公开(公告)号：US11271876B2

公开(公告)日：2022-03-08

申请号：US16548140

申请日：2019-08-22

Applicant: Adobe Inc.

Inventor： Seunghyun Yoon ,  Franck Dernoncourt ,  Doo Soon Kim ,  Trung Bui

IPC: H04L12/58 ,  G06F16/903 ,  G06F16/901 ,  G06N3/08 ,  H04L51/02

Abstract: The present disclosure relates to utilizing a graph neural network to accurately and flexibly identify text phrases that are relevant for responding to a query. For example, the disclosed systems can generate a graph topology having a plurality of nodes that correspond to a plurality of text phrases and a query. The disclosed systems can then utilize a graph neural network to analyze the graph topology, iteratively propagating and updating node representations corresponding to the plurality of nodes, in order to identify text phrases that can be used to respond to the query. In some embodiments, the disclosed systems can then generate a digital response to the query based on the identified text phrases.

公开(公告)号：US20210303555A1

公开(公告)日：2021-09-30

申请号：US16834850

申请日：2020-03-30

Applicant: Adobe Inc.

Inventor： Doo Soon Kim ,  Anthony M Colas ,  Franck Dernoncourt ,  Moumita Sinha ,  Trung Bui

IPC: G06F16/242 ,  G06F16/28

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for generating pairs of natural language queries and corresponding query-language representations. For example, the disclosed systems can generate a contextual representation of a prior-generated dialogue sequence to compare with logical-form rules. In some implementations, the logical-form rules comprise trigger conditions and corresponding logical-form actions for constructing a logical-form representation of a subsequent dialogue sequence. Based on the comparison to logical-form rules indicating satisfaction of one or more trigger conditions, the disclosed systems can perform logical-form actions to generate a logical-form representation of a subsequent dialogue sequence. In turn, the disclosed systems can apply a natural-language-to-query-language (NL2QL) template to the logical-form representation to generate a natural language query and a corresponding query-language representation for the subsequent dialogue sequence.

公开(公告)号：US11120059B2

公开(公告)日：2021-09-14

申请号：US16020328

申请日：2018-06-27

Applicant: Adobe Inc.

Inventor： Walter W. Chang ,  Jonathan Brandt ,  Doo Soon Kim

IPC: G06F16/00 ,  G06F16/332 ,  G06F16/93 ,  G06F16/33 ,  G06F16/338 ,  G06F16/31

Abstract: Techniques of directing a user to content based on a semantic interpretation of a query input by the user involves generating links to specific content in a collection of documents in response to user string query, the links being generated based on an answer suggestion lookahead index. The answer suggestion lookahead index references a mapping between a plurality of groups of semantically equivalent terms and a respective link to specific content of the collection of documents. These techniques are useful for the generalized task of natural language question answering.

公开(公告)号：US20210279084A1

公开(公告)日：2021-09-09

申请号：US16812962

申请日：2020-03-09

Applicant: Adobe Inc.

Inventor： Nedim Lipka ,  Doo Soon Kim

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

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：US20210058345A1

公开(公告)日：2021-02-25

申请号：US16548140

申请日：2019-08-22

Applicant: Adobe Inc.

Inventor： Seunghyun Yoon ,  Franck Dernoncourt ,  Doo Soon Kim ,  Trung Bui

IPC: H04L12/58 ,  G06N3/08 ,  G06F16/901 ,  G06F16/903

Abstract: The present disclosure relates to utilizing a graph neural network to accurately and flexibly identify text phrases that are relevant for responding to a query. For example, the disclosed systems can generate a graph topology having a plurality of nodes that correspond to a plurality of text phrases and a query. The disclosed systems can then utilize a graph neural network to analyze the graph topology, iteratively propagating and updating node representations corresponding to the plurality of nodes, in order to identify text phrases that can be used to respond to the query. In some embodiments, the disclosed systems can then generate a digital response to the query based on the identified text phrases.

公开(公告)号：US20200380030A1

公开(公告)日：2020-12-03

申请号：US16428308

申请日：2019-05-31

Applicant: ADOBE INC.

Inventor： Anthony Michael Colas ,  Doo Soon Kim ,  Franck Dernoncourt ,  Seokhwan Kim

IPC: G06F16/73 ,  G10L15/22 ,  G09B7/02

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for in-app video navigation in which videos including answers to user provided queries are presented within an application. And portions of the videos that specifically include the answer to the query are highlighted to allow for efficient and effective tutorial utilization. Upon receipt of a text or verbal query, top candidate videos including an answer to the query are determined. Within the top candidate videos, a video span with a starting sentence location and an ending location is identified based on the query and contextual information within each candidate video. The video span with the highest overall score calculated based on a video score and a span score is presented to the user.

