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公开(公告)号：US20180182397A1
公开(公告)日：2018-06-28
申请号：US15387884
申请日：2016-12-22
Applicant: Google Inc.
Inventor： Victor Carbune , Pedro Gonnet Anders , Thomas Deselaers , Sandro Feuz
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for collaboration between multiple voice controlled devices are disclosed. In one aspect, a method includes the actions of identifying, by a first computing device, a second computing device that is configured to respond to a particular, predefined hotword; receiving audio data that corresponds to an utterance; receiving a transcription of additional audio data outputted by the second computing device in response to the utterance; based on the transcription of the additional audio data and based on the utterance, generating a transcription that corresponds to a response to the additional audio data; and providing, for output, the transcription that corresponds to the response.
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公开(公告)号：US20180137400A1
公开(公告)日：2018-05-17
申请号：US15349037
申请日：2016-11-11
Applicant: Google Inc.
Inventor： Thomas Deselaers , Victor Carbune , Pedro Gonnet Anders , Daniel Martin Keysers
CPC classification number: G06N3/0445
Abstract: The present disclosure provides systems and methods that leverage machine-learned models (e.g., neural networks) to provide enhanced communication assistance. In particular, the systems and methods of the present disclosure can include or otherwise leverage a machine-learned communication assistance model to detect problematic statements included in a communication and/or provide suggested replacement statements to respectively replace the problematic statements. In one particular example, the communication assistance model can include a long short-term memory recurrent neural network that detects an inappropriate tone or unintended meaning within a user-composed communication and provides one or more suggested replacement statements to replace the problematic statements.
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公开(公告)号：US20160300573A1
公开(公告)日：2016-10-13
申请号：US14681408
申请日：2015-04-08
Applicant: Google Inc.
Inventor： Victor Carbune , Daniel M. Keysers , Thomas Deselaers
CPC classification number: G10L15/26 , G06F17/243 , G10L15/193 , G10L17/22 , G10L25/48
Abstract: In some implementations, user input is received while a form that includes text entry fields is being accessed. In one aspect, a process may include mapping user input to fields of a form and populating the fields of the form with the appropriate information. This process may allow a user to fill out a form using speech input, by generating a transcription of input speech, determining a field that best corresponds to each portion of the speech, and populating each field with the appropriate information.
Abstract translation: 在一些实现中，接收用户输入，而正在访问包括文本输入字段的表单。 在一个方面，过程可以包括将用户输入映射到表单的字段并且用适当的信息填充表单的字段。 该过程可以允许用户使用语音输入来填写表单，通过生成输入语音的转录，确定与语音的每个部分最佳对应的字段，以及用适当的信息填充每个字段。
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公开(公告)号：US11494631B2
公开(公告)日：2022-11-08
申请号：US15716848
申请日：2017-09-27
Applicant: Google Inc.
Inventor： Victor Carbune , Sandro Feuz
IPC: G06N3/08 , G06N7/00 , G06Q30/02 , H04L67/289 , H04L67/50 , H04L67/5681 , G06N3/04 , H04L67/60
Abstract: Methods, systems, and apparatuses for implementing advanced content retrieval are described. Machine learning methods may be implemented so that a system may predict when a user device may experience network disconnections. The system may also predict the type of content one or more applications on the user device may seek to download during the network disconnection period. Neural networks may be trained based on user activity log data and may implement machine-learning techniques to determine user preferences and settings for advanced content retrieval. The system may predict when a user may want to download content in advance, the type of content the user may be interested in, anticipated network connectivity, and anticipated battery consumption. The system may then generate recommendations for the user device based on the predictions. If a user agrees with the recommendations, the system may obtain and cache the content.
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公开(公告)号：US10261685B2
公开(公告)日：2019-04-16
申请号：US15393611
申请日：2016-12-29
Applicant: Google Inc.
Inventor： Thomas Deselaers , Victor Carbune
Abstract: The present disclosure provides systems and methods that leverage machine learning to predict multiple touch interpretations. In particular, the systems and methods of the present disclosure can include and use a machine-learned touch interpretation prediction model that has been trained to receive touch sensor data indicative of one or more locations of one or more user input objects relative to a touch sensor at one or more times and, in response to receipt of the touch sensor data, provide one or more predicted touch interpretation outputs. Each predicted touch interpretation output corresponds to a different type of predicted touch interpretation based at least in part on the touch sensor data. Predicted touch interpretations can include a set of touch point interpretations, a gesture interpretation, and/or a touch prediction vector for one or more future times.
