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11.发明申请公开(公告)号：US20200380418A1
公开(公告)日：2020-12-03
申请号：US16995149
申请日：2020-08-17
Applicant: Google LLC
Inventor： Brian Strope , Yun-hsuan Sung , Wangqing Yuan
IPC: G06N20/00 , G06F16/35 , G06F16/332 , G06F16/33 , G06N5/04
Abstract: Systems, methods, and computer readable media related to: training an encoder model that can be utilized to determine semantic similarity of a natural language textual string to each of one or more additional natural language textual strings (directly and/or indirectly); and/or using a trained encoder model to determine one or more responsive actions to perform in response to a natural language query. The encoder model is a machine learning model, such as a neural network model. In some implementations of training the encoder model, the encoder model is trained as part of a larger network architecture trained based on one or more tasks that are distinct from a “semantic textual similarity” task for which the encoder model can be used.
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公开(公告)号：US10699714B2
公开(公告)日：2020-06-30
申请号：US16041434
申请日：2018-07-20
Applicant: Google LLC
Inventor： Brian Strope , Francoise Beaufays , Olivier Siohan
Abstract: The subject matter of this specification can be embodied in, among other things, a method that includes receiving an audio signal and initiating speech recognition tasks by a plurality of speech recognition systems (SRS's). Each SRS is configured to generate a recognition result specifying possible speech included in the audio signal and a confidence value indicating a confidence in a correctness of the speech result. The method also includes completing a portion of the speech recognition tasks including generating one or more recognition results and one or more confidence values for the one or more recognition results, determining whether the one or more confidence values meets a confidence threshold, aborting a remaining portion of the speech recognition tasks for SRS's that have not generated a recognition result, and outputting a final recognition result based on at least one of the generated one or more speech results.
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公开(公告)号：US10026402B2
公开(公告)日：2018-07-17
申请号：US15284323
申请日：2016-10-03
Applicant: GOOGLE LLC
Inventor： Brian Strope , William J. Byrne , Francoise Beaufays
Abstract: A method of searching a business listing with voice commands includes receiving, over the Internet, from a user terminal, a query spoken by a user, which includes a speech utterance representing a category of merchandize, a speech utterance representing a merchandize item, and a speech utterance representing a geographic location. The method includes recognizing the geographic location with a speech recognition engine based on the speech utterance representing the geographic location, recognizing the category of merchandize with the speech recognition engine based on the speech utterance representing the category of merchandize, recognizing the merchandize item with a speech recognition engine based on the speech utterance representing the merchandize item, searching a business listing for businesses within or near the recognized geographic location to select businesses responsive to the query spoken by the user, and sending to the user terminal information related to at least some of the responsive businesses.
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公开(公告)号：US12086720B2
公开(公告)日：2024-09-10
申请号：US17502343
申请日：2021-10-15
Applicant: Google LLC
Inventor： Brian Strope , Yun-hsuan Sung , Matthew Henderson , Rami Al-Rfou' , Raymond Kurzweil
Abstract: Systems, methods, and computer readable media related to information retrieval. Some implementations are related to training and/or using a relevance model for information retrieval. The relevance model includes an input neural network model and a subsequent content neural network model. The input neural network model and the subsequent content neural network model can be separate, but trained and/or used cooperatively. The input neural network model and the subsequent content neural network model can be “separate” in that separate inputs are applied to the neural network models, and each of the neural network models is used to generate its own feature vector based on its applied input. A comparison of the feature vectors generated based on the separate network models can then be performed, where the comparison indicates relevance of the input applied to the input neural network model to the separate input applied to the subsequent content neural network model.
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15.发明公开公开(公告)号：US20240062111A1
公开(公告)日：2024-02-22
申请号：US18386015
申请日：2023-11-01
Applicant: GOOGLE LLC
Inventor： Brian Strope , Yun-Hsuan Sung , Wangqing Yuan
IPC: G06N20/00 , G06F16/33 , G06F16/332 , G06F16/35 , G06N5/04
CPC classification number: G06N20/00 , G06F16/3329 , G06F16/3344 , G06F16/3346 , G06F16/35 , G06N5/04
Abstract: Systems, methods, and computer readable media related to: training an encoder model that can be utilized to determine semantic similarity of a natural language textual string to each of one or more additional natural language textual strings (directly and/or indirectly); and/or using a trained encoder model to determine one or more responsive actions to perform in response to a natural language query. The encoder model is a machine learning model, such as a neural network model. In some implementations of training the encoder model, the encoder model is trained as part of a larger network architecture trained based on one or more tasks that are distinct from a “semantic textual similarity” task for which the encoder model can be used.
