公开(公告)号：US20160162800A1

公开(公告)日：2016-06-09

申请号：US14560484

申请日：2014-12-04

Applicant: Bin Qin ,  Farooq Azam ,  Denis Malov

Inventor： Bin Qin ,  Farooq Azam ,  Denis Malov

IPC: G06N99/00 ,  G06N3/08

Abstract: Example systems and methods of developing a learning model are presented. In one example, a sample data set to train a first learning algorithm is accessed. A number of states for each input of the sample data set is determined A subset of the inputs is selected, and the sample data set is partitioned into a number of partitions equal to a combined number of states of the selected inputs. A second learning algorithm is created for each of the partitions, wherein each second learning algorithm receives the unselected inputs. Each of the second learning algorithms is assigned to a processor and trained using the samples of the partition corresponding to that algorithm. Decision logic is generated to direct each of a plurality of operational data units as input to one of the second learning algorithms based on states of the selected inputs of the operational data unit.

Abstract translation: 介绍了开发学习模型的示例系统和方法。 在一个示例中，访问用于训练第一学习算法的样本数据集。 确定样本数据集合的每个输入的多个状态被选择输入的子集，并且样本数据集被划分成等于所选输入的组合状态数的多个分区。 为每个分区创建第二学习算法，其中每个第二学习算法接收未选择的输入。 将每个第二学习算法分配给处理器并使用与该算法相对应的分区的样本进行训练。 生成决定逻辑，以基于所选择的操作数据单元的输入的状态将多个操作数据单元中的每一个作为第二学习算法之一进行输入。

公开(公告)号：US10482389B2

公开(公告)日：2019-11-19

申请号：US14560484

申请日：2014-12-04

Applicant: Bin Qin ,  Farooq Azam ,  Denis Malov

Inventor： Bin Qin ,  Farooq Azam ,  Denis Malov

IPC: G06N3/08 ,  G06N20/00

Abstract: Example systems and methods of developing a learning model are presented. In one example, a sample data set to train a first learning algorithm is accessed. A number of states for each input of the sample data set is determined. A subset of the inputs is selected, and the sample data set is partitioned into a number of partitions equal to a combined number of states of the selected inputs. A second learning algorithm is created for each of the partitions, wherein each second learning algorithm receives the unselected inputs. Each of the second learning algorithms is assigned to a processor and trained using the samples of the partition corresponding to that algorithm. Decision logic is generated to direct each of a plurality of operational data units as input to one of the second learning algorithms based on states of the selected inputs of the operational data unit.

公开(公告)号：US09330441B2

公开(公告)日：2016-05-03

申请号：US14196755

申请日：2014-03-04

Applicant: Andjelka Srdic ,  Rafael Pacheco ,  Bin Qin ,  Denis Malov

Inventor： Andjelka Srdic ,  Rafael Pacheco ,  Bin Qin ,  Denis Malov

IPC: G06K9/40 ,  G06T5/00 ,  G01V1/34 ,  G01V1/36

CPC classification number: G06T5/002 ,  G01V1/345 ,  G01V1/364 ,  G06K9/40 ,  G06T2207/20024 ,  G06T2207/20182 ,  G06T2207/30181

Abstract: A filter selection technique is described for automatically selecting filters and filter parameters to apply to a given input data. The technique first receives input data and accesses a library storing information from previously analyzed data. The technique selects an entry from the library where the entry contains data that is correlated with the input data. The technique then applies a filter to the input data. The filter and filter parameters are determined by the selected entry.

Abstract translation: 描述了一种滤波器选择技术，用于自动选择滤波器和滤波器参数以应用于给定的输入数据。 该技术首先接收输入数据并访问从先前分析的数据存储信息的库。 该技术从库中选择一个条目，其中条目包含与输入数据相关的数据。 然后，该技术将过滤器应用于输入数据。 过滤器和过滤器参数由所选条目确定。

公开(公告)号：US20140143110A1

公开(公告)日：2014-05-22

申请号：US13682486

申请日：2012-11-20

Applicant: Bin Qin ,  Denis Malov

Inventor： Bin Qin ,  Denis Malov

IPC: G06Q40/04

CPC classification number: G06Q40/04 ,  G06Q40/10

Abstract: Example systems and methods of circular transaction path detection are presented. In one example, a directed graph comprising nodes and directed edges interconnecting the nodes is generated. The directed graph is based on information describing a plurality of parties and a plurality of transactions between the parties. A circular path length of interest is received. Strongly connected components of the directed graph are identified. Within each of the strongly connected components, each circular path having a length equal to the circular path length of interest is discovered. For each discovered circular path, the transactions represented by the directed edges of the path are denoted as related transactions.

