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公开(公告)号：US20230333518A1
公开(公告)日：2023-10-19
申请号：US18023308
申请日：2020-08-27
Applicant: NEC Corporation
Inventor： Norihito OI , Riki ETO , Yuki CHIBA , Shiichi TAKEDA
IPC: G05B13/02
CPC classification number: G05B13/0265
Abstract: An input unit 81 receives inputs of pre-change performance data acquired by a device before a change and post-change performance data acquired by the device after having undergone the change, through control using a first cost function. An update unit 82 generates a second cost function obtained by updating the first cost function in such a way as to reduce a difference between the pre-change performance data and the post-change performance data. In the process, the update unit 82 generates the second cost function obtained by updating the first cost function by estimating an error that occurs in an output value of the device included in the first cost function before and after the change to the device.
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32.发明申请公开(公告)号：US20220343184A1
公开(公告)日：2022-10-27
申请号：US17640474
申请日：2019-09-18
Applicant: NEC Corporation
Inventor： So YAMADA , Riki ETO , Junko WATANABE , Ryota SUZUKI , Hiromi SHIMIZU
Abstract: A learning apparatus acquires learning data in which odor data of each object and a label representing the object in a label space expressing features of odors are associated with each other, and learns, based on odor data, a model for predicting a label of the odor data in the label space, by using the learning data. In a data processing apparatus for processing odor data, an acquisition unit acquires odor data from an outside. A prediction unit predicts a label of the acquired odor data in the label space by using the model in which a relationship between sets of odor data and labels in the label space expressing the features of the odors is learned.
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公开(公告)号：US20220343180A1
公开(公告)日：2022-10-27
申请号：US17637138
申请日：2019-08-29
Applicant: NEC Corporation
Inventor： Riki ETO , Yuki NAKAGUCHI
Abstract: A reward function estimation unit 81 estimates a reward function by multiple importance sampling using samples of a decision-making history of a subject and of a decision-making history generated based on a sampling policy. A policy estimation unit 82 estimates a policy by reinforcement learning using the estimated reward function. The reward function estimation unit 81 sets the policy estimated by the policy estimation unit as a new sampling policy, and estimates the reward function by the multiple importance sampling using the samples of the decision-making history of the subject and of the decision-making history generated based on the sampling policy.
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公开(公告)号：US20220221839A1
公开(公告)日：2022-07-14
申请号：US17442821
申请日：2019-03-29
Applicant: NEC Corporation
Inventor： Riki ETO
IPC: G05B19/416 , G01N33/00
Abstract: An information processing apparatus (20) includes a model generating unit (210) and a feature value computation unit (220). The model generating unit (210) generates an Auto-Regressive with eXogenous input (ARX) model of a smell sensor by use of input data controlling an input operation of gas including a smell component being a measurement target, and output data acquired by inputting the gas to the smell sensor, based on the input data. The feature value computation unit (220) computes a transfer function of the smell sensor relating to the smell component by subjecting the ARX model to Z-Transform, and further computes a first-order lag transfer function feature value of the smell sensor relating to the smell component by subjecting the transfer function to partial fraction decomposition.
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公开(公告)号：US20220172843A1
公开(公告)日：2022-06-02
申请号：US17601857
申请日：2020-04-03
Applicant: NEC CORPORATION , Masao MIYASHITA
Inventor： So YAMADA , Riki ETO , Junko WATANABE , Masao MIYASHITA , Marina GOTO
Abstract: A selection unit (11) in a learning device (10) inputs a plurality of “learning candidate data units.” The plurality of learning candidate data units are respectively related to a plurality of subjects including a plurality of cancer patients and a plurality of non-cancer patients. Further, each learning candidate data unit at least includes a “urine odor data unit” and a “cancer label.” Then, from the plurality of input learning candidate data units, the selection unit (11) selects part of the plurality of learning candidate data units as a “learning target data set,” based on a “selection rule.” By using the learning target data set selected by the selection unit (11), a determination model formation unit (12) forms a “determination model” for determining which of urine of a cancer patient and urine of a non-cancer patient a determination target urine odor data unit is related to.
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Patent Agency Ranking
公开(公告)号：US20210311009A1
公开(公告)日：2021-10-07
申请号：US17262955
申请日：2018-07-31
Applicant: NEC Corporation
Inventor： Ryota SUZUKI , Riki ETO
IPC: G01N33/00
Abstract: An information processing apparatus (2000) acquires time-series data (14) output by a sensor (10) and computes a plurality of feature constants θi and a contribution value ξi representing contribution with respect to the time-series data (14) for each feature constant θi. Thereafter, the information processing apparatus (2000) outputs information in which a set Θ of the feature constants θi and a set Ξ of the contribution values ξi are associated with each other as a feature value of a target gas. As the feature constant θ, a velocity constant β or a time constant τ that is a reciprocal of the velocity constant can be adopted.
