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    Scalable graph propagation for knowledge expansion
    11.
    发明授权
    Scalable graph propagation for knowledge expansion 有权

    公开(公告)号：US10430464B1

    公开(公告)日：2019-10-01

    申请号：US15849880

    申请日：2017-12-21

    Applicant: GOOGLE LLC

    Inventor： Sujith Ravi ,  Qiming Diao

    IPC: G06F17/30 ,  G06F16/901 ,  G06N20/00

    Abstract: Systems and methods for adding labels to a graph are disclosed. One system includes a plurality of computing devices including processors and memory storing an input graph generated based on a source data set, where an edge represents a similarity measure between two nodes in the input graph, the input graph being distributed across the plurality of computing devices, and some of the nodes are seed nodes associated with one or more training labels from a set of labels, each training label having an associated original weight. The memory may also store instructions that, when executed by the processors, cause the plurality of distributed computing devices to propagate the training labels through the input graph using a sparsity approximation for label propagation, resulting in learned weights for respective node and label pairs, and automatically update the source data set using node and label pairs selected based on the learned weights.

    Organizing images associated with a user
    12.
    发明授权
    Organizing images associated with a user 有权

    公开(公告)号：US10248889B2

    公开(公告)日：2019-04-02

    申请号：US15839739

    申请日：2017-12-12

    Applicant: Google LLC

    Inventor： Robin Dua ,  Sujith Ravi

    IPC: G06K9/62 ,  G06F17/30 ,  G06K9/52 ,  G06Q30/06

    Abstract: A method includes identifying images associated with a user, where the image is identified as at least one of captured by a user device associated with the user, stored on the user device associated with the user, and stored in cloud storage associated with the user. The method also includes for each of the images, determining one or more labels, wherein the one or more labels are based on at least one of metadata and a primary annotation. The method also includes generating a mapping of the one or more labels to one or more confidence scores, wherein the one or more confidence scores indicate an extent to which the one or more labels apply to corresponding images. The method also includes interacting with the user to obtain identifying information that is used to categorize one or more of the images.

    On-device projection neural networks for natural language understanding
    14.
    发明授权
    On-device projection neural networks for natural language understanding 有权

    公开(公告)号：US11423233B2

    公开(公告)日：2022-08-23

    申请号：US17141473

    申请日：2021-01-05

    Applicant: Google LLC

    Inventor： Sujith Ravi ,  Zornitsa Kozareva

    IPC: G06F40/30 ,  G06N3/08 ,  G06N3/04 ,  G06F40/253

    Abstract: The present disclosure provides projection neural networks and example applications thereof. In particular, the present disclosure provides a number of different architectures for projection neural networks, including two example architectures which can be referred to as: Self-Governing Neural Networks (SGNNs) and Projection Sequence Networks (ProSeqoNets). Each projection neural network can include one or more projection layers that project an input into a different space. For example, each projection layer can use a set of projection functions to project the input into a bit-space, thereby greatly reducing the dimensionality of the input and enabling computation with lower resource usage. As such, the projection neural networks provided herein are highly useful for on-device inference in resource-constrained devices. For example, the provided SGNN and ProSeqoNet architectures are particularly beneficial for on-device inference such as, for example, solving natural language understanding tasks on-device.

    On-Device Projection Neural Networks for Natural Language Understanding
    15.
    发明申请
    On-Device Projection Neural Networks for Natural Language Understanding 有权

    公开(公告)号：US20210124878A1

    公开(公告)日：2021-04-29

    申请号：US17141473

    申请日：2021-01-05

    Applicant: Google LLC

    Inventor： Sujith Ravi ,  Zornitsa Kozareva

    IPC: G06F40/30 ,  G06N3/08 ,  G06N3/04 ,  G06F40/253

    Abstract: The present disclosure provides projection neural networks and example applications thereof. In particular, the present disclosure provides a number of different architectures for projection neural networks, including two example architectures which can be referred to as: Self-Governing Neural Networks (SGNNs) and Projection Sequence Networks (ProSegoNets). Each projection neural network can include one or more projection layers that project an input into a different space. For example, each projection layer can use a set of projection functions to project the input into a bit-space, thereby greatly reducing the dimensionality of the input and enabling computation with lower resource usage. As such, the projection neural networks provided herein are highly useful for on-device inference in resource-constrained devices. For example, the provided SGNN and ProSegoNet architectures are particularly beneficial for on-device inference such as, for example, solving natural language understanding tasks on-device.

