Determination device and determination method

    公开(公告)号:US11487280B2

    公开(公告)日:2022-11-01

    申请号:US16689229

    申请日:2019-11-20

    Inventor: Satoshi Amemiya

    Abstract: A determination device includes: a memory; and a processor coupled to the memory and configured to: obtain sensor data on motion of a device from a plurality of sensors, extract, from the sensor data, data related to an anomaly based on a threshold value used in detecting the anomaly with use of the sensor data, convert the data related to the anomaly into structural data having a graph structure focusing on an analogous relationship between or among the plurality of sensors, and generate a classifier that identifies a cause of the anomaly with use of the structural data.

    APPARATUS, METHOD, AND COMPUTER-READABLE, NON-TRANSITORY MEDIUM

    公开(公告)号:US20200073375A1

    公开(公告)日:2020-03-05

    申请号:US16551790

    申请日:2019-08-27

    Inventor: Satoshi Amemiya

    Abstract: A learning device includes: a memory; and a processor coupled to the memory and the processor configured to execute a process, the process comprising: generating a probability distribution with respect to each of devices, from first sensor data for learning obtained from a sensor provided in each of the devices; calculating a difference degree among each group of the probability distributions; generating a probability model by synthesizing a group of which the difference degree is less than a threshold into a single probability distribution, multiplying each coefficient with each of the probability distributions, and adding resulting probability distributions to each other; and generating a standard for abnormality determination from the probability model.

    DISPLAY METHOD, RECORDING MEDIUM, AND INFORMATION PROCESSING APPARATUS

    公开(公告)号:US20230420119A1

    公开(公告)日:2023-12-28

    申请号:US18122925

    申请日:2023-03-17

    CPC classification number: G16H40/20 G06Q10/0633

    Abstract: A display method includes accepting designation of a first workflow, first counting, by applying a plurality of objects to the first workflow, a number of object distributed by an element of a conditional branch of the first workflow for each of element at an end out of elements included in the first workflow, second counting, by applying the plurality of objects to a second workflow that is different from the first workflow, a number of objects distributed by an element of a conditional branch of the second workflow for each of element at an end out of elements included in the second workflow, and displaying the number of objects distributed to an element at the end, associating with the element at the end, for each of the first workflow and the second workflow, by a processor.

    NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM, IMPACT CALCULATION DEVICE, AND IMPACT CALCULATION METHOD

    公开(公告)号:US20210357478A1

    公开(公告)日:2021-11-18

    申请号:US17220398

    申请日:2021-04-01

    Abstract: A non-transitory computer-readable storage medium storing a program that causes a processor included in an impact calculation device to execute a process, the process includes calculating a plurality of gradient values, each of the plurality of gradient values is a gradient value corresponding to each of a plurality of sampling points of a nonlinear regression model, and calculating, as an impact, a root-mean-square of which a first gradient value included in the plurality of the gradient values at a first sampling point included in the plurality of sampling point and a second gradient value at a one or more sampling point within a predetermined range around the first sampling point.

    DETERMINATION DEVICE AND DETERMINATION METHOD

    公开(公告)号:US20200089209A1

    公开(公告)日:2020-03-19

    申请号:US16689229

    申请日:2019-11-20

    Inventor: Satoshi Amemiya

    Abstract: A determination device includes: a memory; and a processor coupled to the memory and configured to: obtain sensor data on motion of a device from a plurality of sensors, extract, from the sensor data, data related to an anomaly based on a threshold value used in detecting the anomaly with use of the sensor data, convert the data related to the anomaly into structural data having a graph structure focusing on an analogous relationship between or among the plurality of sensors, and generate a classifier that identifies a cause of the anomaly with use of the structural data.

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