# US 2026/0065352 A1 · Generating a region- and source-agnostic database of items available in multiple regions

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- Number: US 2026/0065352 A1
- Document ID: US20260065352A1
- Status: Published application
- Kind: A1
- Application number: 18/821,939
- Filed: 2024-08-30
- Published: 2026-03-05
- Earliest claimed: 2024-08-30
- Assignees: Maplebear Inc.
- Applicants: Maplebear Inc.
- Inventors: Naval Shah; Riddhima Sejpal
- Web page: https://patentgrep.com/patent/US20260065352A1

## CPC

- G06Q 30/0639: {Locating goods or services, e.g. based on physical position of the goods or services within a shopping facility}
- G06Q 10/087: Inventory or stock management, e.g. order filling, procurement or balancing against orders
- G06Q 30/0201: Market modelling; Market analysis; Collecting market data
- G06Q 30/0603: {Catalogue creation or management}
- G06Q 30/0631: {Recommending goods or services}
- 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

An online system retrieves item data for items available at sources in multiple regions and generates candidate nodes based on the item data, in which each candidate node represents items having at least a threshold measure of similarity to each other. The system accesses and applies a machine-learning model to predict a matching score for each combination of an item and a candidate node based on item data for the item and attributes of items represented by the candidate node. The system assigns the items to candidate nodes based on the matching scores, retrieves information describing an availability of each item in each geographical region, and identifies an average availability of items assigned to each candidate node across the geographical regions. The system selects nodes to include in a region- and source-agnostic item database, in which the average availability associated with each selected node is at least a threshold.

## Claims

### Claim 1

A method, performed at a computer system comprising a processor and a computer-readable medium, comprising:
retrieving, at an online system, a set of item data for each item of a plurality of items available at a plurality of source locations, wherein each source location of the plurality of source locations is located in a different region of a plurality of regions;
generating a plurality of candidate nodes based at least in part on the set of item data for each item of the plurality of items, wherein each candidate node of the plurality of candidate nodes represents a group of items having at least a threshold measure of similarity to each other;
accessing a machine-learning model trained to predict a matching score for a combination of an item and a candidate node;
for each combination of an item of the plurality of items and a candidate node of the plurality of candidate nodes, applying the machine-learning model to predict the matching score for a corresponding combination based at least in part on the set of item data for a corresponding item and a set of attributes of the group of items represented by a corresponding candidate node;
assigning each item of the plurality of items to a candidate node of the plurality of candidate nodes based at least in part on the matching score predicted for each combination;
retrieving availability information describing an availability of each item of the plurality of items in each region of the plurality of regions;
identifying an average availability of one or more items assigned to each candidate node of the plurality of candidate nodes across the plurality of regions based at least in part on the availability information describing the availability of each item of the plurality of items in each region of the plurality of regions;
for each candidate node of the plurality of candidate nodes, identifying whether the average availability of the one or more items assigned to a corresponding candidate node across the plurality of regions is at least a threshold availability;
selecting, from the plurality of candidate nodes, a set of nodes to include in a region- and source-agnostic item database, wherein the average availability of the one or more items assigned to each selected node is at least the threshold availability; and
storing information describing the selected set of nodes included in the region- and source-agnostic item database.

### Claim 2 (depends on 1)

The method of claim 1, wherein generating the plurality of candidate nodes based at least in part on the set of item data for each item of the plurality of items comprises:
generating an item embedding for each item of a set of items available at the plurality of source locations based at least in part on the set of item data for each item of the set of items;
grouping a set of item embeddings generated for the set of items into a plurality of clusters using a clustering algorithm; and
generating the plurality of candidate nodes based at least in part on the plurality of clusters.

### Claim 3 (depends on 1)

The method of claim 1, wherein generating the plurality of candidate nodes based at least in part on the set of item data for each item of the plurality of items comprises:
generating the plurality of candidate nodes based on a level of a taxonomy associated with each item of the plurality of items.

### Claim 4 (depends on 1)

The method of claim 1, wherein identifying the average availability of the one or more items assigned to each candidate node of the plurality of candidate nodes across the plurality of regions based at least in part on the availability information describing the availability of each item of the plurality of items in each region of the plurality of regions comprises:
weighting the average availability based at least in part on a number of orders associated with each region of the plurality of regions.

