公开(公告)号：US20250086689A1

公开(公告)日：2025-03-13

申请号：US18814761

申请日：2024-08-26

Applicant: NEC Corporation

Inventor： Akihito KATAOKA ,  Yudai YAMAGUCHI ,  Riki ETO ,  Natsumi USUI

IPC: G06Q30/0601 ,  G06V10/40 ,  G06V10/74 ,  G06V10/94

Abstract: An extraction system includes an output unit, an acquisition unit, and an update unit. The output unit outputs a feature amount used for similarity determination by an extraction model that extracts a product similar to a target product, a set value of an importance level of the feature amount, and a change field for changing the set value of the importance level of the feature amount. The acquisition unit acquires a change value for changing the set value of the importance level of the feature amount input in the change field of the output screen. The update unit updates the set value of the importance level of the feature amount in the extraction model based on the change value. The extraction system can be used, for example, to support decision making.

公开(公告)号：US20220309397A1

公开(公告)日：2022-09-29

申请号：US17618045

申请日：2019-06-19

Applicant: NEC Corporation

Inventor： So YAMADA ,  Riki ETO ,  Junko WATANABE ,  Hiromi SHIMIZU ,  Hidetaka HANE ,  Shigeo KIMURA ,  Wataru FUJII ,  Tomoyuki KAWABE

IPC: G06N20/00 ,  G01N33/02

Abstract: To mitigate degradation in the accuracy of a prediction model by re-learning the prediction model with consideration given to the characteristics of a detection value of a sensor.
This prediction model re-learning device comprises: a calculation unit that, on the basis of data related to smell detection by a sensor, calculates an index for determining whether or not to re-learn a prediction model for smell; and a re-learning unit that re-learns the prediction model in cases where the calculated index satisfies a predetermined condition.

公开(公告)号：US20190188344A1

公开(公告)日：2019-06-20

申请号：US16311240

申请日：2017-06-20

Applicant: NEC Corporation

Inventor： Riki ETO ,  Yoshio KAMEDA

IPC: G06F17/50

CPC classification number: G06F17/5009 ,  G05B13/04 ,  G06F2217/16

Abstract: A linear parameter varying model estimation means (83) estimates a linear parameter varying model of a target system based on input data and output data of the target system collected under a condition around each endpoint of an operating region. When a determination is made that the prediction performance is not good, a data addition instruction means (85) outputs a message indicating an instruction for adding input data and output data of the target system collected under a condition corresponding to a point in the operating region. When the input data and the output data of the target system are additionally input, the linear parameter varying model estimation means (83) further uses the input data and the output data to estimate the linear parameter varying model.

公开(公告)号：US20230306270A1

公开(公告)日：2023-09-28

申请号：US18023225

申请日：2020-08-31

Applicant: NEC Corporation

Inventor： Riki ETO

IPC: G06N3/092

CPC classification number: G06N3/092

Abstract: A first inverse reinforcement learning execution unit 91 derives each weight of candidate features, which are plural features as candidates, included in a first objective function by inverse reinforcement learning using the candidate features. A feature selection unit 92 selects a feature when one feature is selected from the candidate features, from which each weight is derived, in such a manner that a reward represented using the feature is estimated to get the closest to an ideal reward result. A second inverse reinforcement learning execution unit 93 generates a second objective function by inverse reinforcement learning using the selected feature.

公开(公告)号：US20230281506A1

公开(公告)日：2023-09-07

申请号：US17922029

申请日：2020-05-11

Applicant: NEC Corporation

Inventor： Dai KUBOTA ,  Riki ETO

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: The first output means 81 outputs a second target, which is an optimization result for a first target using an objective function generated in advance by inverse reinforcement learning based on decision making history data indicating an actual change to the target. The second output means 82 outputs a third target indicating a target resulting from further changing of the second target based on a change instruction regarding the second target accepted from the user. The data output means 83 outputs the actual change from the second target to the third target as decision making history data. The learning means 84 learns the objective function using the decision making history data.

