METHODS AND SYSTEMS FOR GARBAGE COLLECTION AND COMPACTION FOR KEY-VALUE ENGINES

    公开(公告)号:US20240020231A1

    公开(公告)日:2024-01-18

    申请号:US18475664

    申请日:2023-09-27

    CPC classification number: G06F12/0253 G06F2212/7205

    Abstract: Methods and systems for garbage collection and compaction for key-value engines in a data storage and communication system. The method includes determining disk capacity usage of the key-value engine and adjusting a garbage collection percentage threshold and a number of garbage collection threads based on whether the disk capacity usage of the key-value engine meets and/or exceeds predetermined disk capacity usage thresholds. The method may further include performing a periodic compaction process to consolidate one or more expired pages of one or more applications on a log-structured merge (LSM) tree by merging one or more layers into a last layer of the one or more expired pages to reduce data handling during an occurrence of the garbage collection.

    DATA PROCESSING METHOD, AND NON-TRANSITORY MEDIUM AND ELECTRONIC DEVICE

    公开(公告)号:US20230418794A1

    公开(公告)日:2023-12-28

    申请号:US18252926

    申请日:2021-11-06

    Applicant: Lemon Inc.

    CPC classification number: G06F16/215 G06F21/602

    Abstract: The present disclosure relates to a data processing method, non-transitory medium and electronic device. The method includes: acquiring first user data and second user data, and initializing a first time window corresponding to the first user data and first time information corresponding to the first time window, as well as a second time window corresponding to the second user data and second time information corresponding to the second time window; determining first data and the first time information corresponding to the first time window based on the first user data; determining second data and the second time information corresponding to the second time window based on the second user data; based on the first data and the second data, determining alignment data corresponding to a same user from the first user data and the second user data.

    DATA PROCESSING METHOD AND APPARATUS, AND ELECTRONIC DEVICE

    公开(公告)号:US20230418470A1

    公开(公告)日:2023-12-28

    申请号:US18252982

    申请日:2021-11-15

    Applicant: LEMON INC.

    CPC classification number: G06F3/0604 G06F3/0638 G06F3/0683

    Abstract: Disclosed in the embodiments of the present disclosure are a data processing method and apparatus, and an electronic device. A specific implementation of the method comprises: determining a target first storage region from among a preset number of first storage regions; on the basis of a interaction process with a second device, determining identical target identification information comprised in the target first storage region and a target second storage region, and determining whether the interaction process meets a target requirement; and in response to the interaction process meeting the target requirement, storing target first data identified by the target identification information, so that the second device stores target second data identified by the target identification information. Thus, the target first data and the target second data identified by the same target identification information in the target first storage region and the target second storage region are aligned.

    System and method for training a transformer-in-transformer-based neural network model for audio data

    公开(公告)号:US11854558B2

    公开(公告)日:2023-12-26

    申请号:US17502863

    申请日:2021-10-15

    Applicant: Lemon Inc.

    CPC classification number: G10L19/02 G10L25/30

    Abstract: Devices, systems and methods related to causing an apparatus to generate music information of audio data using a transformer-based neural network model with a multilevel transformer for audio analysis, using a spectral and a temporal transformer, are disclosed herein. The processor generates a time-frequency representation of obtained audio data to be applied as input for a transformer-based neural network model; determines spectral embeddings and first temporal embeddings of the audio data based on the time-frequency representation of the audio data; determines each vector of a second frequency class token (FCT) by passing each vector of the first FCT in the spectral embeddings through the spectral transformer; determines second temporal embeddings by adding a linear projection of the second FCT to the first temporal embeddings; determines third temporal embeddings by passing the second temporal embeddings through the temporal transformer; and generates music information based on the third temporal embeddings.

    Display screen or portion thereof with a graphical user interface

    公开(公告)号:USD1008282S1

    公开(公告)日:2023-12-19

    申请号:US29778676

    申请日:2021-04-14

    Applicant: LEMON INC.

