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公开(公告)号：US20170140414A1
公开(公告)日：2017-05-18
申请号：US14942225
申请日：2015-11-16
Applicant: ORACLE INTERNATIONAL CORPORATION
Inventor： Maxime COHEN , Jeremy KALAS , Kiran PANCHAMGAM , Georgia PERAKIS
IPC: G06Q30/02
CPC classification number: G06Q30/0235
Abstract: Systems, methods, and other embodiments associated with determining a promotion price schedule for each item in a group are described. In one embodiment, a method includes computing an item coefficient that corresponds to a change in a value of an objective function when the item is priced at the promotion price. The objective function is based on a multiple product demand model. An item coefficient is computed for each item, each time period in the price schedule, and each promotion price in a price ladder for the item. An approximate objective function is formulated that includes products of item coefficients and binary decision variables. The item coefficients, the approximate objective function, and constraints are provided to an optimizer that determines values of the decision variables that maximize the approximate objective function. A promotion price schedule is created for each item based on values of the decision variables.
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2.发明公开公开(公告)号：US20230401589A1
公开(公告)日：2023-12-14
申请号：US17749388
申请日：2022-05-20
Applicant: ORACLE INTERNATIONAL CORPORATION
Inventor： Kiran PANCHAMGAM , Zhou YANG , Santosh BAI REDDY
CPC classification number: G06Q30/0202 , G06V10/255 , B25J9/1679 , G06Q30/0201 , G06V10/761
Abstract: Systems, methods, and other embodiments for predicting a future characteristic of a target object/product are described based on a digital target image. In one embodiment, the method includes a machine learning model identifying a set of similar known product images by comparing the target product image to a group of known product images. For each similar known product image, product attributes are retrieved including historical characteristic/event data associated with each similar known product image. A predicted characteristic model for the target product is generated which is based on a similarity score combined with the historical characteristic/event data associated with each similar known product image to generate a predicted characteristic for the target product.
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公开(公告)号：US20220284386A1
公开(公告)日：2022-09-08
申请号：US17825334
申请日：2022-05-26
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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Patent Agency Ranking
公开(公告)号：US20170200180A1
公开(公告)日：2017-07-13
申请号：US15062561
申请日：2016-03-07
Applicant: ORACLE INTERNATIONAL CORPORATION
Inventor： Aswin KANNAN , Kiran PANCHAMGAM
Abstract: Systems, methods, and other embodiments associated with generating a price schedule are described. An inventory quantity for the item is allocated amongst a plurality of customer segments based on a predicted contribution of each customer segment to the objective function. For each customer segment, based on a quantity of inventory allocated to the customer segment, a promotion portion of the price schedule is determined that maximizes the objective function. Remaining inventory allocated to the plurality of customer segments at the end of the regular season is aggregated. Based on the aggregated inventory, a markdown portion of the price schedule for the item is determined that maximizes the objective function. The promotion portion and the markdown portion are combined to create a price schedule for the item. In one embodiment, a price schedule may be generated that includes promotions on top of markdown prices during the clearance season.
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公开(公告)号：US20170200104A1
公开(公告)日：2017-07-13
申请号：US14989932
申请日：2016-01-07
Applicant: ORACLE INTERNATIONAL CORPORATION
Inventor： Aswin KANNAN , Kiran PANCHAMGAM , Su-Ming WU
CPC classification number: G06Q10/06315 , G06Q10/087 , G06Q30/0201 , G06Q30/0206 , G06Q30/0223 , G06Q30/0235 , G06Q30/0283
Abstract: Systems, methods, and other embodiments associated with generating a price schedule are described. A inventory quantity for the item is allocated amongst a plurality of customer segments based at least on a predicted contribution of each customer segment to the objective function. For each customer segment, based at least on a quantity of inventory allocated to the customer segment, a promotion portion of the price schedule is determined that maximizes the objective function. A quantity of remaining inventory allocated to the plurality of customer segments at the end of the regular season is aggregated. Based at least on the aggregated inventory, a markdown portion of the price schedule for the item is determined that maximizes the objective function. The promotion portion and the markdown portion are combined to create a price schedule for the item.
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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：US20190066128A1
公开(公告)日：2019-02-28
申请号：US15685116
申请日：2017-08-24
Inventor： Lennart BAARDMAN , Tamar COHEN , Setareh BORJIAN BOROUJENI , Kiran PANCHAMGAM , Georgia PERAKIS
IPC: G06Q30/02
Abstract: Systems, methods, and other embodiments associated with predicting customer behavior are described. The method can include identifying a group comprising customers who satisfy a defined criterion, and receiving input that identifies a factor that influences a decision by the customers to purchase a product. A likelihood that the factor will induce the customers in the group to purchase the product is generated. A customer influence on the generated likelihood is estimated independently of data expressly identifying relationships between the customers in the group. The likelihood is modified by combining the likelihood and the customer influence according to a predictive model, and one or more of the customers eligible for a promotional offer related to the product is identified based, at least in part, on the modified likelihood. Transmission of the promotional offer is controlled to transmit the promotional offer to the identified customers in the group.
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公开(公告)号：US20140200964A1
公开(公告)日：2014-07-17
申请号：US13741817
申请日：2013-01-15
Applicant: ORACLE INTERNATIONAL CORPORATION
Inventor： Anahita HASSANZADEH , Andrew VAKHUTINSKY , Kiran PANCHAMGAM
IPC: G06Q30/02
CPC classification number: G06Q30/0283
Abstract: A system that determines markdown pricing for a plurality of items over a plurality of time periods receives a non-linear time-dependent problem, where the non-linear time-dependent problem comprises a demand model. The system determines approximate inventory levels for each item in each time period and, for a plurality of pair of items in a product category, determines coefficients for a change in demand of a first product at each of the plurality of time periods when a price of a second product is changed using initial prices and initial approximate inventory levels. The system generates an approximate MILP problem comprising a change of demand based on a sum of the determined coefficients. The system then solves the MILP problem to generate revised prices and revised inventory levels. The functionality is repeated until a convergence criteria is satisfied, and then the system assigns the revised prices as the markdown product pricing.
Abstract translation: 确定多个时间段内的多个项目的降价定价的系统接收非线性时间相关问题，其中非线性时间相关问题包括需求模型。 系统确定每个时间段中每个项目的近似库存水平，并且对于产品类别中的多个项目，确定在多个时间段中的每个时间段上的第一产品的需求变化的系数， 使用初始价格和初始近似库存水平更改第二个产品。 该系统基于所确定的系数的和产生包括需求变化的近似MILP问题。 该系统然后解决了MILP问题，以产生修订的价格和修订的库存水平。 重复功能，直到满足收敛标准，然后系统将修订的价格分配为降价产品定价。
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