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    GENERATION OF DOCUMENT CLASSIFIERS
    31.
    发明申请
    GENERATION OF DOCUMENT CLASSIFIERS 审中-公开

    公开(公告)号：US20180349388A1

    公开(公告)日：2018-12-06

    申请号：US15615743

    申请日：2017-06-06

    Applicant: SparkCognition, Inc.

    Inventor： Erik Skiles ,  Joshua Bronson ,  Syed Mohammad Ali ,  Keith D. Moore

    IPC: G06F17/30 ,  G06N99/00 ,  G06F3/0481 ,  G06F3/0482

    Abstract: A method includes performing, by a computing device, a clustering operation to group documents of a document corpus into clusters in a feature vector space. The document corpus includes one or more labeled documents and one or more unlabeled documents. Each of the one or more labeled documents is assigned to a corresponding class in classification data associated with the document corpus, and each of the one or more unlabeled document is not assigned to any class in the classification data. The method also includes generating, by the computing device, a prompt requesting classification of a particular document of the document corpus, where the particular document is selected based on a distance between the particular document and a labeled document of the one or more labeled documents.

    Generation and use of trained file classifiers for malware detection
    32.
    发明授权
    Generation and use of trained file classifiers for malware detection 有权

    公开(公告)号：US10062038B1

    公开(公告)日：2018-08-28

    申请号：US15610228

    申请日：2017-05-31

    Applicant: SparkCognition, Inc.

    Inventor： Na Sai

    IPC: G06N99/00 ,  G06N3/02 ,  G06F21/56

    CPC classification number: G06N20/00 ,  G06F21/562 ,  G06F2221/033 ,  G06N3/02

    Abstract: A method includes accessing information identifying multiple files and identifying classification data for the multiple files, where the classification data indicates, for a particular file of the multiple files, whether the particular file includes malware. The method also includes generating a sequence of entropy indicators for each of the multiple files, each entropy indicator of the sequence of entropy indicators for the particular file corresponding to a chunk of the particular file. The method further includes generating n-gram vectors for the multiple files, where the n-gram vector for the particular file indicates occurrences of groups of entropy indicators in the sequence of entropy indicators for the particular file. The method also includes generating and storing a file classifier using the n-gram vectors and the classification data as supervised training data.

    Natural-language processing across multiple languages
    35.
    发明授权
    Natural-language processing across multiple languages 有权

    公开(公告)号：US12079211B2

    公开(公告)日：2024-09-03

    申请号：US17933250

    申请日：2022-09-19

    Applicant: SparkCognition, Inc.

    Inventor： Erik Skiles ,  Devan Plantamura

    IPC: G06F7/00 ,  G06F16/2452 ,  G06F40/58 ,  G10L15/22 ,  G10L15/26

    CPC classification number: G06F16/24522 ,  G06F40/58 ,  G10L15/22 ,  G10L15/26

    Abstract: A method includes obtaining a query in a base language and translating the query to generate one or more translated queries each in a respective target language. The method also includes searching one or more sets of electronic files based on the one or more translated queries to generate target-language search results, where each translated query is used to search one or more electronic files that include content in the respective target language of the translated query. The method also includes, based on the target-language search results, scheduling one or more electronic files of the one or more sets of electronic files for at least partial translation to the base language.

    Calculating energy loss during an outage
    36.
    发明授权
    Calculating energy loss during an outage 有权

    公开(公告)号：US12066472B2

    公开(公告)日：2024-08-20

    申请号：US17566383

    申请日：2021-12-30

    Applicant: SPARKCOGNITION, INC.

    Inventor： Sahil Maheswari ,  Sandeep Gupta ,  Jayesh Shah ,  Kate Wessels

    IPC: G01R21/00 ,  G01R22/06 ,  G01W1/10 ,  G01W1/18 ,  G06N20/00 ,  G01R31/00

    CPC classification number: G01R21/00 ,  G01W1/10 ,  G01R22/06 ,  G01R31/003 ,  G01W1/18 ,  G06N20/00

    Abstract: Calculating energy loss during an outage, including: determining that windspeed data indicating device windspeeds measured at an energy generating device are unavailable within a particular time duration; receiving meteorological data associated with a site location of the energy generating device, the meteorological data including meteorological windspeed data collected within the particular time duration; and predicting one or more estimated device windspeeds at the energy generating device during the particular time duration based on the meteorological data using a trained model for the energy generating device, the trained model being trained using a machine learning algorithm that utilizes historical meteorological windspeed data associated with the site location collected during a previous time duration and corresponding historical device windspeed data measured at the energy generating device during the previous time duration.

    Executing a genetic algorithm on a low-power controller
    39.
    发明授权
    Executing a genetic algorithm on a low-power controller 有权

    公开(公告)号：US11829883B2

    公开(公告)日：2023-11-28

    申请号：US17017065

    申请日：2020-09-10

    Applicant: SparkCognition, Inc.

    Inventor： Syed Mohammad Amir Husain

    IPC: G06N3/08 ,  G06N3/084 ,  G06N3/126

    CPC classification number: G06N3/084 ,  G06N3/126

    Abstract: A method includes selecting a subset of models from a plurality of models. The plurality of models is generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method includes performing at least one genetic operation of the genetic algorithm with respect to at least one model of the subset to generate a trainable model. The method includes determining a rate of improvement associated with prior backpropagation iterations. The method includes selecting, based on the rate of improvement, one of the trainable model or a prior trainable model as a selected trainable model. The method includes generating the trained model including training the selected trainable model. The method includes adding the trained model as input to a second epoch of the genetic algorithm that is subsequent to the first epoch.

    MALWARE RISK SCORE DETERMINATION
    40.
    发明公开
    MALWARE RISK SCORE DETERMINATION 审中-公开

    公开(公告)号：US20230281314A1

    公开(公告)日：2023-09-07

    申请号：US17653322

    申请日：2022-03-03

    Applicant: SparkCognition, Inc.

    Inventor： Jarred Capellman

    IPC: G06F21/57 ,  H04L9/40

    CPC classification number: G06F21/577 ,  H04L63/20 ,  G06F2221/034 ,  G06F2221/033

    Abstract: A device includes one or more processors configured to collect, at a client device, device data associated with the client device. The one or more processors are configured to determine, at the client device, a risk score associated with the client device based on the device data. The risk score indicates a likelihood that the client device is vulnerable to a malware attack. The one or more processors are also configured to send the risk score from the client device to a management server. Security protocols are implemented at the client device in response to a command from the management server. The command is based at least in part on the risk score.

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