    Designer: Ryan Northway

    Abstract: FIG. 1 is a front view of a first embodiment of a display screen or portion thereof with a graphical user interface showing our new design;
    FIG. 2 is a front view of a second embodiment thereof; and,
    FIG. 3 is a front view of a third embodiment thereof.
    The outermost broken line rectangle illustrates a display screen or portion thereof and forms no part of the claimed design.

    Synchronizing multiple instances of projects

    公开(公告)号:US11842190B2

    公开(公告)日:2023-12-12

    申请号:US17578299

    申请日:2022-01-18

    Applicant: Lemon Inc.

    CPC classification number: G06F8/77 H04L7/0016

    Abstract: The present disclosure describes techniques for synchronizing multiple instances of projects. At least one Transmission Control Protocol (TCP) connection may be established between a server computing device and at least one client computing device. At least one dual instance command may be created. The at least one dual instance command comprises data associated with a project and information indicating how to interpret the data. A plurality of instances of the project may be synchronized between the server computing device and the at least one client computing device by transmitting the at least one dual instance command between the server computing device and the at least one client computing device via the at least one TCP connection.

    Secure multi-party computation and communication

    公开(公告)号:US11836263B1

    公开(公告)日:2023-12-05

    申请号:US18297545

    申请日:2023-04-07

    Applicant: Lemon Inc.

    CPC classification number: G06F21/62 G06F7/507 H04L9/0869

    Abstract: Protecting data privacy in secure multi-party computation and communication is provided. A method of protecting data privacy includes determining a differential privacy configuration, determining a number of iterations based on a first parameter and a second parameter, and for each of the number of iterations generating a random value and a random noise data; generating a first message and a second message; and performing a transfer based on the first message, the second message, and an input data to output one of the first message and the second message. The method also includes generating a first noise data based on the random noise data in each of the number of iterations, generating a first share based on a first dataset and a second dataset, applying the first noise data to the first share, and constructing a result based on the first share and a second share.

    NEURAL NETWORK MODEL FOR AUDIO TRACK LABEL GENERATION

    公开(公告)号:US20230386437A1

    公开(公告)日:2023-11-30

    申请号:US17804198

    申请日:2022-05-26

    Applicant: Lemon Inc.

    CPC classification number: G10H1/0008 G06N3/08 G10H2210/056

    Abstract: System and methods directed to identifying music theory labels for audio tracks are described. More specifically, a first training set of audio portions may be generated from a plurality of audio tracks, segments within the plurality of audio tracks being labeled according to a plurality of music theory labels. A deep neural network model may then be trained using the first training set as an input, a first loss function for music theory label identifications of audio portions of the first training set, and a second loss function for segment boundary identifications within the audio portions of the first training set. In examples, the music theory label identifications and the segment boundary identifications are generated by the deep neural network model. A first audio track is received and segment boundary identifications and music theory labels for segments within the first audio track are generated using the deep neural network model.

    Procedural pattern generation for layered two-dimensional augmented reality effects

    公开(公告)号:US11830106B2

    公开(公告)日:2023-11-28

    申请号:US17531462

    申请日:2021-11-19

    Applicant: Lemon Inc.

    CPC classification number: G06T11/00 G06T7/12 G06T2207/10016 G06T2207/30196

    Abstract: Methods, systems and storage media for applying a pattern application effect to one or more frames of video are disclosed. Some examples may include: obtaining video data including one or more video frames, determining one or more segments in each of the one or more video frames, determining one or more object masks based on the one or more segments in each of the one or more video frames, combining, the one or more object masks into a single mask, obtaining pattern information, the pattern information representing one or more graphical effects to be applied to at least one layer of the one or more video frames, applying the pattern information to the single mask to generate masked pattern information and generating, by the computing device, a rendered video by adding the masked pattern information to the one or more video frames.

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