公开(公告)号：US20210073613A1

公开(公告)日：2021-03-11

申请号：US16949994

申请日：2020-11-23

Applicant: Snap Inc.

Inventor： Yingzhen Yang ,  Jianchao Yang ,  Ning Xu

IPC: G06N3/04 ,  G06T5/20 ,  G06T7/10

Abstract: A compact neural network system can generate multiple individual filters from a compound filter. Each convolutional layer of a convolutional neural network can include a compound filters used to generate individual filters for that layer. The individual filters overlap in the compound filter and can be extracted using a sampling operation. The extracted individual filters can share weights with nearby filters thereby reducing the overall size of the convolutional neural network.

公开(公告)号：US20210021551A1

公开(公告)日：2021-01-21

申请号：US16918343

申请日：2020-07-01

Applicant: Snap Inc.

Inventor： Jianchao Yang ,  Yuke Zhu ,  Ning Xu ,  Kevin Dechau Tang ,  Jia Li

IPC: H04L12/58 ,  G06K9/00 ,  G06F16/22 ,  G06F16/51 ,  G06F16/583 ,  G06N20/00 ,  G06F16/9535 ,  G06F16/954 ,  G06F16/2457 ,  G06F16/951 ,  G06N3/04 ,  G06N3/08

Abstract: Systems, devices, methods, media, and instructions for automated image processing and content curation are described. In one embodiment a server computer system communicates at least a portion of a first content collection to a first client device, and receives a first selection communication in response, the first selection communication identifying a first piece of content of the first plurality of pieces of content. The server analyzes analyzing the first piece of content to identify a set of context values for the first piece of content, and accesses accessing a second content collection comprising pieces of content sharing at least a portion of the set of context values of the first piece of content. In various embodiments, different content values, image processing operations, and content selection operations are used to curate the content collections.

公开(公告)号：US10713754B1

公开(公告)日：2020-07-14

申请号：US15908461

申请日：2018-02-28

Applicant: Snap Inc.

Inventor： Guohui Wang ,  Sumant Milind Hanumante ,  Ning Xu ,  Yuncheng Li

IPC: G06K9/62 ,  G06T3/40 ,  G06N3/08 ,  G06N3/04 ,  G06T1/20 ,  G06T11/60 ,  G06N3/063

Abstract: Remote distribution of multiple neural network models to various client devices over a network can be implemented by identifying a native neural network and remotely converting the native neural network to a target neural network based on a given client device operating environment. The native neural network can be configured for execution using efficient parameters, and the target neural network can use less efficient but more precise parameters.

公开(公告)号：US11907312B1

公开(公告)日：2024-02-20

申请号：US15862403

申请日：2018-01-04

Applicant: Snap Inc.

Inventor： Yanen Li ,  Fei Wu ,  Ning Xu

IPC: G06F16/9535 ,  G06N20/00 ,  G06N7/00 ,  H04L67/50 ,  G06Q50/00

CPC classification number: G06F16/9535 ,  G06N7/00 ,  G06N20/00 ,  H04L67/535 ,  G06Q50/01

Abstract: Systems and methods are provided for generating a user click history table and a random bucket training table, generating training data for training a user-type-affinity machine learning model by combining the user click history table and the random bucket training table, and training the user-type-affinity machine learning model with the generated training data. The systems and methods further provide for generating a user click prediction table and generating user-type-affinity prediction values for each of the plurality of users by inputting the user click prediction table into the user-type-affinity machine learning model.

公开(公告)号：US20240037141A1

公开(公告)日：2024-02-01

申请号：US18378376

申请日：2023-10-10

Applicant: Snap Inc.

Inventor： Xiaoyu Wang ,  Ning Xu ,  Ning Zhang ,  Vitor Rocha de Carvalho ,  Jia Li

IPC: G06F16/58 ,  G06T1/00 ,  G06N3/08 ,  G06F16/9038 ,  G06N3/04 ,  G06F18/24 ,  G06N3/045 ,  H04N23/63 ,  G06V10/764 ,  G06V10/82 ,  G06V10/75

CPC classification number: G06F16/5866 ,  G06T1/0007 ,  G06N3/08 ,  G06F16/9038 ,  G06N3/04 ,  G06F18/24 ,  G06N3/045 ,  H04N23/63 ,  G06V10/764 ,  G06V10/82 ,  G06V10/751 ,  G06N5/022

Abstract: Systems, methods, devices, media, and computer readable instructions are described for local image tagging in a resource constrained environment. One embodiment involves processing image data using a deep convolutional neural network (DCNN) comprising at least a first subgraph and a second subgraph, the first subgraph comprising at least a first layer and a second layer, processing, the image data using at least the first layer of the first subgraph to generate first intermediate output data; processing, by the mobile device, the first intermediate output data using at least the second layer of the first subgraph to generate first subgraph output data, and in response to a determination that each layer reliant on the first intermediate data have completed processing, deleting the first intermediate data from the mobile device. Additional embodiments involve convolving entire pixel resolutions of the image data against kernels in different layers if the DCNN.

公开(公告)号：US20230418910A1

公开(公告)日：2023-12-28

申请号：US18244543

申请日：2023-09-11

Applicant: Snap Inc.

