公开(公告)号：US20220399017A1

公开(公告)日：2022-12-15

申请号：US17374103

申请日：2021-07-13

Applicant: Adobe Inc.

Inventor： Ning Xu ,  Jing Shi ,  Franck Dernoncourt ,  Trung Bui

IPC: G10L15/22 ,  G06T11/60 ,  G06T5/00 ,  G06F3/16 ,  G06N3/04 ,  G10L19/00 ,  G06T1/00

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize a neural network having a long short-term memory encoder-decoder architecture to progressively modify a digital image in accordance with a natural language request. For example, in one or more embodiments, the disclosed systems utilize a language-to-operation decoding cell of a language-to-operation neural network to sequentially determine one or more image-modification operations to perform to modify a digital image in accordance with a natural language request. In some cases, the decoding cell determines an image-modification operation to perform partly based on the previously used image-modification operations. The disclosed systems further utilize the decoding cell to determine one or more operation parameters for each selected image-modification operation. The disclosed systems utilize the image-modification operation(s) and operation parameter(s) to modify the digital image (e.g., by generating one or more modified digital images) via the decoding cell.

公开(公告)号：US20220383150A1

公开(公告)日：2022-12-01

申请号：US17331131

申请日：2021-05-26

Applicant: Adobe Inc.

Inventor： Nham Van Le ,  Tuan Manh Lai ,  Trung Bui ,  Doo Soon Kim

IPC: G06N5/04 ,  G06N20/00

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that provide a platform for on-demand selection of machine-learning models and on-demand learning of parameters for the selected machine-learning models via cloud-based systems. For instance, the disclosed system receives a request indicating a selection of a machine-learning model to perform a machine-learning task (e.g., a natural language task) utilizing a specific dataset (e.g., a user-defined dataset). The disclosed system utilizes a scheduler to monitor available computing devices on cloud-based storage systems for instantiating the selected machine-learning model. Using the indicated dataset at a determined cloud-based computing device, the disclosed system automatically trains the machine-learning model. In additional embodiments, the disclosed system generates a dataset visualization, such as an interactive confusion matrix, for interactively viewing and selecting data generated by the machine-learning model.

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：US11205444B2

公开(公告)日：2021-12-21

申请号：US16543342

申请日：2019-08-16

Applicant: Adobe Inc.

Inventor： Trung Bui ,  Subhadeep Dey ,  Seunghyun Yoon

IPC: G10L25/00 ,  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.

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：US11120801B2

公开(公告)日：2021-09-14

申请号：US17086805

申请日：2020-11-02

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：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：US10803377B2

公开(公告)日：2020-10-13

申请号：US15053448

申请日：2016-02-25

Applicant: Adobe Inc.

Inventor： Anirban Roychowdhury ,  Trung Bui ,  John Kucera ,  Hung Bui ,  Hailin Jin

IPC: G06N3/02 ,  G06Q30/02 ,  G06N3/08 ,  H04L29/08 ,  H04W4/23

Abstract: Techniques for predictively selecting a content presentation in a client-server computing environment are described. In an example, a content management system detects an interaction of a client with a server and accesses client features. Responses of the client to potential content presentations are predicted based on a multi-task neural network. The client features are mapped to input nodes and the potential content presentations are associated with tasks mapped to output nodes of the multi-task neural network. The tasks specify usages of the potential content presentations in response to the interaction with the server. In an example, the content management system selects the content presentation from the potential content presentations based on the predicted responses. For instance, the content presentation is selected based on having the highest likelihood. The content management system provides the content presentation to the client based on the task corresponding to the content presentation.

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.