Abstract translation: 提出了循环事务路径检测的示例系统和方法。 在一个示例中，生成包括连接节点的节点和有向边的有向图。 有向图基于描述多方的信息和双方之间的多个交易。 接收感兴趣的圆形路径长度。 确定有向图的强连接分量。 在每个强连接的部件中，发现了具有等于感兴趣的圆形路径长度的长度的每个圆形路径。 对于每个发现的循环路径，由路径的有向边表示的事务表示为相关事务。

公开(公告)号：US20150254812A1

公开(公告)日：2015-09-10

申请号：US14196755

申请日：2014-03-04

Applicant: Andjelka Srdic ,  Rafael Pacheco ,  Bin Qin ,  Denis Malov

Inventor： Andjelka Srdic ,  Rafael Pacheco ,  Bin Qin ,  Denis Malov

IPC: G06T5/00

CPC classification number: G06T5/002 ,  G01V1/345 ,  G01V1/364 ,  G06K9/40 ,  G06T2207/20024 ,  G06T2207/20182 ,  G06T2207/30181

Abstract: A filter selection technique is described for automatically selecting filters and filter parameters to apply to a given input data. The technique first receives input data and accesses a library storing information from previously analyzed data. The technique selects an entry from the library where the entry contains data that is correlated with the input data. The technique then applies a filter to the input data. The filter and filter parameters are determined by the selected entry.

Abstract translation: 描述了一种滤波器选择技术，用于自动选择滤波器和滤波器参数以应用于给定的输入数据。 该技术首先接收输入数据并访问从先前分析的数据存储信息的库。 该技术从库中选择一个条目，其中条目包含与输入数据相关的数据。 然后，该技术将过滤器应用于输入数据。 过滤器和过滤器参数由所选条目确定。

公开(公告)号：US08170905B2

公开(公告)日：2012-05-01

申请号：US11948082

申请日：2007-11-30

Applicant: Denis Malov

Inventor： Denis Malov

IPC: G06F9/46

CPC classification number: G06Q40/02 ,  G06Q30/0201

Abstract: A computer implemented method for determining the reference values of sensitivities and strategies for price optimization demand models from a profit function and current product price. A total profit objective is expressed as the maximization of profit and volume, where a strategy parameter represents the relationship between profit and volume. From the total profit objective, the bounds of the strategy parameter are expressed as conditional inequalities relating the bounds to functions of the unit profit at the current rate and average volume. The strategy parameter is then set to the average of these bounds. The reference elasticity is expressed as a function of the unit profit function and average volume. The resulting reference values can be used in a price optimization system to generate recommended prices that relate to an industry's current pricing scheme.

Abstract translation: 一种计算机实现的方法，用于从利润函数和当前产品价格确定价格优化需求模型的敏感度和策略的参考值。 总利润目标表示为利润和数量的最大化，其中策略参数表示利润和数量之间的关系。 从总利润目标来看，战略参数的界限表示为以当前利率和平均数量为单位将单位利润函数的界限相关的条件不等式。 然后将策略参数设置为这些边界的平均值。 参考弹性表示为单位利润函数和平均数量的函数。 所产生的参考值可用于价格优化系统，以产生与行业当前定价方案相关的推荐价格。

公开(公告)号：US07580852B2

公开(公告)日：2009-08-25

申请号：US11064874

申请日：2005-02-23

Applicant: Kenneth J. Ouimet ,  Denis Malov

Inventor： Kenneth J. Ouimet ,  Denis Malov

IPC: G06F17/30

CPC classification number: G06Q10/04 ,  G06Q30/0202 ,  G06Q40/00

Abstract: A non-stationary time series model using a likelihood function as a function of input data, base demand parameters, and time dependent parameter. The likelihood function may represent any statistical distribution. The likelihood function uses a prior probability distribution to provide information external to the input data and is used to control the model. In one embodiment the prior is a function of adjacent time periods of the demand profile. The base demand parameters and time dependent parameter are solved using a multi-diagonal band matrix. The solution of base demand parameters and time dependent parameter involves making estimates thereof in an iterative manner until the base demand parameters and time dependent parameter each converge. A non-stationary time series model is provided from an expression using the solution of the base demand parameters and time dependent parameter. The non-stationary time series model provides a demand forecast as a function of time.