### Claim 5 (depends on 1)

The method of claim 1, further comprising:
adjusting the threshold availability for each candidate node of the plurality of candidate nodes based at least in part on information describing whether one or more of a plurality of pickers or a plurality of users were able to find one or more items assigned to a corresponding candidate node.

### Claim 6 (depends on 1)

The method of claim 1, wherein assigning each item of the plurality of items to a candidate node of the plurality of candidate nodes based at least in part on the matching score predicted for each combination comprises:
assigning each item of the plurality of items to a candidate node of the plurality of candidate nodes based on a threshold matching score.

### Claim 7 (depends on 6)

The method of claim 6, wherein assigning each item of the plurality of items to a candidate node of the plurality of candidate nodes based at least on the threshold matching score comprises:
assigning each item of the plurality of items to a candidate node of the plurality of candidate nodes if the matching score predicted for a corresponding combination of an item and a candidate node is at least the threshold matching score.

### Claim 8 (depends on 6)

The method of claim 6, further comprising:
adjusting the threshold matching score based at least in part on a measure of satisfaction of one or more users with one or more replacements for one or more items assigned to a candidate node.

### Claim 9 (depends on 1)

The method of claim 1, further comprising:
receiving, from a client device associated with a user of the online system, a request to access a user interface comprising information describing a set of items assigned to one or more nodes included in the region- and source-agnostic item database;
retrieving the set of item data for each item of the set of items;
generating the user interface comprising information describing the set of items; and
sending the user interface to the client device associated with the user, causing the client device to display the user interface.

### Claim 10 (depends on 1)

The method of claim 1, further comprising:
training the machine-learning model by:
receiving the set of attributes of the group of items represented by each candidate node of the plurality of candidate nodes,
receiving a set of additional attributes of a set of items,
receiving, for each item of the set of items and each candidate node of the plurality of candidate nodes, a label indicating a measure of appropriateness of matching a corresponding item with a corresponding candidate node, and
updating a set of parameters of the machine-learning model based at least in part on the set of attributes, the set of additional attributes, and the label for each item of the set of items and each candidate node of the plurality of candidate nodes.

### Claim 11

A computer program product comprising a non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps comprising:
retrieving, at an online system, a set of item data for each item of a plurality of items available at a plurality of source locations, wherein each source location of the plurality of source locations is located in a different region of a plurality of regions;
generating a plurality of candidate nodes based at least in part on the set of item data for each item of the plurality of items, wherein each candidate node of the plurality of candidate nodes represents a group of items having at least a threshold measure of similarity to each other;
accessing a machine-learning model trained to predict a matching score for a combination of an item and a candidate node;
for each combination of an item of the plurality of items and a candidate node of the plurality of candidate nodes, applying the machine-learning model to predict the matching score for a corresponding combination based at least in part on the set of item data for a corresponding item and a set of attributes of the group of items represented by a corresponding candidate node;
assigning each item of the plurality of items to a candidate node of the plurality of candidate nodes based at least in part on the matching score predicted for each combination;
retrieving availability information describing an availability of each item of the plurality of items in each region of the plurality of regions;
identifying an average availability of one or more items assigned to each candidate node of the plurality of candidate nodes across the plurality of regions based at least in part on the availability information describing the availability of each item of the plurality of items in each region of the plurality of regions;
for each candidate node of the plurality of candidate nodes, identifying whether the average availability of the one or more items assigned to a corresponding candidate node across the plurality of regions is at least a threshold availability;
selecting, from the plurality of candidate nodes, a set of nodes to include in a region- and source-agnostic item database, wherein the average availability of the one or more items assigned to each selected node is at least the threshold availability; and
storing information describing the selected set of nodes included in the region- and source-agnostic item database.

### Claim 12 (depends on 11)

The computer program product of claim 11, wherein generating the plurality of candidate nodes based at least in part on the set of item data for each item of the plurality of items comprises:
generating an item embedding for each item of a set of items available at the plurality of source locations based at least in part on the set of item data for each item of the set of items;
grouping a set of item embeddings generated for the set of items into a plurality of clusters using a clustering algorithm; and
generating the plurality of candidate nodes based at least in part on the plurality of clusters.

