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    GROUP-BASED COMMUNICATION PLATFORM INTERACTION GRAPHING
    3.
    发明申请
    GROUP-BASED COMMUNICATION PLATFORM INTERACTION GRAPHING 有权

    公开(公告)号：US20220351142A1

    公开(公告)日：2022-11-03

    申请号：US17246444

    申请日：2021-04-30

    Applicant: Slack Technologies, Inc.

    Inventor： Joseph Michael McGarr ,  Keeley Hammond ,  Anuj Nair ,  Deani Hansen ,  Samuel Attard

    IPC: G06Q10/10 ,  G06Q10/06 ,  H04L12/58

    Abstract: Techniques for identifying actual communications and data flow between users of an organization exchanged via a communication platform and providing a recommended action for a user to perform based on the actual communications are described herein. The communication platform receives messages exchanged between users of the organization and generates an interaction graph representative of actual interactions (e.g., communications, data flow, etc.) between the users. The interaction graph can be based on a frequency of messages, message content (e.g., message data, attached documents, links, applications, etc.), context data (e.g., personal, business, collaborative (e.g., associated with a project, etc.), etc.), user data associated with the users (e.g., position, title, team identifiers, etc.). The communication platform compares the interaction graph to an organizational graph associated with the organization to determine a recommended action for a user to perform, such as based on an unexpected level of interaction between two or more users.

    DRAFT MESSAGE OBJECT COLLABORATION IN A GROUP-BASED COMMUNICATION PLATFORM
    4.
    发明申请
    DRAFT MESSAGE OBJECT COLLABORATION IN A GROUP-BASED COMMUNICATION PLATFORM 有权

    公开(公告)号：US20220337537A1

    公开(公告)日：2022-10-20

    申请号：US17234137

    申请日：2021-04-19

    Applicant: Slack Technologies, Inc.

    Inventor： Tyler Lewis ,  Joseph Michael McGarr ,  Felix Rieseberg ,  Issac Gerges ,  Anuj Nair

    IPC: H04L12/58 ,  H04L12/18 ,  G06F3/0484 ,  G06F40/169

    Abstract: Draft message object collaboration in a group-based communication platform is described. A request to generate a message object can be received from a first client associated with a first entity of a group-based communication platform. A first instance of a composition user interface, configured to enable a draft of the message object to be generated, can be presented via the first client and a request to add a second entity as a collaborator can be received. A second instance of the composition user interface can be presented via a second client of the second entity. A modification to the draft of the message object can be received and, in response to receiving the modification, the message object can be updated as presented via respective instances of the composition user interface.

    OPTIMIZING APPLICATION PERFORMANCE WITH MACHINE LEARNING
    6.
    发明申请
    OPTIMIZING APPLICATION PERFORMANCE WITH MACHINE LEARNING 有权

    公开(公告)号：US20220308981A1

    公开(公告)日：2022-09-29

    申请号：US17213707

    申请日：2021-03-26

    Applicant: Slack Technologies, Inc.

    Inventor： Jenna Zeigen ,  Natalie Qabazard ,  Anuj Nair ,  Aaron Maurer ,  Yiling Chen

    IPC: G06F11/34 ,  G06N20/00

    Abstract: Media, methods, and systems are disclosed for optimizing performance of a running application in connection with a group-based communication system. Log data is collected regarding prior metrics for applications that have encountered performance events. Application state information is monitored and a machine-learning model mapping application metrics to performance outcomes predicts whether the running application will encounter a performance event. The machine-learning model mapping application metrics to performance outcomes is trained based on the collected logs. Based on whether a degradation outcome will be impactful, an application performance parameter may be degraded.

    METHOD, APPARATUS, AND COMPUTER PROGRAM PRODUCT FOR ORGANIZING THE BOOTING OPERATION OF A GROUP-BASED COMMUNICATION BROWSER SESSION
    7.
    发明申请
    METHOD, APPARATUS, AND COMPUTER PROGRAM PRODUCT FOR ORGANIZING THE BOOTING OPERATION OF A GROUP-BASED COMMUNICATION BROWSER SESSION 有权

    公开(公告)号：US20210099519A1

    公开(公告)日：2021-04-01

    申请号：US16588731

    申请日：2019-09-30

    Applicant: SLACK TECHNOLOGIES, INC.

    Inventor： Mark Christian ,  Anuj Nair ,  James Whimpey ,  Drew Schuster

    IPC: H04L29/08 ,  H04L29/06 ,  H04L12/18

    Abstract: Various methods, apparatuses, and computer program products are provided for initiating a group-based communication browser session. An example computer-implemented method includes generating a group-based communication browser session initiation request via a processor of the apparatus. The computer-implemented method also includes loading a boot data object. The boot data object includes one or more predefined parameters of a booting operation stored locally on the client device and the boot data object is applicable to a plurality of executable software applications. The computer-implemented method further includes downloading at least one asset data and application data based on a specific application of the plurality of executable software applications from a group-based communication system. The at least one of the asset data and the application data is configured for generation of at least a portion of a display associated with the group-based communication browser session. Corresponding apparatuses and computer program products are also provided.

    Optimizing application performance with machine learning
    9.
    发明授权
    Optimizing application performance with machine learning 有权

    公开(公告)号：US11620173B2

    公开(公告)日：2023-04-04

    申请号：US17213707

    申请日：2021-03-26

    Applicant: Slack Technologies, Inc.

    Inventor： Jenna Zeigen ,  Natalie Qabazard ,  Anuj Nair ,  Aaron Maurer ,  Yiling Chen

    IPC: G06F11/00 ,  G06F11/34 ,  G06N20/00

    Abstract: Media, methods, and systems are disclosed for optimizing performance of a running application in connection with a group-based communication system. Log data is collected regarding prior metrics for applications that have encountered performance events. Application state information is monitored and a machine-learning model mapping application metrics to performance outcomes predicts whether the running application will encounter a performance event. The machine-learning model mapping application metrics to performance outcomes is trained based on the collected logs. Based on whether a degradation outcome will be impactful, an application performance parameter may be degraded.

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