公开(公告)号：US20230110600A1

公开(公告)日：2023-04-13

申请号：US17908968

申请日：2020-03-17

Applicant: NEC Corporation

Inventor： So YAMADA ,  Junko WATANABE ,  Riki ETO ,  Hiromi SHIMIZU ,  Noriyuki TONOUCHI

IPC: G01N33/00

Abstract: In a noise removing apparatus, a data acquisition unit acquires sets of odor data measured using a sensor with respect to a plurality of objects, each set of odor data representing features of an odor of an object by respective rates of a plurality of odor molecules. A noise component extraction unit extract a noise component using a set of odor data. A noise removing unit removes the noise component from each set of odor data to be processed.

公开(公告)号：US20220018823A1

公开(公告)日：2022-01-20

申请号：US17294146

申请日：2018-11-16

Applicant: NEC Corporation

Inventor： Riki ETO ,  Hiromi SHIMIZU

IPC: G01N33/00

Abstract: An information processing apparatus (2000) acquires a feature vector (20) obtained based on signal data (14) of a detected value of a sensor (10) that senses gas to be measured. The information processing apparatus (2000) decomposes the feature vector (20) into a product of a coefficient vector and a feature matrix by using a non-negative matrix factorization (NMF). The detected value of the sensor (10) changes according to an attachment and a detachment of a molecule contained in a sensed gas. A value of each element of the feature vector (20) is equal to or greater than zero.

公开(公告)号：US20220003732A1

公开(公告)日：2022-01-06

申请号：US17280439

申请日：2018-09-28

Applicant: NEC Corporation

Inventor： Riki ETO

IPC: G01N33/00

Abstract: An information processing apparatus (20) includes a sensor output data acquisition unit (210), a prediction equation generation unit (220), and an operation setting unit (230). The sensor output data acquisition unit (210) acquires sensor output data for each sampling length of an odor sensor with respect to a target gas. The prediction equation generation unit (220) generates, by using the sensor output data for each sampling length, a prediction equation for making a prediction for an odor component of the target gas. The operation setting unit (230) determines, by using the prediction equation, a sampling length for operating the odor sensor.

公开(公告)号：US20200336543A1

公开(公告)日：2020-10-22

申请号：US16753279

申请日：2018-10-02

Applicant: NEC CORPORATION

Inventor： Junko WATANABE ,  Riki ETO ,  Hidetaka HANE ,  Shigeo KIMURA ,  Shintarou TSUCHIYA

IPC: H04L29/08 ,  G01N33/00

Abstract: A terminal apparatus 20 includes a sensor data collection unit 21 that collects sensor data from an odor sensor 40 that outputs the sensor data in reaction to a plurality of types of odors, an analyzer acquisition unit 22 that, in the case where an analyzer capable of analyzing a designated odor analysis target is transmitted thereto from a server apparatus 10 that holds a plurality of analyzers for analyzing odor analysis targets by analyzing the sensor data, acquires the analyzer transmitted thereto, an analysis execution unit 23 that executes analysis processing of the designated odor analysis target, by applying the acquired analyzer to the collected sensor data, and an analysis result holding unit 24 that holds information indicating a result of the analysis processing.

公开(公告)号：US20180299847A1

公开(公告)日：2018-10-18

申请号：US15578935

申请日：2015-09-25

Applicant: NEC CORPORATION

Inventor： Riki ETO ,  Ryohei FUJIMAKI

IPC: G05B13/04 ,  G06F17/18

Abstract: An initial value determination means 71 determines an initial value of a scheduling parameter of a target system. Furthermore, a convergence determination means 75 determines whether the value of a predetermined evaluation function has converged. Until it is determined that the value of the predetermined evaluation function has converged, a state variable calculation means 72 repeatedly calculates a value of a state variable, a regression coefficient calculation means 73 repeatedly calculates a value of a regression coefficient, and a scheduling parameter prediction model derivation means repeatedly derives a scheduling parameter prediction model and calculates the value of the scheduling parameter. When the value of the predetermined evaluation function converges, a model estimation means 76 estimates a linear parameter-varying model of the target system on the basis of the value of the state variable and the value of the scheduling parameter at that point in time.