Method and system for creating stickers from user-generated content

    公开(公告)号:US12154200B2

    公开(公告)日:2024-11-26

    申请号:US17954098

    申请日:2022-09-27

    Abstract: Example aspects include techniques for creating video stickers from user-generated content. These techniques may include receiving selection of a video content item previously uploaded to a SMP server application corresponding to the SMP client application, and presenting a graphical user interface (GUI) for receiving one or more derivative attributes for generating a derivative video sticker from the video content item. In addition, the techniques may include adding the derivative video sticker to a library of stickers provided by the SMP server application to a plurality of SMP client applications, and presenting the derivative video sticker within a communication interface associated with the SMP server application.

    SOCIAL MEDIA NETWORK DIALOGUE AGENT

    公开(公告)号:US20250013788A1

    公开(公告)日:2025-01-09

    申请号:US18346707

    申请日:2023-07-03

    Applicant: Lemon Inc.

    Abstract: Examples are provided relating to implementing actions on social media network content based on natural language inputs. One aspect includes a computing system configured to implement a social media network, comprising one or more processors, and a storage device comprising instructions executable to receive a user input including a natural language description of a request for an action on a content item from a dialogue agent configured to engage in dialogue using at least a language model, and generate a prompt for the language model based at least on the user input. The instructions are further executable to input the prompt to the language model to generate output describing operations for implementing the action, call a backend service of the social media network to execute commands to implement the operations, and output a result of executing the commands.

    VOICE GENERATION FOR VIRTUAL CHARACTERS
    5.
    发明公开

    公开(公告)号:US20230377556A1

    公开(公告)日:2023-11-23

    申请号:US17751324

    申请日:2022-05-23

    Applicant: Lemon Inc.

    CPC classification number: G10L13/02 G06T13/40 G06T13/205

    Abstract: The present disclosure describes techniques of generating voices for virtual characters. A plurality of source sounds may be received. The plurality of source sounds may correspond to a plurality of frames of a video. The video may comprise a virtual character. The plurality of source sounds may be converted into a plurality of representations in a latent space using a first model. Each representation among the plurality of representations may comprise a plurality of parameters. The plurality of parameters may correspond to a plurality of sound features. A plurality of sounds may be generated in real time for the virtual character in the video based at least in part on modifying at least one of the plurality of parameters of each representation.

    SYSTEM EVOLVING ARCHITECTURES FOR REFINING MEDIA CONTENT EDITING SYSTEMS

    公开(公告)号:US20250014607A1

    公开(公告)日:2025-01-09

    申请号:US18346737

    申请日:2023-07-03

    Applicant: Lemon Inc.

    Abstract: Examples are provided relating to system evolving architectures for refining media content editing systems. One aspect includes a method of refining a media content editing architecture, the method comprising: editing a media content using a large language model and a back-end tool service comprising a prompt pool and a plurality of application programming interfaces corresponding to a plurality of editing tools; publishing the edited media content; storing contextual information relating to the editing of the media content; and refining the media content editing architecture using the stored contextual information.

    TECHNICAL ARCHITECTURES FOR MEDIA CONTENT EDITING USING MACHINE LEARNING

    公开(公告)号:US20250014605A1

    公开(公告)日:2025-01-09

    申请号:US18346727

    申请日:2023-07-03

    Applicant: Lemon Inc.

    Abstract: Examples are provided relating to media content editing architectures utilizing machine learning techniques. One aspect includes a method for media content editing, the method comprising: receiving a media content from a user; receiving an editing request for the media content from the user; and editing the media content based on the editing request to generate edited media content by: retrieving a prompt from a prompt pool, wherein the retrieved prompt is selected based on the editing request; parsing the retrieved prompt and the editing request using a large language model to generate one or more editing actions to be performed on the media content; and performing the one or more editing actions on the media content to generate the edited media content.

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