公开(公告)号：US11232147B2

公开(公告)日：2022-01-25

申请号：US16525366

申请日：2019-07-29

Applicant: Adobe Inc.

Inventor： Ajinkya Kale ,  Baldo Faieta ,  Benjamin Leviant ,  Fengbin Chen ,  Francois Guerin ,  Kate Sousa ,  Trung Bui ,  Venkat Barakam ,  Zhe Lin

IPC: G06F16/20 ,  G06F16/48 ,  G06K9/62 ,  G06F16/2457 ,  G06F16/43

Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for determining multi-term contextual tags for digital content and propagating the multi-term contextual tags to additional digital content. For instance, the disclosed systems can utilize search query supervision to determine and associate multi-term contextual tags (e.g., tags that represent a specific concept based on the order of the terms in the tag) with digital content. Furthermore, the disclosed systems can propagate the multi-term contextual tags determined for the digital content to additional digital content based on similarities between the digital content and additional digital content (e.g., utilizing clustering techniques). Additionally, the disclosed systems can provide digital content as search results based on the associated multi-term contextual tags.

公开(公告)号：US11100917B2

公开(公告)日：2021-08-24

申请号：US16366904

申请日：2019-03-27

Applicant: Adobe Inc.

Inventor： Trung Bui ,  Zahra Rahimi ,  Yinglan Ma ,  Seokhwan Kim ,  Franck Dernoncourt

IPC: G10L15/06 ,  G06F3/16 ,  G06F3/0482 ,  G06N20/00 ,  G10L15/22 ,  G10L15/18 ,  G06F3/0484 ,  G06F40/169

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that generate ground truth annotations of target utterances in digital image editing dialogues in order to create a state-driven training data set. In particular, in one or more embodiments, the disclosed systems utilize machine and user defined tags, machine learning model predictions, and user input to generate a ground truth annotation that includes frame information in addition to intent, attribute, object, and/or location information. In at least one embodiment, the disclosed systems generate ground truth annotations in conformance with an annotation ontology that results in fast and accurate digital image editing dialogue annotation.

公开(公告)号：US10861456B2

公开(公告)日：2020-12-08

申请号：US16133190

申请日：2018-09-17

Applicant: Adobe Inc.

Inventor： Quan Tran ,  Trung Bui ,  Hung Bui

IPC: G10L15/22 ,  G10L15/16 ,  G06N3/10 ,  G06N3/04 ,  G10L15/30

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating dialogue responses based on received utterances utilizing an independent gate context-dependent additive recurrent neural network. For example, the disclosed systems can utilize a neural network model to generate a dialogue history vector based on received utterances and can use the dialogue history vector to generate a dialogue response. The independent gate context-dependent additive recurrent neural network can remove local context to reduce computation complexity and allow for gates at all time steps to be computed in parallel. The independent gate context-dependent additive recurrent neural network maintains the sequential nature of a recurrent neural network using the hidden vector output.

公开(公告)号：US20190325068A1

公开(公告)日：2019-10-24

申请号：US15957556

申请日：2018-04-19

Applicant: Adobe Inc.

Inventor： Tuan Manh Lai ,  Trung Bui ,  Sheng Li ,  Quan Hung Tran ,  Hung Bui

IPC: G06F17/30 ,  G06N3/08 ,  G06Q30/06

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating digital responses to digital queries by utilizing a classification model and query-specific analysis models. For example, the disclosed systems can train a classification model to generate query classifications corresponding to product queries, conversational queries, and/or recommendation/purchase queries. Moreover, the disclosed systems can apply the classification model to select pertinent models for particular queries. For example, upon classifying a product query, disclosed systems can utilize a neural ranking model (trained based on a set of training product specifications and training queries) to generate relevance scores for product specifications associated with a digital query. The disclosed systems can further compare generated relevance scores to select a product specification and generate a digital response that includes the pertinent product specification to provide for display to a client device.

公开(公告)号：US20190278844A1

公开(公告)日：2019-09-12

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

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：US12210800B2

公开(公告)日：2025-01-28

申请号：US18311713

申请日：2023-05-03

Applicant: Adobe Inc.

Inventor： Nikita Soni ,  Trung Bui ,  Kevin Gary Smith

IPC: G06F3/16 ,  G06F3/04845 ,  G06F3/0488 ,  G06T11/00 ,  G06T11/60

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that edit digital images using combinations of speech input and gesture interactions. For instance, in some embodiments, the disclosed systems receive speech input from a client device displaying a digital image within a graphical user interface, the digital image portraying an object. Additionally, the disclosed systems detect, via the graphical user interface, one or more gesture interactions with respect to the object of the digital image. Based on the speech input, the disclosed systems determine an edit for the object of the digital image indicated by the one or more gesture interactions. Further, the disclosed systems modify the object within the digital image using the edit indicated by the one or more gesture interactions.