Inventor： Jianfei Yu ,  Luis Carlos Dos Santos Marujo ,  Venkata Satya Pradeep Karuturi ,  Leonardo Ribas Machado das Neves ,  Ning Xu ,  William Brendel

IPC: G06F18/2431 ,  G06N3/08 ,  G06N20/20 ,  G06F40/284 ,  G06F40/30 ,  G06N3/045

CPC classification number: G06F18/2431 ,  G06N3/08 ,  G06N20/20 ,  G06F40/284 ,  G06F40/30 ,  G06N3/045 ,  G10L25/30

Abstract: Sentiment classification can be implemented by an entity-level multimodal sentiment classification neural network. The neural network can include left, right, and target entity subnetworks. The neural network can further include an image network that generates representation data that is combined and weighted with data output by the left, right, and target entity subnetworks to output a sentiment classification for an entity included in a network post.

公开(公告)号：US11847760B2

公开(公告)日：2023-12-19

申请号：US17714764

申请日：2022-04-06

Applicant: Snap Inc.

Inventor： Guohui Wang ,  Sumant Milind Hanumante ,  Ning Xu ,  Yuncheng Li

IPC: G06N3/08 ,  G06T3/40 ,  G06N3/04 ,  G06T1/20 ,  G06T11/60 ,  G06N3/063

CPC classification number: G06T3/4046 ,  G06N3/04 ,  G06N3/063 ,  G06N3/08 ,  G06T1/20 ,  G06T11/60 ,  G06T2207/20081

Abstract: Remote distribution of multiple neural network models to various client devices over a network can be implemented by identifying a native neural network and remotely converting the native neural network to a target neural network based on a given client device operating environment. The native neural network can be configured for execution using efficient parameters, and the target neural network can use less efficient but more precise parameters.

公开(公告)号：US20230376757A1

公开(公告)日：2023-11-23

申请号：US18230499

申请日：2023-08-04

Applicant: Snap Inc.

Inventor： Yuncheng Li ,  Zhou Ren ,  Ning Xu ,  Enxu Yan ,  Tan Yu

IPC: G06N3/08 ,  G06T7/55 ,  G06T7/33 ,  G06V20/64 ,  G06F18/214 ,  G06F18/2431 ,  G06V10/82 ,  G06V10/44 ,  G06V20/20 ,  G06V20/40

CPC classification number: G06N3/08 ,  G06T7/55 ,  G06T7/344 ,  G06V20/64 ,  G06F18/214 ,  G06F18/2431 ,  G06V10/82 ,  G06V10/454 ,  G06V20/20 ,  G06V20/41

Abstract: Systems and methods are disclosed for capturing multiple sequences of views of a three-dimensional object using a plurality of virtual cameras. The systems and methods generate aligned sequences from the multiple sequences based on an arrangement of the plurality of virtual cameras in relation to the three-dimensional object. Using a convolutional network, the systems and methods classify the three-dimensional object based on the aligned sequences and identify the three-dimensional object using the classification.

公开(公告)号：US11822600B2

公开(公告)日：2023-11-21

申请号：US17248386

申请日：2021-01-22

Applicant: Snap Inc.

Inventor： Xiaoyu Wang ,  Ning Xu ,  Ning Zhang ,  Vitor R. Carvalho ,  Jia Li

IPC: G06F16/58 ,  G06T1/00 ,  G06N3/08 ,  G06F16/9038 ,  G06N3/04 ,  G06F18/24 ,  G06N3/045 ,  H04N23/63 ,  G06V10/764 ,  G06V10/82 ,  G06V10/75 ,  G06N5/022

CPC classification number: G06F16/5866 ,  G06F16/9038 ,  G06F18/24 ,  G06N3/04 ,  G06N3/045 ,  G06N3/08 ,  G06T1/0007 ,  G06V10/751 ,  G06V10/764 ,  G06V10/82 ,  H04N23/63 ,  G06N5/022 ,  G06V2201/09

Abstract: Systems, methods, devices, media, and computer readable instructions are described for local image tagging in a resource constrained environment. One embodiment involves processing image data using a deep convolutional neural network (DCNN) comprising at least a first subgraph and a second subgraph, the first subgraph comprising at least a first layer and a second layer, processing, the image data using at least the first layer of the first subgraph to generate first intermediate output data; processing, by the mobile device, the first intermediate output data using at least the second layer of the first subgraph to generate first subgraph output data, and in response to a determination that each layer reliant on the first intermediate data have completed processing, deleting the first intermediate data from the mobile device. Additional embodiments involve convolving entire pixel resolutions of the image data against kernels in different layers if the DCNN.

公开(公告)号：US11545170B2

公开(公告)日：2023-01-03

申请号：US17247137

申请日：2020-12-01

Applicant: Snap Inc.

Inventor： Jinxi Guo ,  Jia Li ,  Ning Xu

IPC: G10L25/30 ,  H04S7/00 ,  G06K9/62 ,  G06N3/04 ,  G06V20/00

Abstract: An acoustic environment identification system is disclosed that can use neural networks to accurately identify environments. The acoustic environment identification system can use one or more convolutional neural networks to generate audio feature data. A recursive neural network can process the audio feature data to generate characterization data. The characterization data can be modified using a weighting system that weights signature data items. Classification neural networks can be used to generate a classification of an environment.