公开(公告)号：US20210117873A1

公开(公告)日：2021-04-22

申请号：US16736284

申请日：2020-01-07

Applicant: Oracle International Corporation

Inventor： Andrew VAKHUTINSKY ,  Setareh Borjian BOROUJENI ,  Saraswati YAGNAVAJHALA ,  Jorge Luis Rivero PEREZ ,  Dhruv AGARWAL ,  Akash CHATTERJEE

IPC: G06Q10/02 ,  G06Q10/04 ,  G06Q50/12 ,  G06Q10/06

Abstract: Embodiments provide optimized room assignments for a hotel in response to receiving a plurality of hard constraints and soft constraints and receiving reservation preferences and room features. The optimization includes determining a guest satisfaction assignment cost based on the reservation preferences and room features, determining an operational efficiency assignment cost, generating a weighted cost matrix based on the guest satisfaction assignment cost and the operational efficiency assignment cost, and generating preliminary room assignments based on the weighted cost matrix. When the preliminary room assignments are feasible, the preliminary room assignments are the optimized room assignments comprising a feasible selection of elements of the matrix. When the preliminary room assignments are infeasible, embodiments relax one or more constraints and repeat the performing optimization until the preliminary room assignments are feasible.

公开(公告)号：US20240020716A1

公开(公告)日：2024-01-18

申请号：US18049402

申请日：2022-10-25

Applicant: Oracle International Corporation

Inventor： Andrew VAKHUTINSKY ,  Jorge Luis Rivero PEREZ ,  Kirby BOSCH ,  Recep Yusuf BEKCI

IPC: G06Q30/02 ,  G06N20/20 ,  G06Q50/12

CPC classification number: G06Q30/0206 ,  G06N20/20 ,  G06Q30/0201 ,  G06Q50/12

Abstract: Embodiments upsell a hotel room selection by generating a first hierarchical prediction model corresponding to a first hotel chain, the first hierarchical prediction model receiving reservation data from one or more corresponding first hotel properties, and generating a second hierarchical prediction model corresponding to a second hotel chain, the second hierarchical prediction model receiving reservation data from one or more corresponding second hotel properties. At each of the first hierarchical prediction model and the second hierarchical prediction model, embodiments generate corresponding model parameters. At a horizontal federated server, embodiments receive the corresponding model parameters and average the model parameters to be used as a new probability distribution, and distribute the new probability distribution to the first hotel properties and the second hotel properties.

公开(公告)号：US20190122176A1

公开(公告)日：2019-04-25

申请号：US16167900

申请日：2018-10-23

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Su-Ming WU ,  Andrew VAKHUTINSKY

IPC: G06Q10/08 ,  G06Q30/02

Abstract: Systems, methods, and other embodiments associated with controlling inventory depletion by offering different prices to different customers are described. In one embodiment, a method includes establishing first and second allocations of fulfillment centers to different geographic regions during a markdown phase. Different price schedules are determined for the orders to be fulfilled during the markdown phase based on the first and second allocations. A predicted profit is generated for the orders fulfilled under each of the different price schedules. A price schedule corresponding to the first allocation is selected as resulting in a greater predicted profit than another one of the different price schedules. A sale terminal is controlled to enact the selected price schedule during the markdown phase to cause fulfillment of the incoming orders according to the first allocation of the fulfillment centers.

公开(公告)号：US20240394623A1

公开(公告)日：2024-11-28

申请号：US18321831

申请日：2023-05-23

Applicant: Oracle International Corporation

Inventor： Recep Yusuf BEKCI ,  Andrew VAKHUTINSKY ,  Saraswati YAGNAVAJHALA

IPC: G06Q10/0631 ,  G06Q10/04

Abstract: Embodiments optimize inventory assortment and allocation of a group of products, where the group of products are allocated from a plurality of different warehouses to a plurality of different retail stores. Embodiments receive historical sales data for the group of products and estimate demand model parameters of a demand model that models a demand of the group of products. Embodiments solve an optimization problem for the inventory assortment and allocation of the group of products, the optimization including a plurality of decision variables, an objective function, and a corresponding Lagrangian relaxation. The solving to generate an optimized solution includes determining a gradient of the objective function with respect to the decision variables, updating the decision variables based on a direction of the gradient and updating dual lambda variables of the Lagrangian relaxation.

公开(公告)号：US20230186411A1

公开(公告)日：2023-06-15

申请号：US17643638

申请日：2021-12-10

Applicant: Oracle International Corporation

Inventor： John Thomas COULTHURST ,  Denysse DIAZ ,  Jean-Philippe DUMONT ,  Chengyi LYU ,  Jorge Luis Rivero PEREZ ,  Andrew VAKHUTINSKY ,  Alan WOOD

IPC: G06Q50/12 ,  G06Q30/02 ,  G06N20/00

CPC classification number: G06Q50/12 ,  G06Q30/0206 ,  G06N20/00

Abstract: Embodiments optimize display ordering of reservable hotel room choices for a hotel. Embodiments receive a trained prediction demand model for the hotel, the trained prediction model including estimated coefficients, and receive a total inventory of hotel rooms for the hotel. Embodiments determine optimal Lagrangian coefficients from the estimated coefficients using a first iterative gradient search and determine optimized prices per customer based on the estimated coefficients and the optimal Lagrangian coefficients using a second iterative gradient search. Embodiments determine an offer order optimization per customer based on the optimal Lagrangian coefficients and using linear programming. Embodiments receive a request for a hotel room from a first customer, the request including one or more attributes. Based on the one or more attributes and the optimized prices per customer and the offer order optimization per customer, embodiments display an optimized ordered list of hotel room choices.