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Patent Agency Ranking
公开(公告)号：US20220036197A1
公开(公告)日：2022-02-03
申请号：US17502343
申请日：2021-10-15
Applicant: Google LLC
Inventor： Brian Strope , Yun-hsuan Sung , Matthew Henderson , Rami Al-Rfou' , Raymond Kurzweil
IPC: G06N3/08 , G06N5/04 , G06F16/00 , G06N3/04 , G06F16/335
Abstract: Systems, methods, and computer readable media related to information retrieval. Some implementations are related to training and/or using a relevance model for information retrieval. The relevance model includes an input neural network model and a subsequent content neural network model. The input neural network model and the subsequent content neural network model can be separate, but trained and/or used cooperatively. The input neural network model and the subsequent content neural network model can be “separate” in that separate inputs are applied to the neural network models, and each of the neural network models is used to generate its own feature vector based on its applied input. A comparison of the feature vectors generated based on the separate network models can then be performed, where the comparison indicates relevance of the input applied to the input neural network model to the separate input applied to the subsequent content neural network model.
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公开(公告)号：US11238211B2
公开(公告)日：2022-02-01
申请号：US16978658
申请日：2019-03-14
Applicant: Google LLC
Inventor： Jan van de Kerkhof , Balint Miklos , Amr Abdelfattah , Tobias Kaufmann , László Lukacs , Bjarke Ebert , Victor Anchidin , Brian Strope , Heeyoung Lee , Yun-hsuan Sung , Noah Constant , Neil Smith
IPC: G06F17/00 , G06F40/134 , G06F40/166 , G06F40/30
Abstract: A system may use a machine-learned model to determine whether to classify a sequence of one or more words within a first document that is being edited as a candidate hyperlink based at least in part on context associated with the first document. In response to classifying the sequence of one or more words as the candidate hyperlink, the system may use the machine-learned model and based at least in part on the sequence of one or more words and the context to determine one or more candidate document to be hyperlinked from the sequence of one or more words. In response to receiving an indication of a second document being selected out of the one or more candidate documents, the system may modify the first document to associate the sequence of one or more words with a hyperlink to the second document.
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公开(公告)号：US20200302931A1
公开(公告)日：2020-09-24
申请号：US16896192
申请日：2020-06-08
Applicant: GOOGLE LLC
Inventor： Brian Strope , Francoise Beaufays , William J. Byrne
IPC: G10L15/22 , G10L15/26 , G06Q30/02 , G06F16/29 , G06F16/951 , G06F16/9535 , G06F16/9537 , G10L15/18 , G10L15/197 , G10L15/30
Abstract: A method of providing navigation directions includes receiving, at a user terminal, a query spoken by a user, wherein the query spoken by the user includes a speech utterance indicating (i) a category of business, (ii) a name of the business, and (iii) a location at which or near which the business is disposed; identifying, by processing hardware, the business based on the speech utterance; and providing navigation directions to the business via the user terminal.
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公开(公告)号：US20180330735A1
公开(公告)日：2018-11-15
申请号：US16041434
申请日：2018-07-20
Applicant: Google LLC
Inventor： Brian Strope , Francoise Beaufays , Olivier Siohan
Abstract: The subject matter of this specification can be embodied in, among other things, a method that includes receiving an audio signal and initiating speech recognition tasks by a plurality of speech recognition systems (SRS's). Each SRS is configured to generate a recognition result specifying possible speech included in the audio signal and a confidence value indicating a confidence in a correctness of the speech result. The method also includes completing a portion of the speech recognition tasks including generating one or more recognition results and one or more confidence values for the one or more recognition results, determining whether the one or more confidence values meets a confidence threshold, aborting a remaining portion of the speech recognition tasks for SRS's that have not generated a recognition result, and outputting a final recognition result based on at least one of the generated one or more speech results.
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公开(公告)号：US20180322877A1
公开(公告)日：2018-11-08
申请号：US16036662
申请日：2018-07-16
Applicant: GOOGLE LLC
Inventor： Brian Strope , Francoise Beaufays , Willaim J. Byrne
Abstract: A method of providing a personal directory service includes receiving, over the Internet, from a user terminal, a query spoken by a user, where the query spoken by the user includes a speech utterance representing a category of persons. The method also includes determining a geographic location of the user terminal, recognizing the category of persons with the speech recognition engine based on the speech utterance representing the category of persons a listing of persons within or near the determined geographic location matching the query to select persons responsive to the query spoken by the user, and sending to the user terminal information related to at least some of the responsive persons.
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