### Claim 13 (depends on 11)

The computer program product of claim 11, wherein generating the plurality of candidate nodes based at least in part on the set of item data for each item of the plurality of items comprises:
generating the plurality of candidate nodes based on a level of a taxonomy associated with each item of the plurality of items.

### Claim 14 (depends on 11)

The computer program product of claim 11, wherein identifying the average availability of the one or more items assigned to each candidate node of the plurality of candidate nodes across the plurality of regions based at least in part on the availability information describing the availability of each item of the plurality of items in each region of the plurality of regions comprises:
weighting the average availability based at least in part on a number of orders associated with each region of the plurality of regions.

### Claim 15 (depends on 11)

The computer program product of claim 11, wherein the computer-readable storage medium further has instructions encoded thereon that, when executed by the processor, cause the processor to perform steps comprising:
adjusting the threshold availability for each candidate node of the plurality of candidate nodes based at least in part on information describing whether one or more of a plurality of pickers or a plurality of users were able to find one or more items assigned to a corresponding candidate node.

### Claim 16 (depends on 11)

The computer program product of claim 11, wherein assigning each item of the plurality of items to a candidate node of the plurality of candidate nodes based at least in part on the matching score predicted for each combination comprises:
assigning each item of the plurality of items to a candidate node of the plurality of candidate nodes based on a threshold matching score.

### Claim 17 (depends on 16)

The computer program product of claim 16, wherein assigning each item of the plurality of items to a candidate node of the plurality of candidate nodes based at least on the threshold matching score comprises:
assigning each item of the plurality of items to a candidate node of the plurality of candidate nodes if the matching score predicted for a corresponding combination of an item and a candidate node is at least the threshold matching score.

### Claim 18 (depends on 16)

The computer program product of claim 16, wherein the computer-readable storage medium further has instructions encoded thereon that, when executed by the processor, cause the processor to perform steps comprising:
adjusting the threshold matching score based at least in part on a measure of satisfaction of one or more users with one or more replacements for one or more items assigned to a candidate node.

### Claim 19 (depends on 11)

The computer program product of claim 11, wherein the computer-readable storage medium further has instructions encoded thereon that, when executed by the processor, cause the processor to perform steps comprising:
receiving, from a client device associated with a user of the online system, a request to access a user interface comprising information describing a set of items assigned to one or more nodes included in the region- and source-agnostic item database;
retrieving the set of item data for each item of the set of items;
generating the user interface comprising information describing the set of items; and
sending the user interface to the client device associated with the user, causing the client device to display the user interface.

### Claim 20

A computer system comprising:
a processor; and
a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, perform actions comprising:
retrieving, at an online system, a set of item data for each item of a plurality of items available at a plurality of source locations, wherein each source location of the plurality of source locations is located in a different region of a plurality of regions;
generating a plurality of candidate nodes based at least in part on the set of item data for each item of the plurality of items, wherein each candidate node of the plurality of candidate nodes represents a group of items having at least a threshold measure of similarity to each other;
accessing a machine-learning model trained to predict a matching score for a combination of an item and a candidate node;
for each combination of an item of the plurality of items and a candidate node of the plurality of candidate nodes, applying the machine-learning model to predict the matching score for a corresponding combination based at least in part on the set of item data for a corresponding item and a set of attributes of the group of items represented by a corresponding candidate node;
assigning each item of the plurality of items to a candidate node of the plurality of candidate nodes based at least in part on the matching score predicted for each combination;
retrieving availability information describing an availability of each item of the plurality of items in each region of the plurality of regions;
identifying an average availability of one or more items assigned to each candidate node of the plurality of candidate nodes across the plurality of regions based at least in part on the availability information describing the availability of each item of the plurality of items in each region of the plurality of regions;
for each candidate node of the plurality of candidate nodes, identifying whether the average availability of the one or more items assigned to a corresponding candidate node across the plurality of regions is at least a threshold availability;
selecting, from the plurality of candidate nodes, a set of nodes to include in a region- and source-agnostic item database, wherein the average availability of the one or more items assigned to each selected node is at least the threshold availability; and
storing information describing the selected set of nodes included in the region- and source-agnostic item database.