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公开(公告)号：US20220147770A1
公开(公告)日：2022-05-12
申请号：US17091403
申请日：2020-11-06
申请人： Adobe Inc.
发明人： Rajiv Jain , Varun Ion Manjunatha , Joseph Barrow , Vlad Ion Moraniu , Franck Dernoncourt , Sasha Spala , Nicholas Miller
IPC分类号： G06K9/62 , G06K9/00 , G06F40/30 , G06F40/117
摘要： Certain embodiments involve using a machine-learning tool to generate metadata identifying segments and topics for text within a document. For instance, in some embodiments, a text processing system obtains input text and applies a segmentation-and-labeling model to the input text. The segmentation-and-labeling model is trained to generate a predicted segment for the input text using a segmentation network. The segmentation-and-labeling model is also trained to generate a topic for the predicted segment using a pooling network of the model to the predicted segment. The output of the model is usable for generating metadata identifying the predicted segment and the associated topic.
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公开(公告)号：US11783008B2
公开(公告)日：2023-10-10
申请号：US17091403
申请日：2020-11-06
申请人： Adobe Inc.
发明人： Rajiv Jain , Varun Manjunatha , Joseph Barrow , Vlad Ion Morariu , Franck Dernoncourt , Sasha Spala , Nicholas Miller
IPC分类号： G06F18/214 , G06F40/30 , G06F40/117 , G06V30/413 , G06F18/21 , G06F18/2415 , G06F16/33
CPC分类号： G06F18/2148 , G06F18/217 , G06F18/2415 , G06F40/117 , G06F40/30 , G06V30/413 , G06F16/33 , G06V2201/10
摘要： Certain embodiments involve using a machine-learning tool to generate metadata identifying segments and topics for text within a document. For instance, in some embodiments, a text processing system obtains input text and applies a segmentation-and-labeling model to the input text. The segmentation-and-labeling model is trained to generate a predicted segment for the input text using a segmentation network. The segmentation-and-labeling model is also trained to generate a topic for the predicted segment using a pooling network of the model to the predicted segment. The output of the model is usable for generating metadata identifying the predicted segment and the associated topic.
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公开(公告)号：US11232255B2
公开(公告)日：2022-01-25
申请号：US16007632
申请日：2018-06-13
申请人： Adobe Inc.
发明人： Franck Dernoncourt , Walter Chang , Trung Bui , Sean Fitzgerald , Sasha Spala , Kishore Aradhya , Carl Dockhorn
IPC分类号： G06F40/169
摘要： 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.
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公开(公告)号：US20230409672A1
公开(公告)日：2023-12-21
申请号：US18242075
申请日：2023-09-05
申请人： Adobe Inc.
发明人： Rajiv Jain , Varun Manjunatha , Joseph Barrow , Vlad Ion Morariu , Franck Dernoncourt , Sasha Spala , Nicholas Miller
IPC分类号： G06F18/214 , G06F40/30 , G06F40/117 , G06V30/413 , G06F18/21 , G06F18/2415
CPC分类号： G06F18/2148 , G06F40/30 , G06F40/117 , G06V30/413 , G06F18/217 , G06F18/2415 , G06V2201/10 , G06F16/33
摘要： Certain embodiments involve using a machine-learning tool to generate metadata identifying segments and topics for text within a document. For instance, in some embodiments, a text processing system obtains input text and applies a segmentation-and-labeling model to the input text. The segmentation-and-labeling model is trained to generate a predicted segment for the input text using a segmentation network. The segmentation-and-labeling model is also trained to generate a topic for the predicted segment using a pooling network of the model to the predicted segment. The output of the model is usable for generating metadata identifying the predicted segment and the associated topic.
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公开(公告)号：US20210326371A1
公开(公告)日：2021-10-21
申请号：US16849885
申请日：2020-04-15
申请人： Adobe Inc.
发明人： Trung Bui , Yu Gong , Tushar Dublish , Sasha Spala , Sachin Soni , Nicholas Miller , Joon Kim , Franck Dernoncourt , Carl Dockhorn , Ajinkya Kale
摘要： 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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6.发明申请公开(公告)号：US20190384807A1
公开(公告)日：2019-12-19
申请号：US16007632
申请日：2018-06-13
申请人： Adobe Inc.
发明人： Franck Dernoncourt , Walter Chang , Trung Bui , Sean Fitzgerald , Sasha Spala , Kishore Aradhya , Carl Dockhorn
摘要： 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.
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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
申请人： Adobe Inc.
发明人： Trung Bui , Yu Gong , Tushar Dublish , Sasha Spala , Sachin Soni , Nicholas Miller , Joon Kim , Franck Dernoncourt , Carl Dockhorn , Ajinkya Kale
CPC分类号： G06F16/3347 , G06F40/30 , G06N5/04 , G06N20/00
摘要： 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
申请人： Adobe Inc.
发明人： Trung Bui , Yu Gong , Tushar Dublish , Sasha Spala , Sachin Soni , Nicholas Miller , Joon Kim , Franck Dernoncourt , Carl Dockhorn , Ajinkya Kale
摘要： 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
申请人： Adobe Inc.
发明人： Trung Bui , Yu Gong , Tushar Dublish , Sasha Spala , Sachin Soni , Nicholas Miller , Joon Kim , Franck Dernoncourt , Carl Dockhorn , Ajinkya Kale
摘要： 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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