EVENT RESOLUTION AS A DYNAMIC SERVICE
    4.
    发明申请

    公开(公告)号:US20170249283A1

    公开(公告)日:2017-08-31

    申请号:US15054904

    申请日:2016-02-26

    CPC classification number: G06N5/003 G06F9/542

    Abstract: An approach is provided for optimally routing events in an IT system to solvers which provide resolutions of the events. Event streams originating from the IT system are defined. Events are classified into the event streams. An optimization problem is solved that minimizes costs incurred for using respective solvers based on constraints which include success rates of the solvers. Based on the solved optimization problem, policies are defined that associate the event streams to the solvers in a many-to-one correspondence. In real time, the defined policies are applied to the event streams. Based on the applied policies and the classified events, the events are routed to respective solvers. An indication is received that the events are resolved by the respective solvers, which reduces downtime in the IT system.

    CELLULAR HYPERVISOR
    7.
    发明申请
    CELLULAR HYPERVISOR 审中-公开

    公开(公告)号:US20200073690A1

    公开(公告)日:2020-03-05

    申请号:US16117219

    申请日:2018-08-30

    Abstract: Aspects utilize a computing processing capability of a device connected to a cellular network wherein processors are configured to determine processing capabilities of each of a plurality of devices as function of device hardware configuration, software configuration, and average idle utilization, determine first remaining uptime periods of availability that each of the plurality of devices are available for data processing as a function of respective device data comprising battery level, signal strength and usage patterns, wherein the usage patterns are power usage patterns or data usage patterns, in response to receiving a processing task that comprises a needed processing capability, identify a subset of devices that each have a threshold amount of uptime of processing capability in an amount inclusive of the needed processing capability of the task, and assign the processing task to one of the subset devices.

    Support system for cellular based resource sharing service

    公开(公告)号:US10142491B1

    公开(公告)日:2018-11-27

    申请号:US15641619

    申请日:2017-07-05

    Abstract: A system, method and program product for implementing a cellular resource sharing support system. A system is disclosed that includes: a behavior analysis system that collects usage data from a set of mobile devices in a cellular network; a subscription manager that computationally generates offers to mobile device owners to avail excess compute resources of the mobile devices to a resource sharing services system; a device manager that tracks the real-time availability and usage of participating mobile devices that are associated with mobile device owners that have accepted offers to avail excess compute resources; and an account manager that calculates a compensation for participating mobile devices.

    SEMANTIC CONSISTENCY OF EXPLANATIONS IN EXPLAINABLE ARTIFICIAL INTELLIGENCE APPLICATIONS

    公开(公告)号:US20210350275A1

    公开(公告)日:2021-11-11

    申请号:US16870202

    申请日:2020-05-08

    Abstract: An explainable artificially intelligent (XAI) application contains an ordered sequence of artificially intelligent software modules. When an input dataset is submitted to the application, each module generates an output dataset and an explanation that represents, as a set of Boolean expressions, reasoning by which each output element was chosen. If any pair of explanations are determined to be semantically inconsistent, and if this determination is confirmed by further determining that an apparent inconsistency was not a correct response to an unexpected characteristic of the input dataset, nonzero inconsistency scores are assigned to inconsistent elements of the pair of explanations. If the application's overall inconsistency score exceeds a threshold value, the system forwards information about the explanation, the offending modules, and the input dataset to a downstream machine-learning component that uses this information to train the application to better respond to future input that shares certain characteristics with the current input.

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