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    Methods for Emotion Classification in Text
    1.
    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.

    Automatic suggested responses to images received in messages using language model
    4.
    发明授权
    Automatic suggested responses to images received in messages using language model 有权

    公开(公告)号：US10146768B2

    公开(公告)日：2018-12-04

    申请号：US15415506

    申请日：2017-01-25

    Applicant: Google LLC

    Inventor： Ariel Fuxman ,  Vivek Ramavajjala ,  Ning Ye ,  John Patrick McGregor, Jr.

    IPC: G06K9/46 ,  G06F17/27 ,  G06K9/48

    Abstract: Implementations relate to automatic response suggestions to images included in received messages. In some implementations, a computer-implemented method includes detecting an image posted within a first message by a first user, and programmatically analyzing the image to determine a feature vector representative of the image. The method programmatically generates one or more suggested responses to the first message based on the feature vector, each suggested response being a conversational reply to the first message. Generating the suggested responses includes determining probabilities associated with word sequences for the feature vector using a model trained with previous responses to previous images, and selecting one or more of the word sequences based on the associated probabilities. The suggested responses are determined based on the selected word sequences. The method causes the suggested responses to be rendered in the messaging application as one or more suggestions to a second user.

    Methods for emotion classification in text
    5.
    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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