公开(公告)号：US12263910B2

公开(公告)日：2025-04-01

申请号：US18378080

申请日：2023-10-09

Applicant: GOOGLE LLC

Inventor： Sandro Feuz ,  Thomas Deselaers

IPC: H04M3/436 ,  B60T7/08 ,  B62L3/02 ,  G01D5/14 ,  G06F3/01 ,  G06N20/00 ,  H04M3/42

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：US12223410B2

公开(公告)日：2025-02-11

申请号：US18589391

申请日：2024-02-27

Applicant: GOOGLE LLC

Inventor： Thomas Deselaers ,  Victor Carbune

IPC: G06N3/006 ,  B60W30/18 ,  G06F18/21 ,  G06N20/00 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56 ,  G06V20/58

Abstract: To select a lane in a multi-lane road segment for a vehicle travelling on the road segment, a system identifies, in multiple lanes and in a region ahead of the vehicle, another vehicle defining a target; the system applies an optical flow technique to track the target during a period of time, to generate an estimate of how fast traffic moves; and the system applies the estimate to machine learning (ML) model to generate a recommendation which one of the plurality of lanes the vehicle is to choose.

公开(公告)号：US20240169221A1

公开(公告)日：2024-05-23

申请号：US18426325

申请日：2024-01-29

Applicant: GOOGLE LLC

Inventor： Daniel M. Keysers ,  Victor Carbune ,  Thomas Deselaers

IPC: G06N5/04 ,  G06N20/00

CPC classification number: G06N5/04 ,  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.

公开(公告)号：US11915115B2

公开(公告)日：2024-02-27

申请号：US17138866

申请日：2020-12-30

Applicant: GOOGLE LLC

Inventor： Thomas Deselaers ,  Victor Carbune

IPC: G06N3/00 ,  G06N20/00 ,  G06N3/006 ,  B60W30/18 ,  G06V20/56 ,  G06F18/21 ,  G06V10/764 ,  G06V10/82 ,  G06V20/58

CPC classification number: G06N3/006 ,  B60W30/18163 ,  G06F18/217 ,  G06N20/00 ,  G06V10/764 ,  G06V10/82 ,  G06V20/58 ,  G06V20/588 ,  B60W2420/42 ,  B60W2520/10 ,  B60W2552/10 ,  B60W2555/20

Abstract: To selecting a lane in a multi-lane road segment for a vehicle travelling on the road segment, a system determines current traffic information for the road segment including a plurality of lanes and applies the current traffic information to a machine learning (ML) model to generate an indication of one of the plurality of lanes in which the vehicle is to travel. Subsequently to the vehicle selecting the indicated lane, the system determines an amount of time the vehicle took to travel a certain distance following the selection, and provides the determined amount of time to the ML model as a feedback signal.

公开(公告)号：US11831804B2

公开(公告)日：2023-11-28

申请号：US17340768

申请日：2021-06-07

Applicant: GOOGLE LLC

Inventor： Sandro Feuz ,  Thomas Deselaers

IPC: H04M3/436 ,  G06N20/00 ,  G06F3/01 ,  H04M3/42

CPC classification number: H04M3/4365 ,  G06F3/017 ,  G06N20/00 ,  H04M3/42042 ,  H04M2203/25

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：US20230237312A1

公开(公告)日：2023-07-27

申请号：US18128092

申请日：2023-03-29

Applicant: GOOGLE LLC

Inventor： Victor Carbune ,  Thomas Deselaers

IPC: G06N3/045 ,  G06N3/00 ,  G06N3/02 ,  G10L15/00 ,  G10L15/22 ,  G10L15/20

CPC classification number: G06N3/045 ,  G06N3/00 ,  G06N3/02 ,  G10L15/00 ,  G10L15/22 ,  G10L15/222 ,  G10L15/20 ,  G10L2015/223

Abstract: Techniques are disclosed that enable automating user interface input by generating a sequence of actions to perform a task utilizing a multi-agent reinforcement learning framework. Various implementations process an intent associated with received user interface input using a holistic reinforcement policy network to select a software reinforcement learning policy network. The sequence of actions can be generated by processing the intent, as well as a sequence of software client state data, using the selected software reinforcement learning policy network. The sequence of actions are utilized to control the software client corresponding to the selected software reinforcement learning policy network.

公开(公告)号：US11494667B2

公开(公告)日：2022-11-08

申请号：US15874121

申请日：2018-01-18

Applicant: Google LLC

Inventor： Victor Carbune ,  Thomas Deselaers

IPC: G06N5/04 ,  G06K9/62 ,  G06N20/00 ,  G06N3/04 ,  G06N3/08

Abstract: Example aspects of the present disclosure are directed to systems and methods that enable improved adversarial training of machine-learned models. An adversarial training system can generate improved adversarial training examples by optimizing or otherwise tuning one or hyperparameters that guide the process of generating of the adversarial examples. The adversarial training system can determine, solicit, or otherwise obtain a realism score for an adversarial example generated by the system. The realism score can indicate whether the adversarial example appears realistic. The adversarial training system can adjust or otherwise tune the hyperparameters to produce improved adversarial examples (e.g., adversarial examples that are still high-quality and effective while also appearing more realistic). Through creation and use of such improved adversarial examples, a machine-learned model can be trained to be more robust against (e.g., less susceptible to) various adversarial techniques, thereby improving model, device, network, and user security and privacy.

公开(公告)号：US11483170B1

公开(公告)日：2022-10-25

申请号：US16730484

申请日：2019-12-30

Applicant: GOOGLE LLC

Inventor： Victor Carbune ,  Daniel Keysers ,  Thomas Deselaers

IPC: G06F15/16 ,  H04L12/18 ,  H04M3/56

Abstract: Systems and methods for video conference content auto-retrieval and focus based on learned relevance is provided. In accordance with the systems and methods, audio streams and video streams from client devices participating in a video conference are received. Based on the audio streams, a subject being discussed during the video conference at a point in time is determined. A video stream that is most relevant to the subject being discussed during the video conference at the point in time is determined from the video streams. The determined video stream is provided to the client devices for presentation on the client devices while the subject is being discussed during the video conference.

公开(公告)号：US20220036216A1

公开(公告)日：2022-02-03

申请号：US17280034

申请日：2018-11-20

Applicant: Google LLC

Inventor： Jay Yagnik ,  Aleksandr Darin ,  Thierry Coppey ,  Thomas Deselaers ,  Victor Carbune

IPC: G06N5/04 ,  G06N20/00

Abstract: The present disclosure is directed to a new framework the enables the combination of symbolic programming with machine learning, where the programmer maintains control of the overall architecture of the functional mapping and the ability to inject domain knowledge while allowing their program to evolve by learning from examples. In some instances, the framework provided herein can be referred to as “predictive programming.”

公开(公告)号：US20210243200A1

公开(公告)日：2021-08-05

申请号：US17237573

申请日：2021-04-22

Applicant: Google LLC

Inventor： Victor Carbune ,  Thomas Deselaers ,  Sandro Feuz

IPC: H04L29/06 ,  G10L15/22 ,  H04L29/08 ,  G06F16/635 ,  G06F3/16 ,  G10L17/00 ,  G06F21/32 ,  G06F9/50

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.