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    On-device projection neural networks for natural language understanding
    2.
    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
    3.
    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
    4.
    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
    7.
    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
    8.
    发明申请
    Methods for Emotion Classification in Text 有权

    公开(公告)号：US20250045526A1

    公开(公告)日：2025-02-06

    申请号：US18823169

    申请日：2024-09-03

    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

    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.

    Projection neural networks
    10.
    发明授权
    Projection neural networks 有权

    公开(公告)号：US11544573B2

    公开(公告)日：2023-01-03

    申请号：US16926908

    申请日：2020-07-13

    Applicant: Google LLC

    Inventor： Sujith Ravi

    IPC: G06N3/08 ,  G06N3/10 ,  G06F17/16 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a projection neural network. In one aspect, a projection neural network is configured to receive a projection network input and to generate a projection network output from the projection network input. The projection neural network includes a sequence of one or more projection layers. Each projection layer has multiple projection layer parameters, and is configured to receive a layer input, apply multiple projection layer functions to the layer input, and generate a layer output by applying the projection layer parameters for the projection layer to the projection function outputs.

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