Granted Patent B2

US 12,541,739 · App. 18/180,418 · Granted Feb 3, 2026

Inventors: Sebastien Ouellet (Ottawa, CA); Leila Mousapour (Ottawa, CA); Andrii Stepura (Ottawa, CA)

Assignee: Kinaxis Inc.

G06Q10/087G06Q30/0202

Abstract

Claims (155)

1 . A computer-implemented method, comprising:

pre-processing, by a processor, a historical data set the pre-processing executed once per trained machine-learning model;

forecasting, by the processor, a value of an unknown sample while tracking a leaf path of the unknown sample;

limiting, by the processor, the leaf path of the unknown sample to a subset of trees in each trained-machine model;

determining, by the processor, a set of related historical samples based on the leaf path of the unknown sample; and

determining, by the processor, a set of quantiles from the leaf path of the unknown sample.

2 . The computer-implemented method of claim 1 , wherein pre-processing the historical data set comprises:

training, by the processor, a tree-based machine learning model;

making, by the processor, a forecast for the historical data set;

tracking, by the processor, one or more leaf paths associated with the forecast for the historical data set; and

creating, by the processor, a group of similar samples in the historical data set at each branch of the tree-based machine learning model.

3 . The computer-implemented method of claim 2 , wherein training the tree-based model comprises:

preparing, by the processor, the historical data set for training;

defining, by the processor, a target variable;

selecting, by the processor, a set of features; and

building, by the processor, a decision tree.

4 . The computer-implemented method of claim 1 , wherein determining the set of related historical samples comprises:

determining, by the processor, a subset of frequently-repeating training samples from the historical data set, on the leaf path of the unknown sample;

estimating, by the processor, a probability density function for the leaf path of the unknown sample;

generating, by the processor, a set of synthetic values from the probability density function;

computing, by the processor, the quantiles based on specified service levels and the set of synthetic values; and

outputting, by the processor, a quantile forecast for the unknown sample.

5 . The computer-implemented method of claim 1 , further comprising loading inventory in accordance with the set of quantiles.

6 . A system comprising:

a processor; and

a memory storing instructions that, when executed by the processor, configure the system to:

pre-process, by the processor, a historical data set the pre-processing executed once per trained machine-learning model;

forecast, by the processor, a value of an unknown sample while tracking a leaf path of the unknown sample;

limit, by the processor, the leaf path of the unknown sample to a subset of trees in each trained-machine model;

determine, by the processor, a set of related historical samples based on the leaf path of the unknown sample; and

determine, by the processor, a set of quantiles from the leaf path of the unknown sample.

7 . The system of claim 6 , wherein when pre-processing the historical data set, the system is further configured to:

train, by the processor, a tree-based machine learning model;

make, by the processor, a forecast for the historical data set;

track, by the processor, one or more leaf paths associated with the forecast for the historical data set; and

create, by the processor, a group of similar samples in the historical data set at each branch of the tree-based machine learning model.

8 . The system of claim 7 , wherein when training the tree-based model, the system is further configured to:

prepare, by the processor, the historical data set for training;

define, by the processor, a target variable;

select, by the processor, a set of features; and

build, by the processor, a decision tree.

9 . The system of claim 6 , wherein when determining the set of related historical samples, the system is further configured to:

determine, by the processor, a subset of frequently-repeating training samples from the historical data set, on the leaf path of the unknown sample;

estimate, by the processor, a probability density function for the leaf path of the unknown sample;

generate, by the processor, a set of synthetic values from the probability density function;

compute, by the processor, the quantiles based on specified service levels and the set of synthetic values; and

output, by the processor, a quantile forecast for the unknown sample.

10 . The system of claim 6 , wherein inventory is loaded in accordance with the set of quantiles.

11 . A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to:

pre-process, by a processor, a historical data set the pre-processing executed once per trained machine-learning model;

forecast, by the processor, a value of an unknown sample while tracking a leaf path of the unknown sample;

limit, by the processor, the leaf path of the unknown sample to a subset of trees in each trained-machine model;

determine, by the processor, a set of related historical samples based on the leaf path of the unknown sample; and

determine, by the processor, a set of quantiles from the leaf path of the unknown sample.

