# US 2021/0241353 A1 · Automatic item grouping and personalized department layout for reorder recommendations

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2021/0241353 A1
- Document ID: US20210241353A1
- Status: Published application
- Kind: A1
- Application number: 17/163,388
- Filed: 2021-01-30
- Published: 2021-08-05
- Earliest claimed: 2020-01-31
- Assignees: Walmart Apollo, LLC
- Applicants: Walmart Apollo, LLC
- Inventors: Rahul Sridhar; Sinduja Subramaniam; Evren Korpeoglu; Kannan Achan
- Web page: https://patentgrep.com/patent/US20210241353A1

## CPC

- G06Q 30/0631: {Recommending goods or services}
- G06Q 30/0633: {Managing shopping lists, e.g. compiling or processing purchase lists (shipping orders G06Q10/083; order filling G06Q10/087)}
- 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

## Abstract

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform certain acts. The acts can include obtaining reorder likelihood scores for items that a user has ordered historically. The acts also can include grouping the items into groups using a taxonomy. The acts additionally can include adjusting the groups based on a respective number of items in each of the groups and a respective group-specific threshold for each of the groups. The acts further can include ranking the items within the groups based on the reorder likelihood scores. Other embodiments are described.

## Claims

### Claim 1

A system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform:
obtaining reorder likelihood scores for items that a user has ordered historically;
grouping the items into groups using a taxonomy;
adjusting the groups based on a respective number of items in each of the groups and a respective group-specific threshold for each of the groups; and
ranking the items within the groups based on the reorder likelihood scores.

### Claim 2 (depends on 1)

The system of claim 1, wherein adjusting the groups further comprises:
splitting one or more of the groups based on the respective number of items in each of the groups and the respective group-specific threshold for each of the groups.

### Claim 3 (depends on 1)

The system of claim 1, wherein adjusting the groups further comprises:
merging two or more of the groups based on the respective number of items in each of the groups and the respective group-specific threshold for each of the groups.

### Claim 4 (depends on 1)

The system of claim 1, wherein the items that the user has ordered historically that are included in the groups have reorder likelihood scores above a predetermined threshold.

### Claim 5 (depends on 1)

The system of claim 1, wherein the computing instructions, when executed on the one or more processors, further perform:
determining, using an ensemble model, a respective number of items that the user is likely to reorder in each of the groups.

### Claim 6 (depends on 5)

The system of claim 5, wherein:
the ensemble model comprises a random forests model of decision trees; and
predictions generated by the decision trees are averaged for each of the groups.

### Claim 7 (depends on 5)

The system of claim 5, wherein the ensemble model is based on features comprising one or more of a number of orders, a recency of orders, a monetary value at the user level, a monetary value at the department level, reorder rates, similar item orders, or basket sizes.

### Claim 8 (depends on 5)

The system of claim 5, wherein the computing instructions, when executed on the one or more processors, further perform:
ranking the groups based on the respective number of items the user is likely to reorder in each of the groups.

### Claim 9 (depends on 8)

The system of claim 8, wherein the computing instructions, when executed on the one or more processors, further perform:
transmitting for display to the user a user interface comprising the groups of the items, wherein:
the groups are ordered in the user interface based on the ranking of the groups;
the items are ordered within the groups based on the ranking of the items within the groups; and
a respective number of items displayed within each of the groups is limited based on the respective number of items that the user is likely to reorder in each of the groups.

### Claim 10 (depends on 9)

The system of claim 9, wherein the user interface further comprises alternatives for out-of-stock items.

### Claim 11

A method implemented via execution of computing instructions configured to run at one or more processors, the method comprising:
obtaining reorder likelihood scores for items that a user has ordered historically;
grouping the items into groups using a taxonomy;
adjusting the groups based on a respective number of items in each of the groups and a respective group-specific threshold for each of the groups; and
ranking the items within the groups based on the reorder likelihood scores.

### Claim 12 (depends on 11)

The method of claim 11, wherein adjusting the groups further comprises:
splitting one or more of the groups based on the respective number of items in each of the groups and the respective group-specific threshold for each of the groups.

### Claim 13 (depends on 11)

The method of claim 11, wherein adjusting the groups further comprises:
merging two or more of the groups based on the respective number of items in each of the groups and the respective group-specific threshold for each of the groups.

### Claim 14 (depends on 11)

The method of claim 11, wherein the items that the user has ordered historically that are included in the groups have reorder likelihood scores above a predetermined threshold.

### Claim 15 (depends on 11)

The method of claim 11 further comprising:
determining, using an ensemble model, a respective number of items that the user is likely to reorder in each of the groups.

### Claim 16 (depends on 15)

The method of claim 15, wherein:
the ensemble model comprises a random forests model of decision trees; and
predictions generated by the decision trees are averaged for each of the groups.

### Claim 17 (depends on 15)

The method of claim 15, wherein the ensemble model is based on features comprising one or more of a number of orders, a recency of orders, a monetary value at the user level, a monetary value at the department level, reorder rates, similar item orders, or basket sizes.

### Claim 18 (depends on 15)

The method of claim 15 further comprising:
ranking the groups based on the respective number of items the user is likely to reorder in each of the groups.

### Claim 19 (depends on 18)

The method of claim 18 further comprising:
transmitting for display to the user a user interface comprising the groups of the items,
wherein:
the groups are ordered in the user interface based on the ranking of the groups;
the items are ordered within the groups based on the ranking of the items within the groups; and
a respective number of items displayed within each of the groups is limited based on the respective number of items that the user is likely to reorder in each of the groups.

### Claim 20 (depends on 19)

The method of claim 19, wherein the user interface further comprises alternatives for out-of-stock items.