-
公开(公告)号：US20250162682A1
公开(公告)日：2025-05-22
申请号：US19029458
申请日：2025-01-17
Applicant: GOOGLE LLC
Inventor： Sandro Feuz , Thomas Deselaers
Abstract: Implementations set forth herein relate to generating a pre-call analysis for one or more users that are receiving and/or initializing a call with one or more other users, and/or prioritizing pre-call content according to whether security-related value was gleaned from provisioning certain pre-call content. One or more machine learning models can be employed for determining the pre-call content to be cached and/or presented prior to a user accepting a call from another user. Feedback provided before, during, and/or after the call can be used as a basis from which to prioritize certain content and/or sources of content when generating pre-call content for a subsequent call. Other information, such as contextual data (e.g., calendar entries, available peripheral devices, location, etc.) corresponding to the previous call and/or the subsequent call, can also be used as a basis from which to provide a pre-call analysis.
-
公开(公告)号：US11983613B2
公开(公告)日：2024-05-14
申请号：US17545384
申请日：2021-12-08
Applicant: Google LLC
Inventor： Sandro Feuz , Victor Carbune
Abstract: The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a mode controller that allows a user to provide data input indicating whether to operate one or more applications on the device in a first collection mode (e.g., permission mode) for storing training examples or a second collection mode for (e.g., incognito mode) for not storing training examples. The training examples can be generated based on user interaction with the one or more applications and used to personalize one or more machine-learned models used by the application(s) by retraining the models using the user-specific training examples.
-
公开(公告)号：US11893995B2
公开(公告)日：2024-02-06
申请号：US18074758
申请日：2022-12-05
Applicant: GOOGLE LLC
Inventor： Victor Carbune , Pedro Gonnet Anders , Thomas Deselaers , Sandro Feuz
CPC classification number: G10L15/30 , G10L15/22 , G10L13/033 , G10L13/08 , G10L2015/088 , G10L2015/223 , G10L2015/228 , H04W4/80
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.
-
公开(公告)号：US20230275902A1
公开(公告)日：2023-08-31
申请号：US18142926
申请日：2023-05-03
Applicant: GOOGLE LLC
Inventor： Victor Carbune , Thomas Deselaers , Sandro Feuz
IPC: H04L9/40 , G10L15/22 , H04L67/12 , H04L67/30 , G06F16/635 , G06F3/16 , G10L17/00 , G06F21/32 , G06F9/50
CPC classification number: H04L63/107 , G06F3/167 , G06F9/5055 , G06F16/635 , G06F21/32 , G10L15/22 , G10L17/00 , H04L63/0861 , H04L67/12 , H04L67/30 , G10L15/30 , G10L2015/227
Abstract: The present disclosure is generally directed to a data processing system for customizing content in a voice activated computer network environment. With user consent, the data processing system can improve the efficiency and effectiveness of auditory data packet transmission over one or more computer networks by, for example, increasing the accuracy of the voice identification process used in the generation of customized content. The present solution can make accurate identifications while generating fewer audio identification models, which are computationally intensive to generate.
-
公开(公告)号：US20230031521A1
公开(公告)日：2023-02-02
申请号：US17962636
申请日：2022-10-10
Applicant: GOOGLE LLC
Inventor： Pedro Gonnet Anders , Victor Carbune , Daniel Keysers , Thomas Deselaers , Sandro Feuz
Abstract: Techniques are described herein for enabling an automated assistant to adjust its behavior depending on a detected age range and/or “vocabulary level” of a user who is engaging with the automated assistant. In various implementations, data indicative of a user's utterance may be used to estimate one or more of the user's age range and/or vocabulary level. The estimated age range/vocabulary level may be used to influence various aspects of a data processing pipeline employed by an automated assistant. In various implementations, aspects of the data processing pipeline that may be influenced by the user's age range/vocabulary level may include one or more of automated assistant invocation, speech-to-text (“STT”) processing, intent matching, intent resolution (or fulfillment), natural language generation, and/or text-to-speech (“TTS”) processing. In some implementations, one or more tolerance thresholds associated with one or more of these aspects, such as grammatical tolerances, vocabularic tolerances, etc., may be adjusted.