12 . The computer-readable storage medium of claim 11 ,

wherein when pre-processing the historical data set, the instructions that when executed by the computer, further cause the computer to:

train, by the processor, a tree-based machine learning model;

make, by the processor, a forecast for the historical data set;

track, by the processor, one or more leaf paths associated with the forecast for the historical data set; and

create, by the processor, a group of similar samples in the historical data set at each branch of the tree-based machine learning model.

13 . The computer-readable storage medium of claim 12 ,

wherein when training the tree-based model, the instructions that when executed by the computer, further cause the computer to:

prepare, by the processor, the historical data set for training;

define, by the processor, a target variable;

select, by the processor, a set of features; and

build, by the processor, a decision tree.

14 . The computer-readable storage medium of claim 11 ,

wherein when determining the set of related historical samples, the instructions that when executed by the computer, further cause the computer to:

determine, by the processor, a subset of frequently-repeating training samples from the historical data set, on the leaf path of the unknown sample;

estimate, by the processor, a probability density function for the leaf path of the unknown sample;

generate, by the processor, a set of synthetic values from the probability density function;

compute, by the processor, the quantiles based on specified service levels and the set of synthetic values; and

output, by the processor, a quantile forecast for the unknown sample.

15 . The computer-readable storage medium of claim 11 , wherein inventory is loaded in accordance with the set of quantiles.

16 . A computer-implemented method, comprising:

pre-processing, by a processor, a historical data set associated with a plurality of stores and a plurality of products, the pre-processing executed once per trained machine-learning model;

forecasting, by the processor, a value of an unknown sample while tracking a leaf path of the unknown sample;

limiting, by the processor, the leaf path of the unknown sample to a subset of trees in each trained-machine model;

determining, by the processor, a set of related historical samples based on the leaf path of the unknown sample;

determining, by the processor, a set of quantiles across the plurality of stores from the leaf path of the unknown sample;

constructing, by the processor, one or more tradeoff curve functions for each combination of the plurality of products and stores from the set of quantiles;

constructing, by the processor, an objective function that accepts a quantity per store and computes a metric from the one or more tradeoff curves; and

determining, by the processor, a set of quantities across the plurality of stores that maximize the metric while being constrained by a maximum available quantity.

17 . The computer-implemented method of claim 16 , wherein pre-processing the historical data set comprises:

training, by the processor, a tree-based machine learning model;

making, by the processor, a forecast for the historical data set;

tracking, by the processor, one or more leaf paths associated with the forecast for the historical data set; and

creating, by the processor, a group of similar samples in the historical data set at each branch of the tree-based machine learning model.

18 . The computer-implemented method of claim 17 , wherein training the tree-based model comprises:

preparing, by the processor, the historical data set for training;

defining, by the processor, a target variable;

selecting, by the processor, a set of features; and

building, by the processor, a decision tree.

19 . The computer-implemented method of claim 16 , wherein determining the set of related historical samples comprises:

determining, by the processor, a subset of frequently-repeating training samples from the historical data set, on the leaf path of the unknown sample;

estimating, by the processor, a probability density function for the leaf path of the unknown sample;

generating, by the processor, a set of synthetic values from the probability density function;

computing, by the processor, the quantiles based on specified service levels and the set of synthetic values; and

outputting, by the processor, a quantile forecast for the unknown sample.

20 . The computer-implemented method of claim 16 , further comprising loading inventory at each of the plurality of stores in accordance with the set of quantities.