    SEARCH AND RETRIEVAL OF STRUCTURED INFORMATION CARDS
    16.
    发明申请
    SEARCH AND RETRIEVAL OF STRUCTURED INFORMATION CARDS 有权

    公开(公告)号：US20210049165A1

    公开(公告)日：2021-02-18

    申请号：US17086564

    申请日：2020-11-02

    Applicant: Google LLC

    Inventor： Marc Alexander Najork ,  Sujith Ravi ,  Michael Bendersky ,  Peter Shao-sen Young ,  Timothy Youngjin Sohn ,  Mingyang Zhang ,  Thomas Nelson ,  Xuanhui Wang

    IPC: G06F16/248 ,  G06F16/2455 ,  G06F16/951 ,  G06F16/38

    Abstract: Methods, systems, apparatus, including computer programs encoded on computer storage medium, to facilitate identification of additional trigger-terms for a structured information card. In one aspect, the method includes actions of accessing data associated with a template for presenting structured information, wherein the accessed data references (i) a label term and (ii) a value. Other actions may include obtaining a candidate label term, identifying one or more entities that are associated with the label term, identifying one or more of the entities that are associated with the candidate label term, and for each particular entity of the one or more entities that are associated with the candidate label term, associating, with the candidate label term, (i) a label term that is associated with the particular entity, and (ii) the value associated with the label term.

    On-Device Neural Networks for Natural Language Understanding
    19.
    发明申请
    On-Device Neural Networks for Natural Language Understanding 审中-公开

    公开(公告)号：US20200042596A1

    公开(公告)日：2020-02-06

    申请号：US16135545

    申请日：2018-09-19

    Applicant: Google LLC

    Inventor： Sujith Ravi ,  Zornitsa Kozareva

    IPC: G06F17/27 ,  G06N3/04 ,  G06N3/08

    Abstract: The present disclosure provides projection neural networks and example applications thereof. In particular, the present disclosure provides a number of different architectures for projection neural networks, including two example architectures which can be referred to as: Self-Governing Neural Networks (SGNNs) and Projection Sequence Networks (ProSeqoNets). Each projection neural network can include one or more projection layers that project an input into a different space. For example, each projection layer can use a set of projection functions to project the input into a bit-space, thereby greatly reducing the dimensionality of the input and enabling computation with lower resource usage. As such, the projection neural networks provided herein are highly useful for on-device inference in resource-constrained devices. For example, the provided SGNN and ProSeqoNet architectures are particularly beneficial for on-device inference such as, for example, solving natural language understanding tasks on-device.

    Methods for emotion classification in text
    20.
    发明授权
    Methods for emotion classification in text 有权

    公开(公告)号：US12112134B2

    公开(公告)日：2024-10-08

    申请号：US17582206

    申请日：2022-01-24

    Applicant: Google LLC

    Inventor： Dana Movshovitz-Attias ,  John Patrick McGregor, Jr. ,  Gaurav Nemade ,  Sujith Ravi ,  Jeongwoo Ko ,  Dora Demszky

    IPC: G06F40/289 ,  G06F40/30 ,  G06N20/00

    CPC classification number: G06F40/289 ,  G06F40/30 ,  G06N20/00

    Abstract: The technology relates to methods for detecting and classifying emotions in textual communication, and using this information to suggest graphical indicia such as emoji, stickers or GIFs to a user. Two main types of models are fully supervised models and few-shot models. In addition to fully supervised and few-shot models, other types of models focusing on the back-end (server) side or client (on-device) side may also be employed. Server-side models are larger-scale models that can enable higher degrees of accuracy, such as for use cases where models can be hosted on cloud servers where computational and storage resources are relatively abundant. On-device models are smaller-scale models, which enable use on resource-constrained devices such as mobile phones, smart watches or other wearables (e.g., head mounted displays), in-home devices, embedded devices, etc.

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