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公开(公告)号：US20170212921A1
公开(公告)日：2017-07-27
申请号：US15007381
申请日：2016-01-27
Applicant: ORACLE INTERNATIONAL CORPORATION
Inventor： Su-Ming WU , Setareh BORJIAN BOROUJENI
CPC classification number: G06F16/23 , G06F16/211 , G06F16/221 , G06F16/2365 , G06F16/24 , G06F16/24573 , G06N7/005 , G06N20/00 , G06Q10/00
Abstract: Systems, methods, and other embodiments associated with extracting attributes from electronic data structures are described. In one embodiment, a method includes correlating tokens from description strings with defined attributes in an electronic inventory database by identifying which of the defined attributes match the tokens to link the tokens with columns of the database associated with the defined attributes. The method includes iteratively updating annotation strings for unidentified ones of the tokens by generating suggested matches for the unidentified tokens according to known correlations between identified tokens and the defined attributes using a conditional random fields model. The method also includes populating the database using the identified tokens from the description strings according to the annotation strings by automatically storing the tokens from the description strings into the columns as identified by the annotation strings.
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公开(公告)号：US20170068962A1
公开(公告)日：2017-03-09
申请号：US14845792
申请日：2015-09-04
Applicant: ORACLE INTERNATIONAL CORPORATION
Inventor： Kiran V. PANCHAMGAM , Su-Ming WU
CPC classification number: G06Q30/0251 , G06Q30/01 , G06Q30/02 , G06Q30/0204
Abstract: Systems, methods, and other embodiments associated with forecasting customer channel choice using cross-channel loyalty are described. In one embodiment, a method includes accessing historical values for each of one or more loyalty variables for respective customers. The method also includes determining respective loyalty variable predictors for each of the one or more loyalty variables for each customer based on the historical values. In response to a trigger event associated with a given customer, the loyalty variable predictors for the customer are used to estimate a present value of each of the one or more loyalty variables for the customer. The present value of each of the loyalty variables is input to a forecast model that calculates, for each channel, a probability that the customer will make a purchase using the channel. The purchase probabilities are provided for use in selecting a marketing message for the customer.
Abstract translation: 描述了使用跨渠道忠诚度预测客户渠道选择相关联的系统，方法和其他实施例。 在一个实施例中，一种方法包括访问各个客户的一个或多个忠诚度变量中的每一个的历史值。 该方法还包括基于历史值确定针对每个客户的一个或多个忠诚度变量中的每一个的相应忠诚度变量预测器。 响应于与给定客户相关联的触发事件，客户的忠诚度变量预测器用于估计客户的一个或多个忠诚度变量中的每一个的现值。 每个忠诚度变量的现值被输入到预测模型，该预测模型针对每个通道计算客户使用该频道进行购买的概率。 提供购买概率用于为客户选择营销信息。
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公开(公告)号：US20200320467A1
公开(公告)日：2020-10-08
申请号：US16375911
申请日：2019-04-05
Applicant: ORACLE INTERNATIONAL CORPORATION
Inventor： Su-Ming WU , Kiran PANCHAMGAM , Bernard GRIFFITHS
IPC: G06Q10/08
Abstract: One example of computerized inventory redistribution control includes, for each location inventory record in a set of location inventory records, calculating a quantity change that will bring a current item quantity to a different item quantity for the location inventory record. Determining a cost of a minimum-cost redistribution among the physical locations to effect the quantity changes. Determining a scaling factor that maximizes total revenue when the quantity changes are scaled by the scaling factor after deducting the cost scaled by the scaling factor. Generating transfer instructions for a redistribution of the item by scaling the transfer quantities of the minimum-cost redistribution by the scaling factor. Transmitting each transfer instruction to a computing device associated with a physical location indicated in the transfer instruction.
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公开(公告)号：US20140358633A1
公开(公告)日：2014-12-04
申请号：US13906824
申请日：2013-05-31
Applicant: Oracle International Corporation
Inventor： Su-Ming WU , Sandeep TIWARI
IPC: G06Q30/02
CPC classification number: G06Q30/0202
Abstract: A demand transference forecast system receives for a category of merchandise de-promoted sales data for each of a plurality of stock keeping units (“SKUs”), similarities between each pair of SKUs in the category, and SKU-store ranging information. The system determines a sales indices of all SKUs in the category across the de-promoted sales data for the category. The system determines Total Assortment Effect (“TAE”) variable quantities for the SKUs across share intervals in the de-promoted sales data based on the sales indices and the similarities. The system then generates a single parameter based demand transference model based on the similarities, the sales indices, and ratios of the share intervals.
Abstract translation: 需求转移预测系统接收多个库存单位（“SKU”）中的每一个的促销销售数据类别，类别中的每对SKU之间的相似性和SKU存储测距信息。 该系统确定该类别中所有类别的所有SKU的销售指数，用于该类别的促销销售数据。 该系统根据销售指数和相似性，在去推销的销售数据中确定SKU跨股份间隔的总分类效应（“TAE”）变量。 然后，该系统基于相似性，销售指数和份额间隔的比率生成基于单个参数的需求转移模型。
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公开(公告)号：US20230297948A1
公开(公告)日：2023-09-21
申请号：US17698666
申请日：2022-03-18
Applicant: Oracle International Corporation
Inventor： Setareh BORJIAN , Su-Ming WU , Shenghao WANG
CPC classification number: G06Q10/087 , G06Q30/0605
Abstract: Embodiments predict supply chain policies using machine learning. A machine learning model trained to predict one or more supply chain metrics for a first product can be stored. The machine learning model can generate a plurality of supply chain metric predictions for the first product using a plurality of candidate replenishment policies for the first product. A candidate replenishment policy with a corresponding supply chain metric prediction that meets a criteria can be selected. The selected replenishment policy can be implemented for the first product within an inventory system, where one or more physical locations are restocked with the first product based on restocking parameters defined by the selected replenishment policy.
