公开(公告)号：US12045851B2

公开(公告)日：2024-07-23

申请号：US17359908

申请日：2021-06-28

Applicant: Kinaxis Inc.

Inventor： Kanchana Padmanabhan ,  Anneya Golob ,  Brian Keng

IPC: G06Q30/0242 ,  G06F40/40 ,  G06N5/04 ,  G06N20/00 ,  G06Q30/0201 ,  G06Q30/0202 ,  G06Q30/0241 ,  G06Q30/0251

CPC classification number: G06Q30/0244 ,  G06F40/40 ,  G06N5/04 ,  G06N20/00 ,  G06Q30/0201 ,  G06Q30/0202 ,  G06Q30/0264 ,  G06Q30/0276

Abstract: Systems and methods for constraint-based optimization, comprising: an AI demand forecasting engine, an optimization engine, a user-defined objective, and a user-defined set of constraints. Using historical sales data, the AI demand forecasting engine generates a plurality of entities, each entity defined by a placement of an item in a promotion platform; and forecasts the objective associated with each entity. The optimization engine generates a plurality of plans, each plan consisting of a unique subset of entities. Plans that violate at least one constraint are eliminated by the optimization engine, leaving a set of candidate solutions. An optimum plan is selected from the set of candidate solutions based on maximization of the objective.

公开(公告)号：US12118482B2

公开(公告)日：2024-10-15

申请号：US17153257

申请日：2021-01-20

Applicant: Kinaxis Inc.

Inventor： Brian Keng ,  Anneya Golob ,  Yifeng He

IPC: G06Q10/04 ,  G06F17/11 ,  G06F18/21 ,  G06N7/01 ,  G06Q10/0637

CPC classification number: G06Q10/04 ,  G06F17/11 ,  G06F18/217 ,  G06N7/01 ,  G06Q10/0637

Abstract: A system and method for optimizing an objective having discrete constraints using a dataset, the dataset including a plurality of aspects associated with the objective. The method comprising: receiving the dataset, the objective, and constraints, at least one of the constraints comprising discrete values; receiving a seed solution comprising initial values for the at least the constraints; iteratively performing until a predetermined threshold is reached: determining a constraint space for each of the constraints have discrete values using a determination of a constraint satisfaction problem; determining an optimized value of the objective using an optimization model, the optimization model taking as input the dataset and the constraint space; and outputting the optimized objective.

公开(公告)号：US12039564B2

公开(公告)日：2024-07-16

申请号：US17371366

申请日：2021-07-09

Applicant: Kinaxis Inc.

Inventor： Brian Keng ,  Fan Zhang ,  Kanchana Padmanabhan

IPC: G06Q30/0242 ,  G06F18/214 ,  G06N5/01 ,  G06Q30/0201 ,  G06Q30/0207

CPC classification number: G06Q30/0242 ,  G06F18/214 ,  G06N5/01 ,  G06Q30/0201 ,  G06Q30/0223 ,  G06Q30/0244

Abstract: There is provided a method and system for generating an output analytic for a promotion. The method includes determining, using an optimization machine learning model trained or instantiated with an optimization training set, at least one determined parameter for the promotion which optimizes at least one of received input parameters, the optimization training set comprising received historical data; forecasting, using a promotion forecasting machine learning model trained or instantiated with an forecasting training set, at least one output analytic of the promotion, the prediction training set comprising the received historical data, the at least one received input parameter and the at least one determined parameter; and outputting the at least one output analytic to the user.

公开(公告)号：US11928616B2

公开(公告)日：2024-03-12

申请号：US16647748

申请日：2018-09-18

Applicant: Kinaxis Inc.

Inventor： Brian Keng ,  Kanchana Padmanabhan

IPC: G06N20/00 ,  G06F18/214 ,  G06N20/20 ,  G06Q10/04

CPC classification number: G06Q10/04 ,  G06F18/214 ,  G06N20/20

Abstract: A system and method for generation of automated forecasts for a subject based on one or more input parameters. The subject located at an end node of a hierarchy. The method includes: receiving historical data associated with the subject; determining the sufficiency of the historical data based on a feasibility of building a machine learning model to generate a forecast with a predetermined level of accuracy using the historical data; building the machine learning model using the historical data when there is sufficiency of the historical data; building the machine learning model using historical data associated with an ancestor node on the hierarchy when there is not sufficiency of the historical data; generating a forecast for the subject using the machine learning model based on the one or more input parameters; and outputting the forecast.