21 . A system comprising:

a processor; and

a memory storing instructions that, when executed by the processor, configure the system to:

pre-process, by the processor, a historical data set associated with a plurality of stores and a plurality of products, the pre-processing executed once per trained machine-learning model;

forecast, by the processor, a value of an unknown sample while tracking a leaf path of the unknown sample;

limit, by the processor, the leaf path of the unknown sample to a subset of trees in each trained-machine model;

determine, by the processor, a set of related historical samples based on the leaf path of the unknown sample;

determine, by the processor, a set of quantiles across the plurality of stores from the leaf path of the unknown sample;

construct, by the processor, one or more tradeoff curve functions for each combination of the plurality of products and stores from the set of quantiles;

construct, by the processor, an objective function that accepts a quantity per store and computes a metric from the one or more tradeoff curves; and

determine, by the processor, a set of quantities across the plurality of stores that maximize the metric while being constrained by a maximum available quantity.

22 . The system of claim 21 , wherein when pre-processing the historical data set, the system is further configured to:

train, by the processor, a tree-based machine learning model;

make, by the processor, a forecast for the historical data set;

track, by the processor, one or more leaf paths associated with the forecast for the historical data set; and

create, by the processor, a group of similar samples in the historical data set at each branch of the tree-based machine learning model.

23 . The system of claim 22 , wherein when training the tree-based model, the system is further configured to:

prepare, by the processor, the historical data set for training;

define, by the processor, a target variable;

select, by the processor, a set of features; and

build, by the processor, a decision tree.

24 . The system of claim 21 , wherein when determining the set of related historical samples, the system is further configured to:

determine, by the processor, a subset of frequently-repeating training samples from the historical data set, on the leaf path of the unknown sample;

estimate, by the processor, a probability density function for the leaf path of the unknown sample;

generate, by the processor, a set of synthetic values from the probability density function;

compute, by the processor, the quantiles based on specified service levels and the set of synthetic values; and

output, by the processor, a quantile forecast for the unknown sample.

25 . The system of claim 21 , wherein inventory is loaded at each of the plurality of stores in accordance with the set of quantities.

26 . A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to:

pre-process, by a processor, a historical data set associated with a plurality of stores and a plurality of products, the pre-processing executed once per trained machine-learning model;

forecast, by the processor, a value of an unknown sample while tracking a leaf path of the unknown sample;

limit, by the processor, the leaf path of the unknown sample to a subset of trees in each trained-machine model;

determine, by the processor, a set of related historical samples based on the leaf path of the unknown sample;

determine, by the processor, a set of quantiles across the plurality of stores from the leaf path of the unknown sample;

construct, by the processor, one or more tradeoff curve functions for each combination of the plurality of products and stores from the set of quantiles;

construct, by the processor, an objective function that accepts a quantity per store and computes a metric from the one or more tradeoff curves; and

determine, by the processor, a set of quantities across the plurality of stores that maximize the metric while being constrained by a maximum available quantity.

27 . The computer-readable storage medium of claim 26 ,

wherein when pre-processing the historical data set, the instructions that when executed by the computer, further cause the computer to:

train, by the processor, a tree-based machine learning model;

make, by the processor, a forecast for the historical data set;

track, by the processor, one or more leaf paths associated with the forecast for the historical data set; and

create, by the processor, a group of similar samples in the historical data set at each branch of the tree-based machine learning model.

28 . The computer-readable storage medium of claim 27 , wherein when training the tree-based model, the instructions that when executed by the computer, further cause the computer to:

prepare, by the processor, the historical data set for training;

define, by the processor, a target variable;

select, by the processor, a set of features; and

build, by the processor, a decision tree.

29 . The computer-readable storage medium of claim 26 , wherein when determining the set of related historical samples, the instructions that when executed by the computer, further cause the computer to:

determine, by the processor, a subset of frequently-repeating training samples from the historical data set, on the leaf path of the unknown sample;

estimate, by the processor, a probability density function for the leaf path of the unknown sample;

generate, by the processor, a set of synthetic values from the probability density function;

compute, by the processor, the quantiles based on specified service levels and the set of synthetic values; and

output, by the processor, a quantile forecast for the unknown sample.

30 . The computer-readable storage medium of claim 26 , wherein inventory is loaded at each of the plurality of stores in accordance with the set of quantities.

Assignments (1)

Continuity (3)

References Cited (13)