# US 2021/0334682 A1 · Machine learning systems for managing inventory

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- Number: US 2021/0334682 A1
- Document ID: US20210334682A1
- Status: Published application
- Kind: A1
- Application number: 17/218,915
- Filed: 2021-03-31
- Published: 2021-10-28
- Earliest claimed: 2020-04-23
- Assignees: Oracle International Corporation
- Applicants: Oracle International Corporation
- Inventors: Jennifer Darmour; Loretta Marie Grande; Ronald Paul Lapurga Viernes; Jingyi Han; Nicole Santina Giovanetti; Jason Wong; Min Hye Kim
- Web page: https://patentgrep.com/patent/US20210334682A1

## CPC

- G06N 5/04: Inference or reasoning models
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- G06N 20/00: Machine learning
- G06Q 10/063116: {Schedule adjustment for a person or group}
- G06Q 10/06316: {Sequencing of tasks or work}
- G06Q 10/08: Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06F 40/20: Natural language analysis (semantic analysis of natural language G06F40/30)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

Techniques are disclosed for training a machine learning model to select a route for performing tasks in a target set of inventory tasks. The machine learning model may be trained by obtaining training data sets that include characteristics of previously performed tasks by one or more task performers. Example characteristics may include locations associated with the previously performed tasks, a duration of time taken to perform the previous tasks, a route taken to perform the tasks, a sequence in which tasks a set of tasks were performed, and attributes of the task performers themselves. The machine learning model may be trained using these training data sets and the applied to a received set of target tasks. The trained machine learning model may then generate a route and/or sequence in which the tasks of the target set of tasks may be performed.

## Claims

### Claim 1

One or more non-transitory computer-readable media storing instructions, which when executed by one or more hardware processors, cause performance of operations comprising:
training a machine learning model to select a route for performing a target set of tasks at least by:
obtaining training data sets, each training data set comprising:
characteristics of a set of previous tasks performed by one or more task performers, the set of characteristics comprising one or more of:
a location associated with a particular previous task of the set of previous tasks;
a time duration for performing the particular previous task;
a time at which the particular previous task was performed;
a route taken to perform the particular previous task;
a sequence in which the tasks of the set of previous tasks were performed;
an attribute of the task performer that performed the particular previous task;
training the machine learning model based on the training data sets;
receiving a target set of tasks to be performed; and
applying the trained machine learning model to the target set of tasks to generate the route for performing the target set of tasks.

### Claim 2 (depends on 1)

The media of claim 1, wherein training the machine learning model comprises determining that none of the set of previous tasks included a route through a particular location at a first time of day, and wherein the route selected by the machine learning model for performing the target set of task avoids the particular location at the first time of day.

### Claim 3 (depends on 1)

The media of claim 1, wherein:
training the machine learning model comprises determining that the particular previous task of the set of previous tasks was performed first in the sequence in which the set of previous tasks were performed;
the training further comprises assigning a high priority to completing the particular task; and
wherein the high priority is assigned to a task in the target set of tasks similar to the particular previous task of the set of previous tasks.

### Claim 4 (depends on 3)

The media of claim 3, further comprising inferring a set of priorities for the target set of tasks based on the sequence in which the tasks of the set of previous tasks were performed, each priority of the set of priorities corresponding to a task in the target set of tasks.

### Claim 5 (depends on 1)

The media of claim 1, wherein:
the attributes of the task performer the performed the particular previous task comprises a plurality of attributes that include one or more of: a work schedule and a set of permissions; and
the applying operation further comprises selecting a subset of tasks in the target set of tasks to be performed by a target task performer based on the working schedule and the set of permissions of the target task performer.

### Claim 6 (depends on 1)

The media of claim 1, wherein the applying operation comprises generating a set of target times at which to complete corresponding tasks of the target set of tasks.

### Claim 7 (depends on 1)

The media of claim 1, further comprising:
receiving an additional target task added to the target set of tasks after the route for performing the target set of tasks has been selected; and
modifying the selected route by re-applying the trained machine learning model to the target set of tasks that includes the additional target task.

### Claim 8 (depends on 7)

The media of claim 7, wherein the modifying operation comprises generating a revised sequence of target tasks that include the additional target task.

### Claim 9 (depends on 7)

The media of claim 7, wherein the modifying operation comprises generating a revised sequence of target tasks that excludes completed target tasks of the set of target tasks.

### Claim 10 (depends on 7)

The media of claim 7, wherein the modifying operation comprises identifying one or both of:
one or more target tasks of the set of target tasks to be delayed in response to including the additional target task; or
one or more target tasks of the set of target tasks that is required to be completed according to the previously selected route despite including the additional target task.

### Claim 11

A method comprising:
training a machine learning model to select a route for performing a target set of tasks at least by:
obtaining training data sets, each training data set comprising:
characteristics of a set of previous tasks performed by one or more task performers, the set of characteristics comprising one or more of:
a location associated with a particular previous task of the set of previous tasks;
a time duration for performing the particular previous task;
a time at which the particular previous task was performed;
a route taken to perform the particular previous task;
a sequence in which the tasks of the set of previous tasks were performed;
an attribute of the task performer that performed the particular previous task;
training the machine learning model based on the training data sets;
receiving a target set of tasks to be performed; and
applying the trained machine learning model to the target set of tasks to generate the route for performing the target set of tasks.

### Claim 12 (depends on 11)

The method of claim 11, wherein training the machine learning model comprises determining that none of the set of previous tasks included a route through a particular location at a first time of day, and wherein the route selected by the machine learning model for performing the target set of task avoids the particular location at the first time of day.

### Claim 13 (depends on 11)

The method of claim 11, wherein:
training the machine learning model comprises determining that the particular previous task of the set of previous tasks was performed first in the sequence in which the set of previous tasks were performed;
the training further comprises assigning a high priority to completing the particular task; and
wherein the high priority is assigned to a task in the target set of tasks similar to the particular previous task of the set of previous tasks.

### Claim 14 (depends on 13)

The method of claim 13, further comprising inferring a set of priorities for the target set of tasks based on the sequence in which the tasks of the set of previous tasks were performed, each priority of the set of priorities corresponding to a task in the target set of tasks.

### Claim 15 (depends on 11)

The method of claim 11, wherein:
the attributes of the task performer the performed the particular previous task comprises a plurality of attributes that include one or more of: a work schedule and a set of permissions; and
the applying operation further comprises selecting a subset of tasks in the target set of tasks to be performed by a target task performer based on the working schedule and the set of permissions of the target task performer.

### Claim 16 (depends on 11)

The method of claim 11, wherein the applying operation comprises generating a set of target times at which to complete corresponding tasks of the target set of tasks.

### Claim 17 (depends on 11)

The method of claim 11, further comprising:
receiving an additional target task added to the target set of tasks after the route for performing the target set of tasks has been selected; and
modifying the selected route by re-applying the trained machine learning model to the target set of tasks that includes the additional target task.

### Claim 18 (depends on 17)

The method of claim 17, wherein the modifying operation comprises generating a revised sequence of target tasks that include the additional target task.

### Claim 19 (depends on 17)

The method of claim 17, wherein the modifying operation comprises generating a revised sequence of target tasks that excludes completed target tasks of the set of target tasks.

### Claim 20 (depends on 17)

The method of claim 17, wherein the modifying operation comprises identifying one or both of:
one or more target tasks of the set of target tasks to be delayed in response to including the additional target task; or
one or more target tasks of the set of target tasks that is required to be completed according to the previously selected route despite including the additional target task.