公开(公告)号：US11741157B2

公开(公告)日：2023-08-29

申请号：US17544689

申请日：2021-12-07

Applicant: Adobe Inc.

Inventor： Ajinkya Kale ,  Baldo Faieta ,  Benjamin Leviant ,  Fengbin Chen ,  Francois Guerin ,  Kate Sousa ,  Trung Bui ,  Venkat Barakam ,  Zhe Lin

IPC: G06F16/40 ,  G06F16/58 ,  G06F16/48 ,  G06F16/2457 ,  G06F16/43 ,  G06V20/00 ,  G06F18/23213

CPC classification number: G06F16/5866 ,  G06F16/24578 ,  G06F16/43 ,  G06F16/48 ,  G06F18/23213 ,  G06V20/35 ,  G06V2201/10

Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for determining multi-term contextual tags for digital content and propagating the multi-term contextual tags to additional digital content. For instance, the disclosed systems can utilize search query supervision to determine and associate multi-term contextual tags (e.g., tags that represent a specific concept based on the order of the terms in the tag) with digital content. Furthermore, the disclosed systems can propagate the multi-term contextual tags determined for the digital content to additional digital content based on similarities between the digital content and additional digital content (e.g., utilizing clustering techniques). Additionally, the disclosed systems can provide digital content as search results based on the associated multi-term contextual tags.

公开(公告)号：US11594077B2

公开(公告)日：2023-02-28

申请号：US17025477

申请日：2020-09-18

Applicant: Adobe Inc.

Inventor： Trung Bui ,  Zhe Lin ,  Walter Chang ,  Nham Le ,  Franck Dernoncourt

IPC: G06V40/20 ,  G06N3/04 ,  G10L15/26 ,  G10L15/25

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating modified digital images based on verbal and/or gesture input by utilizing a natural language processing neural network and one or more computer vision neural networks. The disclosed systems can receive verbal input together with gesture input. The disclosed systems can further utilize a natural language processing neural network to generate a verbal command based on verbal input. The disclosed systems can select a particular computer vision neural network based on the verbal input and/or the gesture input. The disclosed systems can apply the selected computer vision neural network to identify pixels within a digital image that correspond to an object indicated by the verbal input and/or gesture input. Utilizing the identified pixels, the disclosed systems can generate a modified digital image by performing one or more editing actions indicated by the verbal input and/or gesture input.

公开(公告)号：US20220138185A1

公开(公告)日：2022-05-05

申请号：US17087943

申请日：2020-11-03

Applicant: Adobe Inc.

Inventor： Quan Tran ,  Zhe Lin ,  Xuanli He ,  Walter Chang ,  Trung Bui ,  Franck Dernoncourt

IPC: G06F16/242 ,  G06F40/56 ,  G06N3/08 ,  G06N3/04

Abstract: Systems and methods for natural language processing are described. Embodiments are configured to receive a structured representation of a search query, wherein the structured representation comprises a plurality of nodes and at least one edge connecting two of the nodes, receive a modification expression for the search query, wherein the modification expression comprises a natural language expression, generate a modified structured representation based on the structured representation and the modification expression using a neural network configured to combine structured representation features and natural language expression features, and perform a search based on the modified structured representation.

公开(公告)号：US20220076693A1

公开(公告)日：2022-03-10

申请号：US17526810

申请日：2021-11-15

Applicant: Adobe Inc.

Inventor： Trung Bui ,  Subhadeep Dey ,  Seunghyun Yoon

IPC: G10L25/63 ,  G06N3/04 ,  G06F17/18 ,  G06F17/16 ,  G10L25/30

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for determining speech emotion. In particular, a speech emotion recognition system generates an audio feature vector and a textual feature vector for a sequence of words. Further, the speech emotion recognition system utilizes a neural attention mechanism that intelligently blends together the audio feature vector and the textual feature vector to generate attention output. Using the attention output, which includes consideration of both audio and text modalities for speech corresponding to the sequence of words, the speech emotion recognition system can apply attention methods to one of the feature vectors to generate a hidden feature vector. Based on the hidden feature vector, the speech emotion recognition system can generate a speech emotion probability distribution of emotions among a group of candidate emotions, and then select one of the candidate emotions as corresponding to the sequence of words.