公开(公告)号：US10579737B2

公开(公告)日：2020-03-03

申请号：US15913064

申请日：2018-03-06

Applicant: Adobe Inc.

Inventor： Jacqueline Brixey ,  Walter W. Chang ,  Trung Bui ,  Doo Soon Kim ,  Ramesh Radhakrishna Manuvinakurike

IPC: G06F17/24 ,  G06F17/27 ,  G06F17/30 ,  G06F3/0484

Abstract: A framework for annotating image edit requests includes a structure for identifying natural language request as either comments or image edit requests and for identifying the text of a request that maps to an executable action in an image editing program, as well as to identify other entities from the text related to the action. The annotation framework can be used to aid in the creation of artificial intelligence networks that carry out the requested action. An example method includes displaying a test image, displaying a natural language input with selectable text, and providing a plurality of selectable action tag controls and entity tag controls. The method may also include receiving selection of the text, receiving selection of an action tag control for the selected text, generating a labeled pair, and storing the labeled pair with the natural language input as an annotated natural language image edit request.

公开(公告)号：US12293577B2

公开(公告)日：2025-05-06

申请号：US17651771

申请日：2022-02-18

Applicant: Adobe Inc.

Inventor： Seunghyun Yoon ,  Trung Huu Bui ,  Franck Dernoncourt ,  Hyounghun Kim ,  Doo Soon Kim

IPC: G06V10/86 ,  G06F40/284 ,  G06N3/044 ,  G06N3/088 ,  G06V10/77 ,  G06V10/80 ,  G06V10/82

Abstract: Embodiments of the disclosure provide a machine learning model for generating a predicted executable command for an image. The learning model includes an interface configured to obtain an utterance indicating a request associated with the image, an utterance sub-model, a visual sub-model, an attention network, and a selection gate. The machine learning model generates a segment of the predicted executable command from weighted probabilities of each candidate token in a predetermined vocabulary determined based on the visual features, the concept features, current command features, and the utterance features extracted from the utterance or the image.

公开(公告)号：US11630952B2

公开(公告)日：2023-04-18

申请号：US16518894

申请日：2019-07-22

Applicant: Adobe Inc.

Inventor： Sean MacAvaney ,  Franck Dernoncourt ,  Walter Chang ,  Seokhwan Kim ,  Doo Soon Kim ,  Chen Fang

IPC: G06F17/15 ,  G06F17/16 ,  G06N3/045 ,  G06V10/80 ,  G06V10/82 ,  G06F40/279 ,  G06F18/2431 ,  G06V10/764 ,  G06V10/70

Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that can classify term sequences within a source text based on textual features analyzed by both an implicit-class-recognition model and an explicit-class-recognition model. For example, by applying machine-learning models for both implicit and explicit class recognition, the disclosed systems can determine a class corresponding to a particular term sequence within a source text and identify the particular term sequence reflecting the class. The dual-model architecture can equip the disclosed systems to apply (i) the implicit-class-recognition model to recognize implicit references to a class in source texts and (ii) the explicit-class-recognition model to recognize explicit references to the same class in source texts.

公开(公告)号：US11537950B2

公开(公告)日：2022-12-27

申请号：US17070568

申请日：2020-10-14

Applicant: Adobe Inc.

Inventor： Trung Bui ,  Tuan Manh Lai ,  Quan Tran ,  Doo Soon Kim

IPC: G06N20/00 ,  G06F16/248

Abstract: This disclosure describes one or more implementations of a text sequence labeling system that accurately and efficiently utilize a joint-learning self-distillation approach to improve text sequence labeling machine-learning models. For example, in various implementations, the text sequence labeling system trains a text sequence labeling machine-learning teacher model to generate text sequence labels. The text sequence labeling system then creates and trains a text sequence labeling machine-learning student model utilizing the training and the output of the teacher model. Upon the student model achieving improved results over the teacher model, the text sequence labeling system re-initializes the teacher model with the learned model parameters of the student model and repeats the above joint-learning self-distillation framework. The text sequence labeling system then utilizes a trained text sequence labeling model to generate text sequence labels from input documents.