公开(公告)号：US20190384807A1

公开(公告)日：2019-12-19

申请号：US16007632

申请日：2018-06-13

Applicant: Adobe Inc.

Inventor： Franck Dernoncourt ,  Walter Chang ,  Trung Bui ,  Sean Fitzgerald ,  Sasha Spala ,  Kishore Aradhya ,  Carl Dockhorn

IPC: G06F17/24 ,  G06F17/30 ,  G06N3/08 ,  G06N3/04

Abstract: Systems, methods, and non-transitory computer-readable media are disclosed that collect and analyze annotation performance data to generate digital annotations for evaluating and training automatic electronic document annotation models. In particular, in one or more embodiments, the disclosed systems provide electronic documents to annotators based on annotator topic preferences. The disclosed systems then identify digital annotations and annotation performance data such as a time period spent by an annotator in generating digital annotations and annotator responses to digital annotation questions. Furthermore, in one or more embodiments, the disclosed systems utilize the identified digital annotations and the annotation performance data to generate a final set of reliable digital annotations. Additionally, in one or more embodiments, the disclosed systems provide the final set of digital annotations for utilization in training a machine learning model to generate annotations for electronic documents.

公开(公告)号：US20250068924A1

公开(公告)日：2025-02-27

申请号：US18449291

申请日：2023-08-14

Applicant: Adobe Inc.

Inventor： Meryem M'hamdi ,  Seunghyun Yoon ,  Franck Dernoncourt ,  Trung Bui

IPC: G06N3/096 ,  G06F40/30 ,  G06F40/40

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for providing multilingual semantic search results utilizing meta-learning and knowledge distillation. For example, in some implementations, the disclosed systems perform a first inner learning loop for a monolingual to bilingual meta-learning task for a teacher model. Additionally, in some implementations, the disclosed systems perform a second inner learning loop for a bilingual to multilingual meta-learning task for a student model. In some embodiments, the disclosed systems perform knowledge distillation based on the first inner learning loop for the monolingual to bilingual meta-learning task and the second inner learning loop for the bilingual to multilingual meta-learning task. Moreover, in some embodiments, the disclosed systems perform an outer learning loop and update parameters of a deep learning language model based on the first inner learning loop, the second inner learning loop, and the knowledge distillation.

公开(公告)号：US12130850B2

公开(公告)日：2024-10-29

申请号：US18147960

申请日：2022-12-29

Applicant: Adobe Inc.

Inventor： Trung Bui ,  Yu Gong ,  Tushar Dublish ,  Sasha Spala ,  Sachin Soni ,  Nicholas Miller ,  Joon Kim ,  Franck Dernoncourt ,  Carl Dockhorn ,  Ajinkya Kale

IPC: G06F16/33 ,  G06F40/30 ,  G06N5/04 ,  G06N20/00

CPC classification number: G06F16/3347 ,  G06F40/30 ,  G06N5/04 ,  G06N20/00

Abstract: Techniques and systems are described for performing semantic text searches. A semantic text-searching solution uses a machine learning system (such as a deep learning system) to determine associations between the semantic meanings of words. These associations are not limited by the spelling, syntax, grammar, or even definition of words. Instead, the associations can be based on the context in which characters, words, and/or phrases are used in relation to one another. In response to detecting a request to locate text within an electronic document associated with a keyword, the semantic text-searching solution can return strings within the document that have matching and/or related semantic meanings or contexts, in addition to exact matches (e.g., string matches) within the document. The semantic text-searching solution can then output an indication of the matching strings.

公开(公告)号：US20240256218A1

公开(公告)日：2024-08-01

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

CPC classification number: G06F3/167 ,  G06F3/04845 ,  G06F3/0488 ,  G06T11/001 ,  G06T11/60 ,  G06T2200/24

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：US11776036B2

公开(公告)日：2023-10-03

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: G06Q30/0601 ,  G06N3/08 ,  G06F16/951 ,  G06F16/583 ,  G06V10/764

CPC classification number: G06Q30/0631 ,  G06F16/583 ,  G06F16/951 ,  G06N3/08 ,  G06V10/764

Abstract: The present description 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 described systems can train a classification model to generate query classifications corresponding to product queries, conversational queries, and/or recommendation/purchase queries. Moreover, the described systems can apply the classification model to select pertinent models for particular queries. For example, upon classifying a product query, the described 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 described 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：US11670023B2

公开(公告)日：2023-06-06

申请号：US17007693

申请日：2020-08-31

Applicant: Adobe Inc.

