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：US20230297775A1

公开(公告)日：2023-09-21

申请号：US18323900

申请日：2023-05-25

Applicant: Snap Inc.

Inventor： Vitor Rocha de Carvalho ,  Luis Carlos Dos Santos Marujo ,  Leonardo Ribas Machado das Neves

IPC: G06F40/263 ,  G06N20/00 ,  G06F40/284 ,  G06F40/295

CPC classification number: G06F40/263 ,  G06N20/00 ,  G06F40/284 ,  G06F40/295

Abstract: Systems, devices, media, and methods are presented for generating a language detection model of a language analysis system. The systems and methods access a set of messages including text elements and convert the set of messages into a set of training messages. The set of training messages are configured for training a language detection model. The systems and methods train a classifier based on the set of training messages. The classifier has a set of features representing word frequency, character frequency, and a character ratio. The systems and methods generate a language detection model based on the classifier and the set of features.

公开(公告)号：US11687720B2

公开(公告)日：2023-06-27

申请号：US17306010

申请日：2021-05-03

Applicant: Snap Inc.

Inventor： Di Lu ,  Leonardo Ribas Machado das Neves ,  Vitor Rocha de Carvalho ,  Ning Zhang

IPC: G06F40/295 ,  G06N20/00 ,  G06N3/08 ,  G06F40/30

CPC classification number: G06F40/295 ,  G06F40/30 ,  G06N3/08 ,  G06N20/00

Abstract: A caption of a multimodal message (e.g., social media post) can be identified as a named entity using an entity recognition system. The entity recognition system can use a visual attention based mechanism to generate a visual context representation from an image and caption. The system can use the visual context representation to identify one or more terms of the caption as a named entity.

公开(公告)号：US11620001B2

公开(公告)日：2023-04-04

申请号：US16948018

申请日：2020-08-27

Applicant: Snap Inc.

Inventor： William Brendel ,  Francesco Barbieri ,  Xin Chen ,  Wei Chu ,  Venkata Satya Pradeep Karuturi ,  Luis Carlos Dos Santos Marujo ,  Leonardo Ribas Machado das Neves

IPC: G06F40/166 ,  G06F3/023 ,  G06N3/084 ,  G06K9/62 ,  G06F3/04817 ,  H04L51/04 ,  G06F40/274

Abstract: Symbol prediction can be implemented using a multi-task system trained for different tasks. The tasks may include a single symbol prediction, symbol category prediction, and symbol subcategory prediction. Categories of symbols can be generated by clustering sets of training data using a clustering scheme.

公开(公告)号：US11468883B2

公开(公告)日：2022-10-11

申请号：US16948582

申请日：2020-09-24

Applicant: Snap Inc.

Inventor： Leonardo Ribas Machado das Neves ,  Vitor Silva Sousa ,  Shubham Vij

IPC: G10L15/18 ,  H04L51/10 ,  G10L15/197

Abstract: A messaging system performs trend analysis on content produced by users of the messaging system. The messaging system is configured to extract modifications from content items received from client devices associated with users where the content items are modified using the modifications that comprises a text caption or a media overlay. The messaging system is further configured to determine one or more words from the content items and the extracted modifications and determine a frequency of the one or more words in the content items and the extracted modifications. The messaging system is further configured to determine whether the one or more words is a trend based on the frequency and an aggregate frequency. The messaging system is further configured to in response to the one or more words being determined as the trend, generating trend content associated with the one or more words, the trend content being a text, an image, or an augmentation content.

公开(公告)号：US20220207080A1

公开(公告)日：2022-06-30

申请号：US17248400

申请日：2021-01-22

Applicant: Snap Inc.

Inventor： Vítor Silva Sousa ,  Nils Murrugarra-Llerena ,  Leonardo Ribas Machado das Neves ,  Neil Shah

IPC: G06F16/58 ,  G06K9/62 ,  G06K9/00 ,  G06N3/08

Abstract: A messaging system performs engagement analysis based on labels associated with content items produced by users of the messaging system. The messaging system is configured to process content items comprising images to identify elements in the images and determine labels for the images based on conditions indicating when to associate a label of the labels with an image of the images based on the elements in the image. The messaging system is further configured to associate the label with the content item, in response to determining to associate the label with the image, associating the label with the content item. The messaging system is further configured to determine engagement scores for the label based on interactions of users with the content items associated with label and adjust the engagement scores to determine trends in the labels to generate adjusted engagement scores.

公开(公告)号：US11120334B1

公开(公告)日：2021-09-14

申请号：US16125615

申请日：2018-09-07

Applicant: Snap Inc.

Inventor： Vitor Rocha de Carvalho ,  Leonardo Ribas Machado das Neves ,  Seungwhan Moon

IPC: G06N3/08 ,  G06K9/66 ,  H04L12/58 ,  H04L29/08

Abstract: A caption of a multimodal message (e.g., social media post) can be identified as a named entity using an entity recognition system. The entity recognition system can use an attention-based mechanism that emphasis or de-emphasizes each data type (e.g., image, word, character) in the multimodal message based on each datatypes relevance. The output of the attention mechanism can be used to update a recurrent network to identify one or more words in the caption as being a named entity.