TECHNICAL ARCHITECTURE ASSESSMENT SYSTEM
    1.
    发明申请
    TECHNICAL ARCHITECTURE ASSESSMENT SYSTEM 审中-公开
    技术建筑评估系统

    公开(公告)号:US20160371617A1

    公开(公告)日:2016-12-22

    申请号:US14746100

    申请日:2015-06-22

    CPC classification number: G06Q10/0635

    Abstract: A technical architecture system comprises a memory, an interface, and a processor. The system stores a plurality of risk areas, receives a request to send data to a third-party entity and retrieves architecture information associated with an architecture of the third-party entity. The architecture information corresponds to at least one of the plurality of risk areas. The system also determines a risk rating of the architecture for at least one of the plurality of risk areas. The risk rating is based on the architecture information. The system may further determine a weight based on the at least one of the plurality of risk areas and determine an area score based on the risk rating and the weight. Finally, the system determines whether to grant permission to send the data to the third-party entity based at least in part on the area score.

    Abstract translation: 技术架构系统包括存储器,接口和处理器。 系统存储多个风险区域,接收向第三方实体发送数据的请求,并且检索与第三方实体的架构相关联的架构信息。 架构信息对应于多个风险区域中的至少一个。 系统还确定多个风险区域中的至少一个的架构的风险等级。 风险等级基于架构信息。 系统可以基于多个风险区域中的至少一个进一步确定权重,并且基于风险等级和权重来确定区域得分。 最后,系统至少部分地基于区域得分来确定是否授予向第三方实体发送数据的许可。

    PERFORMANCE MONITORING SYSTEM USING AGGREGATED TELEMETRY

    公开(公告)号:US20240160552A1

    公开(公告)日:2024-05-16

    申请号:US18508654

    申请日:2023-11-14

    CPC classification number: G06F11/3409 G06F11/3024

    Abstract: Systems, computer program products, and methods are described herein for performance monitoring using aggregated telemetry. The present disclosure is configured to receive, from the first performance monitoring engine, a first metadata associated with the first resiliency status; receive, from the second performance monitoring engine, a second metadata associated with the second resiliency status; determine, using a machine learning (ML) subsystem, an overall resiliency status of the device based on at least the first metadata, the second metadata, the first resiliency status, and the second resiliency status; determine one or more actions to be executed on the device, wherein the one or more actions are associated with the overall resiliency status; generate a notification indicating the overall resiliency status of the device and the one or more actions associated with the overall resiliency status; and transmit control signals configured to cause a user input device to display the notification.

    SYSTEM AND METHOD FOR ANALYZING SYSTEM HEALTH OF INDIVIDUAL ELECTRONIC COMPONENTS USING IMAGE MAPPING

    公开(公告)号:US20240177298A1

    公开(公告)日:2024-05-30

    申请号:US18522864

    申请日:2023-11-29

    CPC classification number: G06T7/001 G06T2207/30141

    Abstract: Systems, computer program products, and methods are described herein for analyzing system health of individual electronic components using image mapping. The method includes receiving a component health image for a component based on an execution of a process. The method also includes comparing the component health image based on the execution of the process to previous component health image(s) for the component based on one or more previous executions of the process The method further includes determining a component health image similarity score based on the comparison of the component health image to the one or more previous component health images for the component. The method still further includes determining a component health action based on the component health image similarity score. The component health action includes causing a transmission of an alert in an instance in which the component health image similarity score is outside of a threshold range.

    HYBRID NEURAL NETWORK FOR PREVENTING SYSTEM FAILURE

    公开(公告)号:US20240045784A1

    公开(公告)日:2024-02-08

    申请号:US17879930

    申请日:2022-08-03

    CPC classification number: G06F11/3442 G06F11/0769 G06F9/5083

    Abstract: Aspects of the disclosure relate to outage prevention. A computing platform may train, using historical parameter information and historical outage information, an outage prediction model. The computing platform may receive, from at least one system, current parameter information, and may normalize the current parameter information. The computing platform may convert, using a CNN of the outage prediction model, the normalized current parameter information to a frequency domain. The computing platform may input, into at least one RNN of the outage prediction model, the frequency domain information, to produce a likelihood of outage score. The computing platform may compare the likelihood of outage score to a predetermined outage threshold. Based on identifying that the likelihood of outage score meets or exceeds the predetermined outage threshold, the computing platform may direct the at least one system to execute a performance modification to prevent a predicted outage.

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