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.

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.

公开(公告)号：US20220292261A1

公开(公告)日：2022-09-15

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

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.