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公开(公告)号：US20240395075A1
公开(公告)日：2024-11-28
申请号：US18692480
申请日：2021-10-11
Applicant: NEC Corporation
Inventor： Norihito OI , Riki Eto , Yuki Chiba
IPC: G07C5/00
Abstract: The input means 81 accepts input of observation data observed along with an operation of equipment and input of a cost function whose explanatory variable is a factor of action intended by equipment operator. The learning means 82 generates the cost function by inverse reinforcement learning using the observation data. The distribution map generation means 83 extracts weight of the explanatory variable of the generated cost function as a feature representing an intention of the operator, and generates a distribution map in which information on the cost function is placed at corresponding positions in a multidimensional space with the explanatory variables as dimensional axes according to the extracted feature.
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公开(公告)号：US20230304979A1
公开(公告)日：2023-09-28
申请号：US18023422
申请日：2020-09-02
Applicant: NEC Corporation
Inventor： Hiromi Shimizu , Shinnosuke Nishimoto , Junko Watanabe , Riki Eto , Noriyuki Tonouchi , So Yamada
IPC: G06Q30/0208 , G01N33/00
CPC classification number: G01N33/0027 , G06Q30/0208
Abstract: An information processing device is configured to include an acquisition unit, a determination unit, an instruction unit, an instruction unit, and output unit. The acquisition unit is configured to acquire measurement target and measurement environment information, and measurement environment information that a measurer can measure with an odor sensor. The determination unit is configured to determine a measurement target that the measurer should be instructed to measure, based on the measurement target and measurement environment information and the measurement environment information that can be measured, the instruction is configured to instruct the measurer to measure the determined measurement target. The output unit configured to output a reward to the measurer after the acquisition means acquires odor data of the determined measurement target.
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3.发明授权公开(公告)号：US09324026B2
公开(公告)日：2016-04-26
申请号：US14032295
申请日：2013-09-20
Applicant: NEC Corporation
Inventor： Yosuke Motohashi , Satoshi Morinaga , Ryohei Fujimaki , Riki Eto
CPC classification number: G06N5/02 , G06N7/005 , G06N99/005
Abstract: A hierarchical latent structure setting unit 81 sets a hierarchical latent structure that is a structure in which latent variables are represented by a tree structure and components representing probability models are located at nodes of a lowest level of the tree structure. A variational probability computation unit 82 computes a variational probability of a path latent variable that is a latent variable included in a path linking a root node to a target node in the hierarchical latent structure. A component optimization unit 83 optimizes each of the components for the computed variational probability. A gating function optimization unit 84 optimizes a gating function model that is a model for determining a branch direction according to the multivariate data in a node of the hierarchical latent structure, based on the variational probability of the latent variable in the node.
Abstract translation: 分层潜在结构设置单元81设置作为其中潜变量由树结构表示的结构的分层潜在结构，并且表示概率模型的分量位于树结构的最底层的节点处。 变分概率计算单元82计算作为潜在变量的路径潜变量的变分概率，所述潜变量包括在将根节点链接到分层潜在结构中的目标节点的路径中。 分量优化单元83针对所计算的变分概率优化每个分量。 门控功能优化单元84基于节点中的潜在变量的变分概率来优化门控功能模型，门控功能模型是根据层级潜在结构的节点中的多变量数据确定分支方向的模型。
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公开(公告)号：US12044667B2
公开(公告)日：2024-07-23
申请号：US17262955
申请日：2018-07-31
Applicant: NEC Corporation
Inventor： Ryota Suzuki , Riki Eto
CPC classification number: G01N33/0067 , G01N1/24 , G01N33/0009 , G01N33/0011 , G01N33/0016 , G01N33/0027 , G01N33/0031
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：US11789001B2
公开(公告)日：2023-10-17
申请号：US17280439
申请日：2018-09-28
Applicant: NEC Corporation
Inventor： Riki Eto
IPC: G01N33/00
CPC classification number: G01N33/0062 , G01N33/0036 , G01N2033/0068
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.
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Patent Agency Ranking
公开(公告)号：US20230186099A1
公开(公告)日：2023-06-15
申请号：US17922485
申请日：2020-05-11
Applicant: NEC Corporation
Inventor： Dai Kubota , Riki Eto
IPC: G06N3/092
CPC classification number: G06N3/092
Abstract: The target output means 91 outputs a plurality of second targets, which are optimization results for a first target using one or more objective functions generated in advance by inverse reinforcement learning based on decision making history data indicating an actual change to a target. The selection acceptance means 92 accepts a selection instruction from a user for a plurality of the output second targets. The data output means 93 outputs the actual change from the first target to the accepted second target as the decision making history data. The learning means 94 learns the objective function using the decision making history data.
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7.发明公开公开(公告)号：US20230166783A1
公开(公告)日：2023-06-01
申请号：US17920885
申请日：2020-04-28
Applicant: NEC Corporation
Inventor： Dai Kubota , Riki Eto
Abstract: The congestion degree calculation means 81 calculates a congestion degree at a vehicle and a stop. The diagram output means 82 outputs a modified diagram in which a current diagram is modified by optimizing an objective function learned using business history data including an actual change of a diagram. The risk calculation means 83 calculates a current risk which is a risk occurring at the present time, and a modification risk which is a risk caused by modifying the diagram, based on the congestion degree. The risk output means 84 outputs the calculated current risk and the modification risk.
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8.发明申请公开(公告)号：US20230061026A1
公开(公告)日：2023-03-02
申请号：US17800305
申请日：2020-03-13
Applicant: NEC Corporation
Inventor： Hiromi Shimizu , Shinnosuke Nishimoto , Junko Watanabe , Riki Eto , Noriyuki Tonouchi , So Yamada
IPC: G06N20/00 , G06V10/774 , G06F40/279 , G10L15/06 , G10L15/26
Abstract: A training data generation device includes a label candidate generation unit, a reception unit, and a training data generation uni. The acquisition unit is configured to acquire smell data and information pertaining to the smell data. The label candidate generation unit which generates label candidates on the basis of the information pertaining to the smell data; an output unit which outputs the generated label candidates. The reception unit is configured to receive selection of a label from the output label candidates. The training data generation unit which generates training data from the selected label and the smell data.
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公开(公告)号：US11443219B2
公开(公告)日：2022-09-13
申请号：US16481715
申请日：2018-01-18
Applicant: NEC CORPORATION
Inventor： Riki Eto , Yoshio Kameda
Abstract: A model estimation system estimates a model of a system represented by an ordinary differential equation with all 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 unit 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 one element as an unknown element. A model estimation unit uses input data and a state at multiple past times, to estimate the model by learning unknown elements of the first matrix and the unknown element of the second matrix.
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公开(公告)号：US20250077616A1
公开(公告)日：2025-03-06
申请号：US18285749
申请日：2023-04-11
Applicant: NEC Corporation
Inventor： Akira KITAOKA , Riki Eto
IPC: G06F17/16
Abstract: An AI (Artificial Intelligence) system comprising: the input means 81 accepts input of multiple candidate solutions to an optimization problem in which an objective function is express as an inner product of a feature and weight, or expressed in a bilinear form, and weight of the objective function; the optimal solution determination means 82 determines as an optimal solution, among the candidate solutions, the candidate solution that maximizes the inner product with the weight of the objective function or the candidate solution that maximizes a value of the bilinear form; and the output means 83 outputs the optimal solution.
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