-
Patent Agency Ranking
公开(公告)号：US11521600B2
公开(公告)日：2022-12-06
申请号：US16883690
申请日：2020-05-26
Applicant: GOOGLE LLC
Inventor： Pedro Gonnet Anders , Victor Carbune , Daniel Keysers , Thomas Deselaers , Sandro Feuz
Abstract: Techniques are described herein for enabling an automated assistant to adjust its behavior depending on a detected vocabulary level or other vocal characteristics of an input utterance provided to an automated assistant. The estimated vocabulary level or other vocal characteristics may be used to influence various aspects of a data processing pipeline employed by the automated assistant. In some implementations, one or more tolerance thresholds associated with, for example, grammatical tolerances or vocabulary tolerances, may be adjusted based on the estimated vocabulary level or vocal characteristics of the input utterance.
-
公开(公告)号：US11138384B2
公开(公告)日：2021-10-05
申请号：US16662755
申请日：2019-10-24
Applicant: Google LLC
Inventor： Sebastian Millius , Sandro Feuz
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for announcing and detecting automated conversation are disclosed. One of the methods includes initiating, over a natural language communication channel, a conversation with a communication participant using a natural language communication method that includes a dialogue of natural language communications. The communication participant is determined to be automated using a pre-defined adaptive interactive protocol that specifies natural language linguistic transformations defined in a sequence. The conversation can be transitioned to a communication method that is different form the natural language communication method in response to determining that the communication participant is automated.
-
公开(公告)号：US20210209248A1
公开(公告)日：2021-07-08
申请号：US17142974
申请日：2021-01-06
Applicant: Google LLC
Inventor： Sandro Feuz , Thomas Deselaers
Abstract: This document describes techniques and systems that enable privacy controls for sharing embeddings for searching and indexing media content. A set of images of a user's face are obtained and a machine-learned model is applied to the set of images to generate a user-specific dataset of face embeddings for the user. Media content stored in a media storage is indexed by applying the machine-learned model to the media content to provide indexed media information identifying one or more faces shown in the media content. Access to the indexed media information by another user querying the media content for images or videos depicting the user is controlled based on a digital key shared by the user with the other user, where the digital key is associated with the user-specific dataset and the user-specific dataset is usable to identify the images or videos depicting the user.
-
9.发明申请公开(公告)号：US20190163667A1
公开(公告)日：2019-05-30
申请号：US15825551
申请日：2017-11-29
Applicant: Google LLC
Inventor： Sandro Feuz , Victor Carbune
Abstract: The present disclosure provides an on-device machine learning platform that enables sharing of machine-learned models between applications on a computing device. For example, a first application which has a machine-learned model for a specific task can expose the model to other applications through a system level application programming interface (API) for the other applications to use. Communications using the API can be handled by the on-device machine learning platform. In some implementations, some exchange of resources (e.g., computing resources) can be provided so that the first application is compensated for sharing the machine-learned model (e.g., on a per model invocation basis).
-
公开(公告)号：US20190138940A1
公开(公告)日：2019-05-09
申请号：US15805484
申请日：2017-11-07
Applicant: Google LLC
Inventor： Sandro Feuz , Victor Carbune
Abstract: The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a mode controller that allows a user to provide data input indicating whether to operate one or more applications on the device in a first collection mode (e.g., permission mode) for storing training examples or a second collection mode for (e.g., incognito mode) for not storing training examples. The training examples can be generated based on user interaction with the one or more applications and used to personalize one or more machine-learned models used by the application(s) by retraining the models using the user-specific training examples.
-
-
-
-
-
-
-
-
-
Search Results
88
records
(took 0.021 seconds)
