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    SYSTEM AND METHOD FOR MANAGING SERVICE REQUESTS OF A RESTORING DEVICE USING A DIGITAL TWIN
    2.
    发明公开
    SYSTEM AND METHOD FOR MANAGING SERVICE REQUESTS OF A RESTORING DEVICE USING A DIGITAL TWIN 审中-公开

    公开(公告)号：US20240289161A1

    公开(公告)日：2024-08-29

    申请号：US18175665

    申请日：2023-02-28

    申请人： Dell Products L. P.

    发明人： Parminder Singh Sethi ,  Atishay Jain ,  Nithish Kn

    IPC分类号： G06F9/46 ,  G06F9/54

    CPC分类号： G06F9/461 ,  G06F9/546

    摘要： A method for managing restoration includes receiving, by a restoration orchestrator, a restoration request for the restoration by a restoring device, in response to the restoration request: identifying a downtime preparation time for the restoration, collecting, during the downtime preparation time, service requests from the restoring device, forwarding the service requests to a digital twin device, after the forwarding, making a determination that the downtime preparation time is complete, and based on the determination, initiating a re-servicing of the restoring device, wherein the restoration is complete after the determination.

    Microservice management using machine learning
    4.
    发明授权
    Microservice management using machine learning 有权

    公开(公告)号：US11934947B2

    公开(公告)日：2024-03-19

    申请号：US16677890

    申请日：2019-11-08

    申请人： Dell Products L. P.

    发明人： Shubham Gupta ,  Hung The Dinh ,  Sabu Syed ,  Ramu Kannappan ,  Jatin Kamlesh Thakkar

    IPC分类号： G06N3/08 ,  G06F40/284 ,  G06N3/044

    CPC分类号： G06N3/08 ,  G06F40/284 ,  G06N3/044

    摘要： In some examples, a computing device may implement a method that includes receiving microservice profile information at a microservice profiler, performing lexical analysis of the microservice profile information (where the lexical analysis produces tokenized information), generating microservice modification information by performing machine learning analysis of one or more inputs (where the one or more inputs comprise the tokenized information), and outputting the microservice modification information from the microservice profiler. The microservice profile information describes one or more characteristics of a microservice. The lexical analysis is performed by a lexical analysis engine of the microservice profiler, and the machine learning analysis is performed by a machine learning system of the microservice profiler.

    Automated file naming and file organization using machine learning
    8.
    发明授权
    Automated file naming and file organization using machine learning 有权

    公开(公告)号：US11625361B2

    公开(公告)日：2023-04-11

    申请号：US16855428

    申请日：2020-04-22

    申请人： Dell Products L. P.

    发明人： Sathish Kumar Bikumala ,  Ares Sakamoto ,  Deepak NagarajeGowda

    IPC分类号： G06F16/00 ,  G06F16/16 ,  G06N20/00 ,  G06F16/13

    摘要： In some examples, before saving a file, a device analyzes contents (e.g., text and images) of the file to create a bag of words. The device performs Latent Dirichlet Allocation of the bag of words to determine topics included in the file and orders the topics in descending order of frequency. The device generates a particular name of the file based on the ordered topics and automatically save (or suggest saving) the file with the particular name. The device determines, based on the ordered topics, a folder name of a folder in which to store the file. If an existing folder has the folder name, then the device saves the file in the existing folder. If not, then the device creates a new folder with the folder name and saves the file in the new folder.

    System for allocating task processing between an IoT device and an edge device
    9.
    发明授权
    System for allocating task processing between an IoT device and an edge device 有权

    公开(公告)号：US11620167B2

    公开(公告)日：2023-04-04

    申请号：US16864181

    申请日：2020-05-01

    申请人： Dell Products L. P.

    发明人： Shubham Gupta ,  Rangan Basu ,  Rohan Sharma

    IPC分类号： G06F9/44 ,  G06F9/50 ,  G06N3/04 ,  G06N3/08

    摘要： Methods and systems are disclosed for allocating tasks between apparatus in an IoT system in a manner to generally minimize the total amount of time to execute the tasks. At least one embodiment includes a computer-implemented method for allocating task processing between an internet of things (IoT) device and an edge device. The computer-implemented method includes collecting data from one or more sensors to execute a task having data size Xt; predicting a space complexity data size Xc for the task based on data size Xt, and allocating data for processing between the IoT device and edge device as a function of Xc. In at least one embodiment, the space complexity data size Xc is determined by applying Xt to the input of a long short-term memory neural network.