公开(公告)号：US20170075372A1

公开(公告)日：2017-03-16

申请号：US15125394

申请日：2015-02-27

Applicant: NEC Corporation

Inventor： Yosuke MOTOHASHI ,  Ryohei FUJIMAKI ,  Satoshi MORINAGA ,  Riki ETO

IPC: G05F1/66 ,  G05B13/02

CPC classification number: G05F1/66 ,  G05B13/026 ,  G05B13/048 ,  G05B15/02 ,  G05B2219/25011 ,  G05B2219/40458 ,  G06N7/005 ,  G06Q10/04 ,  G06Q50/06

Abstract: An energy-amount estimation device that can predict an energy amount with a high degree of precision is disclosed. Said energy-amount estimation device has a prediction unit that, on the basis of the relationship between energy amount and one or more explanatory variables representing information that can influence said energy amount, predicts an energy amount pertaining to prediction information that indicates a prediction target. The aforementioned relationship is computed on the basis of specific learning information, within learning information in which an objective variable representing the aforementioned energy amount is associated with the one or more explanatory variables, that matches or is similar to the aforementioned prediction information.

Abstract translation: 公开了能够高精度地预测能量的能量估计装置。 所述能量估计装置具有预测单元，其基于能量量与表示能够影响所述能量的信息的一个或多个说明变量之间的关系，预测与表示预测对象的预测信息有关的能量。 上述关系是基于特定学习信息计算的，其中学习信息中表示上述能量量的客观变量与一个或多个解释变量相关联，其与上述预测信息相匹配或类似。

公开(公告)号：US20160232637A1

公开(公告)日：2016-08-11

申请号：US15021065

申请日：2014-08-21

Applicant: NEC CORPORATION

Inventor： Yosuke MOTOHASHI ,  Satoshi MORINAGA ,  Koutarou OCHIAI ,  Norihito GOTO

IPC: G06Q50/28 ,  G06Q30/02 ,  G06N7/00 ,  G06N5/04 ,  G06N99/00

CPC classification number: G06Q50/28 ,  G06N5/003 ,  G06N5/04 ,  G06N7/005 ,  G06N20/00 ,  G06Q30/0202

Abstract: This invention discloses a shipment-volume prediction device that predicts the shipment volumes of products at a new store. A classification unit (90) classifies a plurality of existing stores into a plurality of clusters. On the basis of information regarding the new store, a cluster estimation unit (91) estimates which cluster the new store will belong to. A shipment-volume prediction unit (92) estimates the shipment volumes of products at the new store by computing predicted shipment volumes for said products at existing stores that belong to the same cluster as the new store.

Abstract translation: 本发明公开了一种装运量预测装置，其预测新商店的产品的装运量。 分类单元（90）将多个现有存储分类为多个簇。 基于关于新商店的信息，群集估计单元（91）估计新商店将属于哪个群集。 出货量预测单元（92）通过计算属于与新商店相同的集群的现有存储处的所述产品的预计出货量来估计新商店的产品的出货量。

公开(公告)号：US20160210681A1

公开(公告)日：2016-07-21

申请号：US15022843

申请日：2014-08-21

Applicant: NEC CORPORATION

Inventor： Yosuke MOTOHASHI ,  Koutarou OCHIAI ,  Norihito GOTO

IPC: G06Q30/06

CPC classification number: G06Q30/0631 ,  G06Q30/0254

Abstract: This invention discloses a product recommendation device that recommends products that are selling well in many stores, not products that are selling well in only some stores. For each of a plurality of products sold at a plurality of stores, a score computation unit (90) computes a score that increases as a function of both shipment volume and the number of stores at which the product in question is being dealt. A product recommendation unit (91) recommends products that have higher scores than products being dealt at the store for which the recommendation is being made.

Abstract translation: 本发明公开了一种产品推荐装置，其推荐在许多商店销售良好的产品，而不是仅在一些商店销售的产品。 对于在多个商店中销售的多个产品中的每一个，分数计算单元（90）计算作为出货量和所讨论的产品正在被处理的商店的数量的函数而增加的分数。 产品推荐单位（91）推荐比商品正在处理的商品分数高的产品，该商品正在进行推荐。

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。

公开(公告)号：US20160224998A1

公开(公告)日：2016-08-04

申请号：US15021824

申请日：2014-08-21

Applicant: NEC CORPORATION

Inventor： Yosuke MOTOHASHI ,  Satoshi MORINAGA

IPC: G06Q30/02 ,  G06Q10/08

CPC classification number: G06Q30/0202 ,  G06Q10/083 ,  G06Q30/06

Abstract: This invention discloses an order-volume determination device that determines an appropriate order volume. A component determination unit (91) determines a component to use in a shipment-volume prediction on the basis of the following: a hierarchical hidden structure in which hidden variables are represented by a tree structure and components representing probability models are assigned to the nodes at the lowest level of said tree structure; a gate function that determines the direction in which to branch at each node of the aforementioned hierarchical hidden structure; and prediction data. On the basis of the determined component and the prediction data, a shipment-volume prediction unit (92) computes a predicted shipment volume for a product between the present time and a second point in time that is after a first point in time. An order-volume determination unit determines (93) an order volume for said product by adding or subtracting an amount corresponding to the prediction-error spread of the determined component to or from an amount obtained by subtracting, from the predicted shipment volume for the product between the present time and the abovementioned second point in time, the current inventory of the product and the amount of the product that will be received between the present time and the abovementioned first point in time.

Abstract translation: 本发明公开了一种确定适当订单量的订单卷确定装置。 分量确定单元（91）基于以下方式确定在出货量预测中使用的组件：其中隐藏变量由树结构表示的分层隐藏结构，并且代表概率模型的分量被分配给节点 所述树结构的最低水平; 门功能，其确定在上述分层隐藏结构的每个节点处分支的方向; 和预测数据。 根据所确定的分量和预测数据，出货量预测单元（92）计算当前时间与第一时间点之后的第二时间点之间的商品的预测出货量。 订单量确定单元通过将与所确定的组件的预测误差扩展相对应的量相加或减去从通过从产品的预测出货量中减去所获得的量来确定（93）所述产品的订单量 在当前时间和上述第二时间点之间，当前与当前时间和上述第一时间点之间的产品的库存和将要接收的产品的数量。

公开(公告)号：US20150088789A1

公开(公告)日：2015-03-26

申请号：US14032295

申请日：2013-09-20

Applicant: NEC Corporation

Inventor： Yosuke MOTOHASHI ,  Satoshi MORINAGA ,  Ryohei FUJIMAKI ,  Riki ETO

IPC: G06N5/02 ,  G06N99/00

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。