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1.发明申请公开(公告)号：US20250022459A1
公开(公告)日：2025-01-16
申请号：US18220910
申请日：2023-07-12
Applicant: Adobe Inc.
Inventor： Viet Dac Lai , Trung Bui , Seunghyun Yoon , Quan Tran , Hao Tan , Hanieh Deilamsalehy , Abel Salinas , Franck Dernoncourt
IPC: G10L15/183 , G10L15/065
Abstract: The disclosed method generates helpful training data for a language model, for example, a model implementing a punctuation restoration task, for real-world ASR texts. The method uses a reinforcement learning method using a generative AI model to generate additional data to train the language model. The method allows the generative AI model to learn from real-world ASR text to generate more effective training examples based on gradient feedback from the language model.
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2.发明授权公开(公告)号：US11570318B2
公开(公告)日：2023-01-31
申请号：US17374103
申请日：2021-07-13
Applicant: Adobe Inc.
Inventor： Ning Xu , Jing Shi , Franck Dernoncourt , Trung Bui
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.
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公开(公告)号：US11561969B2
公开(公告)日：2023-01-24
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: G06F16/00 , 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.
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公开(公告)号：US20220374426A1
公开(公告)日：2022-11-24
申请号：US17317052
申请日：2021-05-11
Applicant: ADOBE INC.
Inventor： Dung Thai , Doo Soon Kim , Franck Dernoncourt , Trung Bui
IPC: G06F16/2452 , G06F16/22 , G06N3/08 , G06N3/04 , G06F40/30
Abstract: Systems and methods for natural language processing are described. One or more embodiments of the present disclosure receive a query related to information in a table, compute an operation selector by combining the query with an operation embedding representing a plurality of table operations, compute a column selector by combining the query with a weighted operation embedding, compute a row selector based on the operation selector and the column selector, compute a probability value for a cell in the table based on the row selector and the column selector, where the probability value represents a probability that the cell provides an answer to the query, and transmit contents of the cell based on the probability value.
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公开(公告)号：US11113479B2
公开(公告)日：2021-09-07
申请号：US16569513
申请日：2019-09-12
Applicant: Adobe Inc.
Inventor： Quan Tran , Tuan Manh Lai , Trung Bui
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that can determine an answer to a query based on matching probabilities for combinations of respective candidate answers. For example, the disclosed systems can utilize a gated-self attention mechanism (GSAM) to interpret inputs that include contextual information, a query, and candidate answers. The disclosed systems can also utilize a memory network in tandem with the GSAM to form a gated self-attention memory network (GSAMN) to refine outputs or predictions over multiple reasoning hops. Further, the disclosed systems can utilize transfer learning of the GSAM/GSAMN from an initial training dataset to a target training dataset.
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Patent Agency Ranking
公开(公告)号：US20210081503A1
公开(公告)日：2021-03-18
申请号：US16569513
申请日：2019-09-12
Applicant: Adobe Inc.
Inventor： Quan Tran , Tuan Manh Lai , Trung Bui
IPC: G06F17/28 , G06N5/04 , G06F16/9032 , G06F17/16 , G06K9/62
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that can determine an answer to a query based on matching probabilities for combinations of respective candidate answers. For example, the disclosed systems can utilize a gated-self attention mechanism (GSAM) to interpret inputs that include contextual information, a query, and candidate answers. The disclosed systems can also utilize a memory network in tandem with the GSAM to form a gated self-attention memory network (GSAMN) to refine outputs or predictions over multiple reasoning hops. Further, the disclosed systems can utilize transfer learning of the GSAM/GSAMN from an initial training dataset to a target training dataset.
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公开(公告)号：US20210034657A1
公开(公告)日：2021-02-04
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: G06F16/48 , G06K9/62 , G06F16/43 , G06F16/2457
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.
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8.发明授权公开(公告)号：US12236975B2
公开(公告)日：2025-02-25
申请号：US17526810
申请日：2021-11-15
Applicant: Adobe Inc.
Inventor： Trung Bui , Subhadeep Dey , Seunghyun Yoon
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.
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9.发明公开公开(公告)号：US20230197081A1
公开(公告)日：2023-06-22
申请号：US18107620
申请日：2023-02-09
Applicant: Adobe Inc.
Inventor： Tuan Manh Lai , Trung Bui , Quan Tran
CPC classification number: G10L15/22 , G10L15/02 , G10L15/183
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.
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公开(公告)号：US11556826B2
公开(公告)日：2023-01-17
申请号：US16825531
申请日：2020-03-20
Applicant: Adobe Inc.
Inventor： Trung Bui , Lidan Wang , Franck Dernoncourt
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for selecting hyper-parameter sets by utilizing a modified Bayesian optimization approach based on a combination of accuracy and training efficiency metrics of a machine learning model. For example, the disclosed systems can fit accuracy regression and efficiency regression models to observed metrics associated with hyper-parameter sets of a machine learning model. The disclosed systems can also implement a trade-off acquisition function that implements an accuracy-training efficiency balance metric to explore the hyper-parameter feature space and select hyper-parameters for training the machine learning model considering a balance between accuracy and training efficiency.
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