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公开(公告)号：US20230029218A1
公开(公告)日：2023-01-26
申请号：US17380189
申请日：2021-07-20
发明人： Anuradha Bhamidipaty , Yingjie Li , Shuxin Lin , Zhengliang Xue , Bhavna Agrawal
摘要： A concept associated with a feature used in machine learning model can be determined, the feature extracted from a first data source. A second data source containing the concept can be identified. An additional feature can be generated by performing a natural language processing on the second data source. The feature and the additional feature can be merged. A second machine learning model can be generated, which use the merged feature. A prediction result of the first machine learning model can be compared with a prediction result of the second machine learning model relative to ground truth data, to evaluate effective of the merged feature. Based on the evaluated effectiveness, the feature can be augmented with the merged feature in machine learning.
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公开(公告)号：US20190005512A1
公开(公告)日：2019-01-03
申请号：US15637395
申请日：2017-06-29
发明人： Pawan Chowdhary , Markus Ettl , Donald Keefer , Gabriel Toma , Zhengliang Xue
摘要： Systems, methods, and computer-readable media are disclosed for identifying customers having associated opportunities for improved growth and/or profitability with respect to product or service offerings and determining investment solutions that enhance the probability that the customers transition to the higher growth/profitability opportunities. Prior customer transactions are segmented based on segmentation criteria and used to generate a transaction graph. The nodes of the transaction graph represent the segmented transactions and client transaction paths between the nodes represent potential customer life-cycle trajectories. The transaction graph can be used to identify high-value penetration opportunities associated
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公开(公告)号：US20190108579A1
公开(公告)日：2019-04-11
申请号：US15725393
申请日：2017-10-05
摘要： Systems, methods, and computer-readable media are disclosed for identifying product configurations that are alternatives to a requested product configuration, ranking the alternative product configurations based on one or more pricing metrics, and presenting the alternative product configurations to a prospective customer, thereby providing the customer with the option of selecting an alternative product configuration in lieu of the initially requested product configuration.
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公开(公告)号：US20130132147A1
公开(公告)日：2013-05-23
申请号：US13743023
申请日：2013-01-16
发明人： Daniel P. Connors , Markus Ettl , David D. Yao , Zhengliang Xue
IPC分类号： G06Q10/06
CPC分类号： G06Q10/06315 , G06Q10/087
摘要： Freshness inventory control problem may be formulated as a stochastic dynamic program. In one aspect, a stochastic dynamic programming formulation takes as input inventory status, stochastic demand forecast and cost information associated with on-hand inventory. The stochastic dynamic programming formulation is maximized to determine order quantity and depletion quantity of the product per period.
摘要翻译： 新鲜度库存控制问题可能被形成为随机动态程序。 一方面，随机动态规划公式将作为输入库存状态，随机需求预测和与现有库存相关联的成本信息。 随机动态规划公式最大化，以确定产品每期的订单数量和耗尽量。
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公开(公告)号：US10692039B2
公开(公告)日：2020-06-23
申请号：US15270364
申请日：2016-09-20
摘要： System and method that improves cargo logistics may be presented. For instance, shipping capacity in cargo logistics may be best utilized based on providing pricing and scheduling solutions that are jointly optimized and prices differentiated based on flexibility of service request. Scheduled service and pricing may be transmitted as a signal to control execution of the cargo logistics.
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6.发明授权公开(公告)号：US10332032B2
公开(公告)日：2019-06-25
申请号：US15340548
申请日：2016-11-01
发明人： Pawan R. Chowdhary , Markus R. Ettl , Roger D. Lederman , Tim Nonner , Ulrich B. Schimpel , Zhengliang Xue , Hongxia Yang
摘要： A machine learning algorithm is trained to learn to cluster a plurality of original-destination routes in a network for transporting cargo into a plurality of clusters based on similarities of the original-destination routes, and to learn to cluster the plurality of clusters into a plurality of subgroups based on customer behavior. Influencing criteria associated with each of the subgroups may be determined and based on the influencing criteria, a price elasticity curve for each of the subgroups may be generated. Based on the price elasticity curve and current network traffic, cargo transportation price associated with each of the subgroups may be determined.
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公开(公告)号：US20140310065A1
公开(公告)日：2014-10-16
申请号：US14027810
申请日：2013-09-16
IPC分类号： G06Q30/02
CPC分类号： G06Q30/0283
摘要： A top-down and bottom-up approach that decomposes product bundles to components, classifies them into different groups corresponding to a component similarity measure, and detects their inherent values. The bundles are reassembled and characterized by several key attributes according to their component inherent values, and classified into segments. A normalized utility model is constructed for each product bundle segment, taking into account the additive effect among different commodity types and product families. The goodness of fit of the top-down and the bottom-up model may be validated. The model may be applied in an RFQ pricing environment.
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公开(公告)号：US20140310064A1
公开(公告)日：2014-10-16
申请号：US13863812
申请日：2013-04-16
IPC分类号： G06Q30/02
CPC分类号： G06Q30/0283
摘要： A top-down and bottom-up approach that decomposes product bundles to components, classifies them into different groups corresponding to a component similarity measure, and detects their inherent values. The bundles are reassembled and characterized by several key attributes according to their component inherent values, and classified into segments. A normalized utility model is constructed for each product bundle segment, taking into account the additive effect among different commodity types and product families. The goodness of fit of the top-down and the bottom-up model may be validated. The model may be applied in an RFQ pricing environment.
摘要翻译： 将产品束分解成组件的自上而下和自下而上的方法，将它们分类为与组件相似性度量相对应的不同组，并检测其固有值。 根据组件固有值重新组合并表征了几个关键属性，并将其分为几个部分。 考虑到不同商品类型和产品系列之间的加和效应，为每个产品捆绑段构建了一个标准化的实用新型。 可以验证自顶向下和自下而上的模型的适合性。 该模型可以应用在RFQ定价环境中。
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公开(公告)号：US20240220855A1
公开(公告)日：2024-07-04
申请号：US18148926
申请日：2022-12-30
发明人： Zhengliang Xue , Mo Liu , Shivaram Subramanian , Markus Ettl
IPC分类号： G06N20/00
CPC分类号： G06N20/00
摘要： A total demand model can be trained, by machine learning and using historical data. The total demand model can be configured to process current data and output first data indicating a predicted future total demand for a product. A target demand model can be trained. The target demand model can be configured to process the current data and, based on processing the current data, output a plurality of class demand models. Each class demand model can be configured to predict demand, for each of a plurality of future time periods, for a plurality of classes of the product. The class demand models configured to optimize, for each of the plurality of future time periods, a respective set of optimal prices for the respective classes of the product that maximizes total expected revenue for the product over the plurality of classes of the product.
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10.发明授权公开(公告)号：US11176492B2
公开(公告)日：2021-11-16
申请号：US16352958
申请日：2019-03-14
发明人： Pawan R. Chowdhary , Markus R. Ettl , Roger D. Lederman , Tim Nonner , Ulrich B. Schimpel , Zhengliang Xue , Hongxia Yang
摘要： A machine learning algorithm is trained to learn to cluster a plurality of original-destination routes in a network for transporting cargo into a plurality of clusters based on similarities of the original-destination routes, and to learn to cluster the plurality of clusters into a plurality of subgroups based on customer behavior. Influencing criteria associated with each of the subgroups may be determined and based on the influencing criteria, a price elasticity curve for each of the subgroups may be generated. Based on the price elasticity curve and current network traffic, cargo transportation price associated with each of the subgroups may be determined.
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