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    MACHINE LEARNING MODEL GENERATION APPARATUS, MACHINE LEARNING MODEL GENERATION METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM
    5.
    发明公开
    MACHINE LEARNING MODEL GENERATION APPARATUS, MACHINE LEARNING MODEL GENERATION METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM 审中-公开

    公开(公告)号：US20230420129A1

    公开(公告)日：2023-12-28

    申请号：US18210428

    申请日：2023-06-15

    Applicant: NEC Corporation

    Inventor： Yuki KOSAKA ,  Shinto Eguchi

    IPC: G16H50/20

    CPC classification number: G16H50/20

    Abstract: A machine learning model generation apparatus includes: a movement unit that performs movement processing of moving a sample, having an output error of a (t+1)-th order machine learning model with respect to observation data at time t+1 being larger than a predetermined amount, from the target sample group to a source sample group; and a generation unit that generates a plurality of weak learners by using at least observation data of a sample included in the target sample group after the movement processing and a sample included in the source sample group after the movement processing, and generates a t-th order machine learning model, based on at least each of the plurality of weak learners, and a classification error being evaluated, for each of the plurality of weak learners, by using observation data at time t of the sample included in the target sample group after the movement processing.

    METHOD AND SYSTEM FOR SIGNAL PROCESSING REHABILITATION EXERCISE SIGNALS
    6.
    发明申请
    METHOD AND SYSTEM FOR SIGNAL PROCESSING REHABILITATION EXERCISE SIGNALS 有权

    公开(公告)号：US20210362005A1

    公开(公告)日：2021-11-25

    申请号：US16963385

    申请日：2019-01-16

    Applicant: NEC Corporation

    Inventor： Yuki KOSAKA ,  Yusuke TAKAHASHI ,  Charles CHOY

    IPC: A63B24/00 ,  G06K9/62

    Abstract: The present disclosure relates to method and system for signal processing rehabilitation exercise signals. The method comprises the step of receiving a first and a second motion signals associated with movements of a body part, wherein the motion signals comprise temporal data of the movements. The method further comprises the step of segmenting each of the first and second motion signals into a plurality of segmented signals based on gradients of the motion signals, wherein each segmented signal has consistent gradient. The method further comprises the step of automatically modifying the segmented signals to form multiple combinations of matching signals with similar gradients between the first and second motion signals, such that the first and second motion signals are in one-to-one correspondence. The method further comprises the step of extracting corresponding time intervals of the matching signals in the correspondences.

    KNOWLEDGE GENERATION SYSTEM, METHOD, AND PROGRAM
    7.
    发明申请
    KNOWLEDGE GENERATION SYSTEM, METHOD, AND PROGRAM 有权

    公开(公告)号：US20210343413A1

    公开(公告)日：2021-11-04

    申请号：US17284612

    申请日：2018-10-25

    Applicant: NEC Corporation

    Inventor： Yuki KOSAKA ,  Masahiro KUBO

    IPC: G16H50/20 ,  G06F40/40 ,  G06F40/289 ,  G06F40/166

    Abstract: A sentence extraction means extracts a sentence that is different from a cause or a coping method included in knowledge corresponding to a predetermined event and is related to the predetermined event, from an unconfirmed work record. A group selection means selects a group based on a similarity between a first character string of the extracted sentence that is likely to represent a cause or a coping method and a group generated in advance. A character string selection means selects a second character string from the group, and a replacement means replaces the first character string in the work record with the second character string. When a predetermined condition is satisfied, a knowledge generation means selects a character string from the group and generates new knowledge based on the character string.

    INFORMATION PROCESSING DEVICE
    8.
    发明公开
    INFORMATION PROCESSING DEVICE 审中-公开

    公开(公告)号：US20240249841A1

    公开(公告)日：2024-07-25

    申请号：US18419976

    申请日：2024-01-23

    Applicant: NEC Corporation

    Inventor： Yuki KOSAKA

    IPC: G16H50/20 ,  G16H10/60

    CPC classification number: G16H50/20 ,  G16H10/60

    Abstract: An information processing device 100 of the present disclosure includes: an acquisition unit 121 that acquires a model that is generated for each elapsed period, and has learned by machine learning to output a measure for a human by receiving input of a plurality of types of feature value representing a condition of the human; a collection unit 122 that collects first output that is obtained when a predetermined number of types of feature value are input to the model of each elapsed period, and second output that is obtained when some types of feature value in the predetermined number of types of feature value are input to the model of each elapsed period; and a setting unit 123 that sets, on the basis of the first output and the second output, types to be associated with the model of each elapsed period. Thereby, the information processing device 100 can be used for assistance of decision-making by a user, or the like.

    INFORMATION PROCESSING METHOD
    10.
    发明公开
    INFORMATION PROCESSING METHOD 审中-公开

    公开(公告)号：US20230395259A1

    公开(公告)日：2023-12-07

    申请号：US18022728

    申请日：2020-08-28

    Applicant: NEC Corporation

    Inventor： Yuki KOSAKA ,  Kenji Araki ,  Makoto Yasukawa

    IPC: G16H50/30 ,  G16H50/50

    CPC classification number: G16H50/30 ,  G16H50/50

    Abstract: An information processing apparatus 100 includes an input unit 121 and a generation unit 122. The input unit 121 receives input of a first assessment value representing assessment of a subject at a predetermined point of time and input of a second assessment value representing assessment of the subject after a predetermined time elapsed from the predetermined point of time. The first and second assessment values are values for each of an item of the Stroke Impairment Assessment Set (SIAS) and an item of a second index, different from the SIAS, for assessing the condition of a human body. The generation unit 122 generates a model for calculating the second assessment value with respect to the first assessment value for each item of the SIAS and the second index, on the basis of information representing a relationship between the item of the SIAS and the item of the second index.

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