公开(公告)号：US20220414557A1

公开(公告)日：2022-12-29

申请号：US17399342

申请日：2021-08-11

Applicant: Oracle International Corporation

Inventor： Sanghoon CHO ,  Andrew VAKHUTINSKY ,  Alan WOOD ,  Jorge Luis Rivero PEREZ ,  Jean-Philippe DUMONT ,  John Thomas COULTHURST ,  Denysse DIAZ

IPC: G06Q10/06 ,  G06Q30/02 ,  G06Q10/02 ,  G06K9/62 ,  G06N20/00

Abstract: Embodiments generate a demand model for a potential hotel customer of a hotel room. Embodiments, based on features of the potential hotel customer, form a plurality of clusters, each cluster including a corresponding weight and cluster probabilities. Embodiments generate an initial estimated mixture of multinomial logit (“MNL”) models corresponding to each of the plurality of clusters, the mixture of MNL models including a weighted likelihood function based on the features and the weights. Embodiments determine revised cluster probabilities and update the weights. Embodiments estimate an updated estimated mixture of MNL models and maximize the weighted likelihood function based on the revised cluster probabilities and updated weights. Based on the update weights and updated estimated mixture of MNL models, embodiments generate the demand model that is adapted to predict a choice probability of room categories and rate code combinations for the potential hotel customer.

公开(公告)号：US20210117998A1

公开(公告)日：2021-04-22

申请号：US16784634

申请日：2020-02-07

Applicant: Oracle International Corporation

Inventor： Sanghoon CHO ,  Andrew VAKHUTINSKY ,  Saraswati YAGNAVAJHALA

IPC: G06Q30/02 ,  G06Q50/12 ,  G06K9/62 ,  G06F17/18 ,  G06N5/00 ,  G06N20/00

Abstract: Embodiments model demand and pricing for hotel rooms. Embodiments receive historical data regarding a plurality of previous guests, the historical data including a plurality of attributes including guest attributes, travel attributes and external factors attributes. Embodiments generate a plurality of distinct clusters based the plurality of attributes using machine learning soft clustering and segment each of the previous guests into one or more of the distinct clusters. Embodiments build a model for each of the distinct clusters, the model predicting a probability of a guest selecting a certain room category and including a plurality of variables corresponding to the attributes. Embodiments eliminate insignificant variables of the models and estimate model parameters of the models, the model parameters including coefficients corresponding to the variables. Embodiments determine optimal pricing of the hotel rooms using the model parameters and a personalized pricing algorithm.

公开(公告)号：US20240206766A1

公开(公告)日：2024-06-27

申请号：US18085974

申请日：2022-12-21

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Zejin DING ,  Matthew T. GERDES ,  Guang Chao WANG ,  Kenny C. GROSS ,  Andrew VAKHUTINSKY

IPC: A61B5/11 ,  A61B5/00

CPC classification number: A61B5/112 ,  A61B5/1126 ,  A61B5/7203 ,  A61B5/746 ,  A61B2562/0219 ,  G01P15/18

Abstract: Systems, methods, and other embodiments associated with detecting impairment using a vibration fingerprint that characterizes gait dynamics are described. An example method includes receiving measurements of a gait of a being from a sensor. The measurements of the gait are converted into a time series of observations for each frequency bin in a set of frequency bins. A time series of residuals is generated for each range of the set by pointwise subtraction between the time series of observations and a time series of references for each range of the set. An impairment metric is generated based on the time series of residuals. The impairment metric is compared to a threshold for the impairment. In response to the impairment metric satisfying the threshold, the being is indicated to be impaired.

公开(公告)号：US20220138783A1

公开(公告)日：2022-05-05

申请号：US17083905

申请日：2020-10-29

Applicant: Oracle International Corporation

Inventor： Andrew VAKHUTINSKY ,  Natalia KOSILOVA

IPC: G06Q30/02 ,  G06Q50/12 ,  G06Q10/06 ,  G06Q10/04 ,  G06Q10/02 ,  G06N5/02 ,  G07C9/00

Abstract: Embodiments model the demand and pricing for hotel rooms. Embodiments receive historical data regarding a plurality of previous guests and generate a multinomial logit (“MNL”) model with demand shock variables, the demand shock variables expressed using MNL utility parameters. Embodiments estimate the MNL utility parameters using a likelihood maximization and determine demand shock parameters using the estimating the MNL utility parameters. Embodiments then predict a future demand of the hotel rooms based on the demand shock parameters.

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。