Abstract translation: 使用似然函数作为输入数据，基本需求参数和时间相关参数的函数的非平稳时间序列模型。 似然函数可以表示任何统计分布。 似然函数使用先验概率分布来提供输入数据外部的信息，并用于控制模型。 在一个实施例中，先前是需求简档的相邻时间段的函数。 基本需求参数和时间相关参数使用多对角线频带矩阵求解。 基本需求参数和时间相关参数的解决涉及以迭代方式进行估计，直到基本需求参数和时间相关参数各自收敛。 使用基本需求参数和时间相关参数的解的表达式提供非平稳时间序列模型。 非平稳时间序列模型提供了作为时间的函数的需求预测。

公开(公告)号：US20090144123A1

公开(公告)日：2009-06-04

申请号：US11948420

申请日：2007-11-30

Applicant: Denis Malov ,  Wei Sun

Inventor： Denis Malov ,  Wei Sun

IPC: G06Q10/00

CPC classification number: G06Q40/02 ,  G06Q10/04 ,  G06Q30/0201

Abstract: A computer system is provided which models financial products such as demand deposits and time deposits. The computer system collects transactional data related to a plurality of financial products. The demand model includes an acquisition model, average balance model, and time demand renewable model for predicting customer responses to changes in interest rate based on the transactional data. The demand model evaluates consumer response through account opening, balance variations, and time deposit renewals. The demand model can also predict effects of cannibalization, seasonality, promotions, and time-dependent demand on the financial products. The cannibalization model estimates model parameters by demand group level, categorical level, and multicurrency level. The interest rate is optimized for each of the financial products by utilizing one or more of the acquisition, average balance, time demand renewable, cannibalization, seasonality, promotional, and time-dependent models. The optimized interest rate is exported to a financial institution.

Abstract translation: 提供了一个计算机系统，用于模拟诸如活期存款和定期存款的金融产品。 计算机系统收集与多个金融产品有关的交易数据。 需求模型包括收购模型，平均余额模型和时间需求可再生模型，用于根据交易数据预测客户对利率变化的反应。 需求模型通过开户，平衡差异和定期存款续期来评估消费者的反应。 需求模型还可以预测食品化，季节性，促销和时间依赖性需求对金融产品的影响。 食人族化模型根据需求群体级别，分类级别和多币种水平估算模型参数。 通过利用一次或多次采购，平均余额，可再生可再生能源，食人族化，季节性，促销和时间依赖模型，为每个金融产品优化利率。 优化利率出口到金融机构。

公开(公告)号：US20140058794A1

公开(公告)日：2014-02-27

申请号：US13595625

申请日：2012-08-27

Applicant: Denis Malov ,  Gustavo Ayres De Castro

Inventor： Denis Malov ,  Gustavo Ayres De Castro

IPC: G06Q30/02 ,  G06Q10/08

CPC classification number: G06Q10/083 ,  G06Q10/087

Abstract: A system, a computer program product, and a method for order planning and optimization are disclosed. A first data is received, where the first data represents historical shipment data of an item from a distributor to a location. The received first data is processed and a model for at least one shipping pattern of the item from the distributor to the location is determined based on the processed received first data. A forecast for a future shipping demand of the item by the location is generated based on the determined model. At least one shipping pattern of the item from the distributor to the location is optimized based on the generated forecast.

Abstract translation: 公开了一种系统，计算机程序产品和用于订单规划和优化的方法。 接收到第一数据，其中第一数据表示从经销商到位置的物品的历史出货数据。 处理接收的第一数据，并且基于处理的接收到的第一数据来确定从分发者到位置的项目的至少一个运送模式的模型。 基于确定的模型生成对该位置的未来运输需求的预测。 根据产生的预测，将经销商至该位置的物品的至少一种运输模式进行优化。

公开(公告)号：US20130159059A1

公开(公告)日：2013-06-20

申请号：US13331538

申请日：2011-12-20

Applicant: Denis Malov

Inventor： Denis Malov

IPC: G06Q30/02

CPC classification number: G06Q30/02

Abstract: Various embodiments herein include at least one of systems, methods, and software for freight market demand modeling and price optimization. Some such embodiments include acquiring historical data regarding hauled loads, bid loads that were not hauled, data representative of at least one of current and expected conditions, and data representing business goals. The acquired data may then be mapped to market segments and a statistical, spot load demand model is generated for each market segment based on a number of factors included in the mapped data including at least a load price factor. A demand and price forecast model may next be generated for each market segment based on the generated model and the data representative of at least one of current and expected conditions. For each market segment, a pricing element may then be determined based on the respective market segment model and forecast in view of the business goals.

Abstract translation: 本文的各种实施例包括用于货运市场需求建模和价格优化的系统，方法和软件中的至少一个。 一些这样的实施例包括获取关于牵引负载的历史数据，未被拖运的投标负荷，表示当前和预期条件中的至少一个的数据以及表示业务目标的数据。 所获取的数据然后可以被映射到市场段，并且基于包括在包括至少负载价格因子的映射数据中的多个因素，为每个市场段生成统计的现货负载需求模型。 基于生成的模型和表示当前和预期条件中的至少一个的数据，可以为每个市场段生成需求和价格预测模型。 对于每个细分市场，可以根据各自的市场细分模型和商业目标的预测来确定定价要素。