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：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.