jain ,  Shaurya Goel ,  Ryan A. Rossi ,  Pratyush Garg ,  Nedim Lipka ,  Harkanwar Singh

IPC: G06F16/583 ,  G06F16/54 ,  G06F16/55 ,  G06F40/30 ,  G06F40/47 ,  G06K9/62 ,  G06N20/00

Abstract: In implementations of systems for generating occurrence contexts for objects in digital content collections, a computing device implements a context system to receive context request data describing an object that is depicted with additional objects in digital images of a digital content collection. The context system generates relationship embeddings for the object and each of the additional objects using a representation learning model trained to predict relationships for objects. A relationship graph is formed for the object that includes a vertex for each relationship between the object and the additional objects indicated by the relationship embeddings. The context system clusters the vertices of the relationship graph into contextual clusters that each represent an occurrence context of the object in the digital images of the digital content collection. The context system generates, for each contextual cluster, an indication of a respective occurrence context for the object for display in a user interface.