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.