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    DYNAMIC MONITORING, DETECTION OF EMERGING COMPUTER EVENTS
    2.
    发明申请
    DYNAMIC MONITORING, DETECTION OF EMERGING COMPUTER EVENTS 审中-公开

    公开(公告)号：WO2020197897A1

    公开(公告)日：2020-10-01

    申请号：PCT/US2020/023469

    申请日：2020-03-19

    Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

    Inventor： WILSON, Alexander James ,  NECKERMANN, Tom ,  VAN BRUGGEN, Simone

    IPC: H04L29/06 ,  G06F21/55 ,  H04L12/24

    Abstract: Technologies are provided for the monitoring, detection, and notification of emerging, related issues within a system, which may indicate a problem. Within a computing-security system, a sudden increase in the frequency of events associated with unauthorized logon attempts signal a real-time and ongoing security risk. A method monitors system-related events and generates a vector representation for each event based on event features. Clusters of related events are determined, and a state automaton is employed to determine a strength of temporal "bursty" activity for each cluster. Hypothesis testing is performed on each cluster to determine a likelihood that the cluster is a temporally emergent cluster. Clusters with a bursting likelihood above a threshold are determined to be an emergent cluster associated with an anomalous issue. A notification regarding the detected anomaly is provided. A remedial action addressing the anomaly is performed. Noisy clusters are filtered and aggregated based on their bursting likelihood and overlapping sub-spaces of the hyperspace.

    MACHINE LEARNING APPLICATIONS FOR TEMPORALLY-RELATED EVENTS
    3.
    发明申请
    MACHINE LEARNING APPLICATIONS FOR TEMPORALLY-RELATED EVENTS 审中-公开

    公开(公告)号：WO2020123323A1

    公开(公告)日：2020-06-18

    申请号：PCT/US2019/065118

    申请日：2019-12-08

    Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

    Inventor： WILSON, Alexander James ,  NECKERMANN, Tom ,  VAN BRUGGEN, Simone

    IPC: G06F40/30 ,  G06F40/284 ,  G06F11/07 ,  G06F16/332

    Abstract: Systems and methods for enhanced classification of sequences of objects based on clique similarity and metadata associated with the sequences are presented. Sequences are received. Events are detected based on analyzing k-skip-n-grams included in the sequences. For each event of the detected plurality of events, a graph is generated. The graph for a particular event includes z-cliques that correspond to portions of the k-skip-n-grams that are included in the sequences that are associated with the particular event. A first sequence, which is separate from the other sequences, is received. The first sequence includes a first plurality of k-skip-n-grams. A trained classifier is employed to classify the first sequence as being associated with a first event of the detected events. Classifying the first sequence is based on a comparison between the first plurality of k-skip-n-grams and the z-cliques of the graph that is generated for the first event.

    MACHINE LEARNING FOR PERSONALIZED, USER-BASED NEXT ACTIVE TIME PREDICTION
    4.
    发明申请
    MACHINE LEARNING FOR PERSONALIZED, USER-BASED NEXT ACTIVE TIME PREDICTION 审中-公开

    公开(公告)号：WO2021236210A1

    公开(公告)日：2021-11-25

    申请号：PCT/US2021/022027

    申请日：2021-03-12

    Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

    Inventor： WILSON, Alexander ,  NECKERMANN, Tom ,  REY, Romain

    IPC: G06Q10/10

    Abstract: Techniques performed by a data processing system for predicting availability of a user include receiving, from a first computing device over a network connection, a first request for predicted availability of a first user to participate in an online communication session, wherein the first request includes an identifier associated with the first user and a time period for the predicted availability of the user, in response to receiving the first request, determining a first predicted availability of the first user over the predicted time period using a first machine learning model trained with user information from a plurality of data sources, the user information being indicative of when the user is likely to be available to participate in the online communication session, and sending, to the first computing device over the network connection, availability information including the first predicted availability of the first user.

    AUGMENTED DATA INSIGHT GENERATION AND PROVISION
    6.
    发明申请
    AUGMENTED DATA INSIGHT GENERATION AND PROVISION 审中-公开

    公开(公告)号：WO2022093358A1

    公开(公告)日：2022-05-05

    申请号：PCT/US2021/045629

    申请日：2021-08-12

    Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

    Inventor： NECKERMANN, Tom ,  WILSON, Alexander James ,  REY, Romain Gabriel Paul

    IPC: G06Q10/10 ,  G06N20/00 ,  G06F16/383

    Abstract: In the present disclosure, artificial intelligence (AI) processing is trained and leveraged to learn user-specific insights that are contextually relevant to a state of a user communication. Contextual information about a state of a user communication may be collected and analyzed. That contextual information may be cross-referenced with an extensive knowledge graph that is constructed from user context data. Exemplary AI processing may further be trained to apply a relevance analysis to assist with processing described herein including generation and curation of data insights that are most relevant to a state of a user communication. In some examples, the data insight generation process may be augmented by pre-generating data insights that may be relevant to a user communication prior to occurrence of the user communication. Further technical examples pertain to the rendering and presentation of representations of data insights through a graphical user interface (GUI).

    REAL-TIME CONTENT OF INTEREST DETECTION AND NOTIFICATION FOR MEETINGS
    8.
    发明申请
    REAL-TIME CONTENT OF INTEREST DETECTION AND NOTIFICATION FOR MEETINGS 审中-公开

    公开(公告)号：WO2022250847A1

    公开(公告)日：2022-12-01

    申请号：PCT/US2022/026865

    申请日：2022-04-29

    Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

    Inventor： REY, Romain Gabriel Pa ,  WILSON, Alexander ,  NECKERMANN, Tom

    IPC: G06Q10/10

    Abstract: A meeting application server and a method for real-time content of interest detection and notification for a meeting are described herein. The meeting application server includes a processor and a computer-readable storage medium operatively coupled to the processor. The computer-readable storage medium includes computer-executable instructions that cause the processor to receive, via a remote computing system, content of interest data for a meeting. The computer-executable instructions also cause the processor to analyze the content of interest data to determine a theme of interest for the meeting, train a classification model for the theme of interest, and generate real-time meeting data for the meeting. The computer-executable instructions further cause the processor to determine the probability that the real-time meeting data relate to the theme of interest using the classification model and, if the probability exceeds a threshold value, transmit a content of interest alert to the remote computing system.

    REALISTIC PERSONALIZED STYLE TRANSFER IN IMAGE PROCESSING
    9.
    发明申请
    REALISTIC PERSONALIZED STYLE TRANSFER IN IMAGE PROCESSING 审中-公开

    公开(公告)号：WO2022245481A1

    公开(公告)日：2022-11-24

    申请号：PCT/US2022/026244

    申请日：2022-04-26

    Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

    Inventor： WILSON, Alexander J. ,  NECKERMANN, Tom ,  REY, Romain Gabriel Paul

    IPC: G06T11/00

    Abstract: Computerized systems are provided for applying data indicative of a personal style to a feature of a user represented in one or more images based on determining or estimating the personal style. In operation, embodiments can receive a first image of a first user that indicates the personal style of the first user. The first image can then be fed to one or more machine learning models in order to learn and capture the personal style of the first user. Subsequently, some embodiments capture the first user in another image or set of images. Some embodiments can then detect one or more features of the first user in these other images and based on the determining of the user's personal style in the first image, can apply data indicative of the personal style of the first user to the one or more features of the user in these other images.

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