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

IPC: G06T7/00 ,  G06T19/20 ,  G06T11/60 ,  G10L15/16 ,  G10L15/22

CPC classification number: G06T11/60 ,  G10L15/16 ,  G10L15/22 ,  G10L2015/223

Abstract: This disclosure involves executing artificial intelligence models that infer image editing operations from natural language requests spoken by a user. Further, this disclosure performs the inferred image editing operations using inferred parameters for the image editing operations. Systems and methods may be provided that infer one or more image editing operations from a natural language request associated with a source image, locate areas of the source that are relevant to the one or more image editing operations to generate image masks, and performing the one or more image editing operations to generate a modified source image.

公开(公告)号：US20230133583A1

公开(公告)日：2023-05-04

申请号：US18147960

申请日：2022-12-29

Applicant: Adobe Inc.

Inventor： Trung Bui ,  Yu Gong ,  Tushar Dublish ,  Sasha Spala ,  Sachin Soni ,  Nicholas Miller ,  Joon Kim ,  Franck Dernoncourt ,  Carl Dockhorn ,  Ajinkya Kale

IPC: G06F16/33 ,  G06N20/00 ,  G06F40/30 ,  G06N5/04

Abstract: Techniques and systems are described for performing semantic text searches. A semantic text-searching solution uses a machine learning system (such as a deep learning system) to determine associations between the semantic meanings of words. These associations are not limited by the spelling, syntax, grammar, or even definition of words. Instead, the associations can be based on the context in which characters, words, and/or phrases are used in relation to one another. In response to detecting a request to locate text within an electronic document associated with a keyword, the semantic text-searching solution can return strings within the document that have matching and/or related semantic meanings or contexts, in addition to exact matches (e.g., string matches) within the document. The semantic text-searching solution can then output an indication of the matching strings.

公开(公告)号：US11610584B2

公开(公告)日：2023-03-21

申请号：US16889669

申请日：2020-06-01

Applicant: Adobe Inc.

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

IPC: G10L15/00 ,  G10L15/22 ,  G10L15/02 ,  G10L15/183 ,  G10L15/18

Abstract: A computer-implemented method is disclosed for determining one or more characteristics of a dialog between a computer system and user. The method may comprise receiving a system utterance comprising one or more tokens defining one or more words generated by the computer system; receiving a user utterance comprising one or more tokens defining one or more words uttered by a user in response to the system utterance, the system utterance and the user utterance forming a dialog context; receiving one or more utterance candidates comprising one or more tokens; for each utterance candidate, generating an input sequence combining the one or more tokens of each of the system utterance, the user utterance, and the utterance candidate; and for each utterance candidate, evaluating the generated input sequence with a model to determine a probability that the utterance candidate is relevant to the dialog context.

公开(公告)号：US11567981B2

公开(公告)日：2023-01-31

申请号：US16849885

申请日：2020-04-15

Applicant: Adobe Inc.

Inventor： Trung Bui ,  Yu Gong ,  Tushar Dublish ,  Sasha Spala ,  Sachin Soni ,  Nicholas Miller ,  Joon Kim ,  Franck Dernoncourt ,  Carl Dockhorn ,  Ajinkya Kale

IPC: G06F40/30 ,  G06F16/33 ,  G06N20/00 ,  G06N5/04

Abstract: Techniques and systems are described for performing semantic text searches. A semantic text-searching solution uses a machine learning system (such as a deep learning system) to determine associations between the semantic meanings of words. These associations are not limited by the spelling, syntax, grammar, or even definition of words. Instead, the associations can be based on the context in which characters, words, and/or phrases are used in relation to one another. In response to detecting a request to locate text within an electronic document associated with a keyword, the semantic text-searching solution can return strings within the document that have matching and/or related semantic meanings or contexts, in addition to exact matches (e.g., string matches) within the document. The semantic text-searching solution can then output an indication of the matching strings.

公开(公告)号：US11544456B2

公开(公告)日：2023-01-03

申请号：US16810345

申请日：2020-03-05

Applicant: ADOBE INC.

Inventor： Khalil Mrini ,  Walter Chang ,  Trung Bui ,  Quan Tran ,  Franck Dernoncourt

IPC: G06F40/211 ,  G06N3/04 ,  G06N3/08

Abstract: Systems and methods for parsing natural language sentences using an artificial neural network (ANN) are described. Embodiments of the described systems and methods may generate a plurality of word representation matrices for an input sentence, wherein each of the word representation matrices is based on an input matrix of word vectors, a query vector, a matrix of key vectors, and a matrix of value vectors, and wherein a number of the word representation matrices is based on a number of syntactic categories, compress each of the plurality of word representation matrices to produce a plurality of compressed word representation matrices, concatenate the plurality of compressed word representation matrices to produce an output matrix of word vectors, and identify at least one word from the input sentence corresponding to a syntactic category based on the output matrix of word vectors.