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Patent Agency Ranking
公开(公告)号：US20220036289A1
公开(公告)日：2022-02-03
申请号：US16952468
申请日：2020-11-19
Applicant: ORACLE INTERNATIONAL CORPORATION
Inventor： Setareh BORJIAN BOROUJENI , Su-Ming WU
Abstract: Systems, methods, and other embodiments associated with self-tuning optimization of a replenishment policy of an item are described. In one embodiment, the method includes determining an initial replenishment policy of the item. A performance of the initial replenishment policy is determined based upon past performance of the initial replenishment policy. The initial replenishment policy is revised to get a service level of the item for future sales periods closer to a target service level of the item. Information is forwarded in real-time about the revised replenishment policy to an order fulfillment facility.
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7.发明申请公开(公告)号：US20170323333A1
公开(公告)日：2017-11-09
申请号：US15583328
申请日：2017-05-01
Applicant: ORACLE INTERNATIONAL CORPORATION
Inventor： Su-Ming WU , Mark E. FERGUSON , Olga PAK , Olga PERDIKAKI
CPC classification number: G06Q30/0247 , G06F16/24 , G06Q10/06311 , G06Q30/00
Abstract: Systems, methods, and other embodiments associated with computing and generating schedule data structures for items in a display are described. In one embodiment, a method includes accessing a sales data structure corresponding to a store and analyzing sales records for items associated with subcategories to calculate a subcategory profit contribution score for each subcategory. The method may also include selecting a first subcategory from the subcategories as a candidate subcategory of items and analyzing the sales records to calculate an item profit contribution score for each of the items assigned to the candidate subcategory. A first item is selected from the candidate subcategory to be placed on a promotional display space, based upon the item profit contribution score of the first item. A schedule data structure is generated that assigns the first item to the promotional display space.
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公开(公告)号：US20210142126A1
公开(公告)日：2021-05-13
申请号：US16682147
申请日：2019-11-13
Applicant: Oracle International Corporation
Inventor： William M. WARRICK, II , Su-Ming WU , Stephen CLEGG , Randall FERNANDES
Abstract: Embodiments detect fraud of risk targets that include both customer accounts and cashiers. Embodiments receive historical point of sale (“POS”) data and divide the POS data into store groupings. Embodiments create a first aggregation of the POS data corresponding to the customer accounts and a second aggregation of the POS data corresponding to the cashiers. Embodiments calculate first features corresponding to the customer accounts and second features corresponding to the cashiers. Embodiments filter the risk targets based on rules and separate the filtered risk targets into a plurality of data ranges. For each combination of store groupings and data ranges, embodiments train an unsupervised machine learning model. Embodiments then apply the unsupervised machine learning models after the training to generate first anomaly scores for each of the customer accounts and cashiers.
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公开(公告)号：US20200380452A1
公开(公告)日：2020-12-03
申请号：US16426360
申请日：2019-05-30
Applicant: Oracle International Corporation
Inventor： Andrew VAKHUTINSKY , Kiran V. PANCHAMGAM , Su-Ming WU
Abstract: Embodiments optimize the inventory allocation of a retail item that is provided from a plurality of warehouses to a plurality of price zones, each of the warehouses adapted to allocate inventory of the retail item to at least two of the price zones via links. Embodiments generate an initial inventory allocation for each warehouse to price zone link to generate a plurality of warehouse to price zone allocations. For each of the warehouse to price zone allocations, embodiments determine a marginal profit as a function of inventory allocated. Embodiments construct a bi-partite graph corresponding to each warehouse to price zone allocation, each bi-partite graph having a link weight equal to the marginal profit. Embodiments determine when there is a positive weight path between any two price zones and then reallocate the initial inventory allocation and repeat the functionality.
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公开(公告)号：US20200342475A1
公开(公告)日：2020-10-29
申请号：US16380185
申请日：2019-04-10
Applicant: Oracle International Corporation
Inventor： Su-Ming WU , Andrew VAKHUTINSKY , Setareh Borjian BOROUJENI , Santosh Bai REDDY , Kiran V. PANCHAMGAM , Sajith VIJAYAN , Mengzhenyu ZHANG
Abstract: Embodiments determine a price schedule for an item by, for each item, receiving a set of prices for the item, an inventory quantity for the item, a per-segment demand model for the item, and an objective function that is a function of the per-segment demand model and maximizes revenue based at least on a probability of a return of the item and a cost of the return. Embodiments allocate the inventory quantity among a plurality of customer segments based at least on a predicted contribution of each customer segment to the objective function. Embodiments determine a markdown portion of the price schedule for the item that maximizes the objective function, where the markdown portion assigns a series of prices selected from the set of prices for respective time periods during a clearance season for the item.
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