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公开(公告)号：US20220343561A1
公开(公告)日：2022-10-27
申请号：US17240030
申请日：2021-04-26
Applicant: ADOBE INC.
Inventor： Pranav Aggarwal , Ajinkya Kale
Abstract: Systems and methods for color replacement are described. Embodiments of the disclosure include a color replacement system that adjusts an image based on a user-input source color and target color. For example, the source color may be replaced with the target color throughout the entire image. In some embodiments, a user provides a speech or text input that identifies a source color to be replaced. The user may then provide a speech or text input identifying the target color, replacing the source color. A color replacement system creates and embedding of the source color, segments the image based on the source color embedding, and then replaces the color of segmented portion of the image with the target color.
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公开(公告)号：US20210319056A1
公开(公告)日：2021-10-14
申请号：US16843218
申请日：2020-04-08
Applicant: ADOBE INC.
Inventor： Handong Zhao , Ajinkya Kale , Xiaowei Jia , Zhe Lin
IPC: G06F16/43 , G06F16/45 , G06F16/438 , G06N3/04 , G06N3/08
Abstract: The present disclosure relates to a retrieval method including: generating a graph representing a set of users, items, and queries; generating clusters from the media items; generating embeddings for each cluster from embeddings of the items within the corresponding cluster; generating augmented query embeddings for each cluster from the embedding of the corresponding cluster and query embeddings of the queries; inputting the cluster embeddings and the augmented query embeddings to a layer of a graph convolutional network (GCN) to determine user embeddings of the users; inputting the embedding of the given user and a query embedding of the given query to a layer of the GCN to determine a user-specific query embedding; generating a score for each of the items based on the item embeddings and the user-specific query embedding; and presenting the items having the score exceeding a threshold.
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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
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.
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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
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.
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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
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.
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Patent Agency Ranking
公开(公告)号：US11423206B2
公开(公告)日：2022-08-23
申请号：US17090055
申请日：2020-11-05
Applicant: ADOBE INC.
Inventor： William Frederick Kraus , Nathaniel Joseph Grabaskas , Ajinkya Kale
IPC: G06F40/109 , G06N3/08 , G06T11/60 , G06F40/237 , G06F40/30
Abstract: Embodiments provide systems, methods, and computer storage media for text style suggestions and/or text emphasis suggestions. In an example embodiment, an electronic design application provides a text style suggestion tool that generates text style suggestions to stylize a selected text element based on the context of the design. A text emphasis tool allows a user to select a text element and generate text emphasis suggestions for which words should be emphasized with a different text styling. Various interaction elements allow the user to iterate through the suggestions. For example, a set of style suggestions may be mapped to successive rotational increments around a style wheel, and as the user rotates through the positions on the style wheel, a corresponding text style suggestion is previewed and/or applied.
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公开(公告)号：US20220237682A1
公开(公告)日：2022-07-28
申请号：US17159554
申请日：2021-01-27
Applicant: ADOBE INC.
Inventor： Handong Zhao , Zhankui He , Zhaowen Wang , Zhe Lin , Ajinkya Kale , Fengbin Chen
Abstract: Systems and methods for item recommendation are described. Embodiments identify a sequence of items selected by a user, embed each item of the sequence of items to produce item embeddings having a reduced number of dimensions, predict a next item based on the item embeddings using a recommendation network, wherein the recommendation network includes a sequential encoder trained based at least in part on a sampled softmax classifier, and wherein predicting the next item represents a prediction that the user will interact with the next item, and provide a recommendation to the user, wherein the recommendation includes the next item.
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公开(公告)号：US20220138439A1
公开(公告)日：2022-05-05
申请号：US17088847
申请日：2020-11-04
Applicant: Adobe Inc.
Inventor： Ritiz Tambi , Pranav Aggarwal , Ajinkya Kale
IPC: G06F40/58 , G06F40/117
Abstract: Introduced here is an approach to translating tags assigned to digital images. As an example, embeddings may be extracted from a tag to be translated and the digital image with which the tag is associated by a multimodal model. These embeddings can be compared to embeddings extracted from a set of target tags associated with a target language by the multimodal model. Such an approach allows similarity to be established along two dimensions, which ensures the obstacles associated with direct translation can be avoided.
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19.发明申请公开(公告)号：US20220121702A1
公开(公告)日：2022-04-21
申请号：US17075450
申请日：2020-10-20
Applicant: Adobe Inc.
Inventor： Ajinkya Kale , Zhe Lin , Pranav Aggarwal
IPC: G06F16/535 , G06K9/62 , G06F40/279 , G06F16/242 , G06F16/538 , G06N3/04 , G06N3/08
Abstract: The present disclosure relates to methods, systems, and non-transitory computer-readable media for retrieving digital images in response to queries. For example, in one or more embodiments, the disclosed systems receive a query comprising text and generates a cross-lingual-multimodal embedding for the text within a multimodal embedding space. The disclosed systems further identifies an image embedding for a digital image that corresponds to (e.g., is relevant to) the text from the query based on an embedding distance between the image embedding and the cross-lingual-multimodal embedding for the text within the multimodal embedding space. Accordingly, the disclosed systems retrieve the digital image associated with the image embedding for display on a client device, such as the client device that submitted the query.
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公开(公告)号：US11928418B2
公开(公告)日：2024-03-12
申请号：US17805910
申请日：2022-06-08
Applicant: ADOBE INC.
Inventor： William Frederick Kraus , Nathaniel Joseph Grabaskas , Ajinkya Kale
IPC: G06F40/109 , G06F40/237 , G06F40/30 , G06N3/08 , G06T11/60
CPC classification number: G06F40/109 , G06F40/237 , G06F40/30 , G06N3/08 , G06T11/60
Abstract: Embodiments provide systems, methods, and computer storage media for text style suggestions and/or text emphasis suggestions. In an example embodiment, an electronic design application provides a text style suggestion tool that generates text style suggestions to stylize a selected text element based on the context of the design. A text emphasis tool allows a user to select a text element and generate text emphasis suggestions for which words should be emphasized with a different text styling. Various interaction elements allow the user to iterate through the suggestions. For example, a set of style suggestions may be mapped to successive rotational increments around a style wheel, and as the user rotates through the positions on the style wheel, a corresponding text style suggestion is previewed and/or applied.
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