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Patent Agency Ranking
公开(公告)号：US20180189647A1
公开(公告)日：2018-07-05
申请号：US15393322
申请日：2016-12-29
Applicant: Google, Inc.
Inventor： Marcos Calvo , Victor Carbune , Pedro Gonnet Anders , Thomas Deselaers
CPC classification number: G06N3/08 , G05B13/0265 , G06N3/00 , G06N5/022
Abstract: The present disclosure provides systems and methods that leverage machine learning to refine and/or predict sensor outputs for multiple sensors. In particular, systems and methods of the present disclosure can include and use a machine-learned virtual sensor model that has been trained to receive sensor data from multiple sensors that is indicative of one or more measured parameters in each sensor's physical environment, recognize correlations among sensor outputs of the multiple sensors, and in response to receipt of the sensor data from multiple sensors, output one or more virtual sensor output values. The one or more virtual sensor output values can include one or more of refined sensor output values and one or more predicted future sensor output value.
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公开(公告)号：US20180121828A1
公开(公告)日：2018-05-03
申请号：US15340284
申请日：2016-11-01
Applicant: Google Inc.
Inventor： Daniel M. Keysers , Victor Carbune , Thomas Deselaers
IPC: G06N99/00
CPC classification number: G06N20/00
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing actionable suggestions are disclosed. In one aspect, a method includes receiving (i) an indication that an event detection module has determined that a shared event of a particular type is presently occurring or has occurred, and (ii) data referencing an attribute associated with the shared event. The method includes selecting, from among multiple output templates that are each associated with a different type of shared event, a particular output template associated with the particular type of shared event detected by the module. The method generates a notification for output using at least (i) the selected particular output template, and (ii) the data referencing the attribute associated with the shared event. The method then provides, for output to a user device, the notification that is generated.
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公开(公告)号：US11237696B2
公开(公告)日：2022-02-01
申请号：US15383966
申请日：2016-12-19
Applicant: GOOGLE INC.
Inventor： Victor Carbune , Daniel Keysers , Thomas Deselaers
IPC: G06F3/0481 , G06F3/0482 , G06Q30/06 , G06F9/451 , G06K9/00 , G06Q50/12
Abstract: Systems and methods enable a computing system to recognize a sequence of repeated actions and offer to automatically repeat any such recognized actions. An example method includes determining a current sequence of user actions is similar to a previous sequence of user actions, determining whether the previous sequence is reproducible and, when reproducible, initiating display of a prompt that requests approval for completing the current sequence based on the previous sequence and, responsive to receiving an indication of approval, completing the previous sequence. Another example method includes determining that a first current sequence of user interactions is complete and is not similar to any saved sequence of user interactions, saving the first current sequence as a previous sequence, identifying a second current sequence as satisfying a similarity threshold with the previous sequence, and initiating display of a prompt that requests approval for saving the previous sequence as a shortcut.
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公开(公告)号：US20190082046A1
公开(公告)日：2019-03-14
申请号：US15698734
申请日：2017-09-08
Applicant: Google Inc.
Inventor： Victor Carbune , Sandro Feuz
CPC classification number: H04M1/72566 , G06F16/245 , G06F16/951 , G06F16/9574 , G06Q20/145 , H04L67/06 , H04L67/2847 , H04L67/32 , H04M1/72569 , H04M2201/42 , H04M2215/0116 , H04M2242/14 , H04M2242/28
Abstract: Methods, systems, and apparatuses, including computer programs encoded on a computer-readable storage medium for implementing advanced information retrieval are described. A user may provide fetching parameter values to acquire content. The system may determine whether or not the user-provided fetching parameter values can be satisfied. If the fetching parameters can be satisfied, the system obtains and caches the information.
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公开(公告)号：US20180176173A1
公开(公告)日：2018-06-21
申请号：US15380748
申请日：2016-12-15
Applicant: Google Inc.
Inventor： Daniel Martin Keysers , Thomas Deselaers , Victor Carbune
CPC classification number: H04L51/32 , G06F17/27 , G06N3/0445 , G06N5/025 , G06N7/005 , G06N20/00 , H04L51/063 , H04L51/16
Abstract: A social network server system may receive a social media message that is to be posted at the social network server system, the social media message being authored by a user of the social network server system. Prior to posting the social media message at the social network server system, the social network server system may determine, based at least in part on applying one or more rules to content of the social media message, a likelihood that the user would modify the content of the social media message after it is posted at the social network server system, wherein the one or more rules are generated based at least in part on previous actions taken by the user on previous social media messages authored by the user and posted at the social network server system and may, responsive to determining that the likelihood exceeds a threshold, generate an alert message.
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