TECHNIQUES FOR RECOMMENDING REPLY STICKERS

    公开(公告)号:US20250055818A1

    公开(公告)日:2025-02-13

    申请号:US18929410

    申请日:2024-10-28

    Applicant: Snap Inc.

    Abstract: Described herein is a technique for processing a received media content item (e.g., a message), received at a messaging application of a first end-user of a messaging service, to generate a selection of some predetermined number of recommended stickers. The recommended stickers are then presented in a user interface to the first end-user, allowing the first end-user to select a sticker for use in replying to the received media content item. To generate the selection of recommended stickers, in response to receiving the media content item, the messaging application processes the media content item to identify specific attributes and characteristics (e.g., text included with the message, stickers used with the message, and other contextual metadata). The identified attributes and characteristics of the received message are then processed by a scoring model to identify the predetermined number of stickers for presenting in the reply interface as recommended reply stickers.

    PROMPT MODIFICATION FOR AUTOMATED IMAGE GENERATION

    公开(公告)号:US20240295953A1

    公开(公告)日:2024-09-05

    申请号:US18116003

    申请日:2023-03-01

    Applicant: Snap Inc.

    CPC classification number: G06F3/04845 G06T11/00 G06F40/12 G06T2200/24

    Abstract: Examples disclosed herein describe prompt modification techniques for automated image generation. An image generation request comprising a base prompt is received from a user device. A plurality of prompt modifiers is identified. A processor-implemented scoring engine determines, for each prompt modifier, a modifier score. The modifier score for each prompt modifier is associated with the base prompt. One or more of the prompt modifiers are automatically selected based on the modifier scores. A modified prompt is generated. The modified prompt is based on the base prompt and the one or more selected prompt modifiers. The modified prompt is provided as input to an automated image generator to generate an image, and the image is caused to be presented on the user device.

    Prompt modification for automated image generation

    公开(公告)号:US12169626B2

    公开(公告)日:2024-12-17

    申请号:US18116003

    申请日:2023-03-01

    Applicant: Snap Inc.

    Abstract: Examples disclosed herein describe prompt modification techniques for automated image generation. An image generation request comprising a base prompt is received from a user device. A plurality of prompt modifiers is identified. A processor-implemented scoring engine determines, for each prompt modifier, a modifier score. The modifier score for each prompt modifier is associated with the base prompt. One or more of the prompt modifiers are automatically selected based on the modifier scores. A modified prompt is generated. The modified prompt is based on the base prompt and the one or more selected prompt modifiers. The modified prompt is provided as input to an automated image generator to generate an image, and the image is caused to be presented on the user device.

    TECHNIQUES FOR RECOMMENDING REPLY STICKERS
    5.
    发明公开

    公开(公告)号:US20240314091A1

    公开(公告)日:2024-09-19

    申请号:US18183794

    申请日:2023-03-14

    Applicant: Snap Inc.

    CPC classification number: H04L51/04 G06F3/04842 G06N20/00 H04L51/10 H04L51/52

    Abstract: Described herein is a technique for processing a received media content item (e.g., a message), received at a messaging application of a first end-user of a messaging service, to generate a selection of some predetermined number of recommended stickers. The recommended stickers are then presented in a user interface to the first end-user, allowing the first end-user to select a sticker for use in replying to the received media content item. To generate the selection of recommended stickers, in response to receiving the media content item, the messaging application processes the media content item to identify specific attributes and characteristics (e.g., text included with the message, stickers used with the message, and other contextual metadata). The identified attributes and characteristics of the received message are then processed by a scoring model to identify the predetermined number of stickers for presenting in the reply interface as recommended reply stickers.

    AUTOMATIC IMAGE QUALITY EVALUATION
    6.
    发明公开

    公开(公告)号:US20240296535A1

    公开(公告)日:2024-09-05

    申请号:US18176843

    申请日:2023-03-01

    Applicant: Snap Inc.

    Abstract: Examples disclosed herein describe techniques for automatic image quality evaluation. A first set of images generated by a first automated image generator and a second set of images generated by a second automated image generator are accessed. A first machine learning model generates a first quality indicator for each image in the first set of images and the second set of images. A second machine learning model generates a second quality indicator for each image in the first set of images and the second set of images. Based on the generated indicators, a first image from the first set of images and a second image from the second set of images are automatically selected and compared. A first ranking of the first automated image generator and the second automated image generator is generated based on the comparison, and ranking data is caused to be presented on a device.

    CHATBOT RESPONSE SYSTEM
    8.
    发明申请

    公开(公告)号:US20250150414A1

    公开(公告)日:2025-05-08

    申请号:US18388153

    申请日:2023-11-08

    Applicant: Snap Inc.

    Abstract: A computer-implemented method and system for responding to user posts containing images with relevant image responses during conversation between a user and a chatbot. The system receives an image post from the user and generates a description of the image using an image-to-text model. User intent is determined based on the image and description. If responding with an image is appropriate based on the user intent, the system generates a prompt using the image description and passes it to a text generation model to create an image description and caption. The image description and caption are used to synthesize a new image. The resulting image and caption are packaged into a post that is provided as a response to the user. The system uses machine learning pipelines and models to analyze images, detect inappropriate content, classify user intent, generate text, and synthesize images.

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