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公开(公告)号：US20210065071A1
公开(公告)日：2021-03-04
申请号：US16962253
申请日：2018-01-18
Applicant: NEC CORPORATION
Inventor： Riki ETO
Abstract: The learning unit 3 learns a regression equation based on learning data. The learning data includes a value for a state in a state space model at each time. Also, the learning data includes a set of combinations of a certain time, a value for the state at each of times from a time one time before the certain time to a time n times (n is an integer of 2 or more) before the certain time, a value for an input in the state space model at the certain time obtained one time before the certain time, and a value or values for one or more attributes at the certain time obtained one time before the certain time. The learning unit 3 learns a regression equation using the state at a future time as an objective variable and including, as explanatory variables, at least, explanatory variables each representing the state at each of times from a time one time before the future time to a time m times (m is an integer of 1 or more and n or less) before the future time and an explanatory variable representing the input at the future time obtained one time before the future time. A conversion unit 4 converts the regression equation to have a form of the state space model.
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公开(公告)号：US20200027013A1
公开(公告)日：2020-01-23
申请号：US16481715
申请日：2018-01-18
Applicant: NEC CORPORATION
Inventor： Riki ETO , Yoshio KAMEDA
Abstract: Provided is a model estimation system that can estimate a discrete time state space model having controllability. The model estimation system of the present invention estimates a model of a system that is represented by an ordinary differential equation with all the coefficients being non-zero, and with which input data and a state at each time can be obtained. When an order of the ordinary differential equation and input data and a state at multiple past times in the system are inputted, a model expression construction means 22 constructs an expression representing a model by using a first matrix that is a matrix according to the order and has only some elements as unknown elements and a second matrix that is a matrix according to the order and has only some element as an unknown element. A model estimation means 23 uses input data and a state at multiple past times, to estimate the model by learning unknown elements of the first matrix and an unknown element of the second matrix in the expression.
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39.发明申请公开(公告)号：US20180373208A1
公开(公告)日：2018-12-27
申请号：US16062722
申请日：2015-12-25
Applicant: NEC CORPORATION
Inventor： Wemer WEE , Yoshio KAMEDA , Riki ETO
Abstract: A learner unit 81 learns a quantity model for a quantity the user is interest in based on data acquired from dynamics and surroundings of a plant which is a control target. A cost function designing unit 82 designs a cost function to be used in the derivation of solutions to optimally control the plant so as to include at least the quantity model as terms.
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40.发明申请公开(公告)号：US20170076307A1
公开(公告)日：2017-03-16
申请号：US15125267
申请日：2015-02-27
Applicant: NEC Corporation
Inventor： Yosuke MOTOHASHI , Satoshi MORINAGA , Ryohei FUJIMAKI , Riki ETO , Masato ASAHARA
IPC: G06Q30/02
CPC classification number: G06Q30/0206 , G06N7/005 , G06Q30/0202
Abstract: A price estimation device that can predict a price with a high degree of precision is disclosed. Said price estimation device has a price-predicting means that predicts a price pertaining to second information in a target second time period by applying rule information to said second information, which includes explanatory variables. Said rule information represents the relationship between the explanatory variables and the price, said relationship having been extracted on the basis of a first-information set comprising first information in which explanatory-variable values are associated with price values. The explanatory variables include an attribute that represents a length of time, determined on the basis of a first time period in which a specific event occurs, pertaining to a target object associated with the aforementioned first information or the abovementioned second information. The value of said attribute in the second information is the length of time between the first time period and the second time period, and the value of the attribute in the first information is the length of time between the first time period and a third time period associated with the abovementioned price.
Abstract translation: 公开了可以高精度地预测价格的价格估计装置。 所述价格估计装置具有价格预测装置，其通过将规则信息应用于包括解释变量的所述第二信息来预测与目标第二时间段中的第二信息有关的价格。 所述规则信息表示解释变量和价格之间的关系，所述关系是基于包括解释变量值与价格值相关联的第一信息的第一信息集来提取的。 解释性变量包括表示基于与特定事件发生的第一时间段相关的与上述第一信息相关联的目标对象或上述第二信息确定的时间长度的属性。 第二信息中的所述属性的值是第一时间段和第二时间段之间的时间长度，第一信息中的属性值是第一时间段与第三时间段之间的时间长度 与上述价格相关。
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