Methods and apparatus for grouping items

Patent metadata

Field	Value
Publication number	US-2021406812-A1
Application number	US-202016914962-A
Country	US
Kind code	A1
Filing date	Jun 29, 2020
Priority date	Jun 29, 2020
Publication date	Dec 30, 2021
Grant date	—

Abstract

This application relates to apparatus and methods for automatically grouping the same or similar items provided by various suppliers that may use various supplier identification systems to identify the items to a retailer. In some examples, a computing device receives a supplier identifier from a supplier. The supplier identifier identifies an item that the supplier provides to the retailer for sale. The computing device may determine a group identifier for the item, where the group identifier identifies the item within a group of the retailer. The computing device may update the supplier identifier's association to the retailer identifier based on whether the group identifier is also associated with the retailer identifier. In some examples, the computing device executes a machine learning model to determine anomalies within a current database of supplier identifiers and associated retailer identifiers. In some examples, the computing device indicates changes to associations and any detected anomalies.

First claim

1 . A system comprising:
a memory having instructions stored thereon, a processor configured to read the memory, wherein the processor is coupled to a database, the processor configured to read the instructions to:
receive a first supplier identifier that identifies a first item in a first supplier's numbering system;
determine a first retailer identifier associated with the first supplier identifier, wherein the first retailer identifier identifies the first item in a retailer's first numbering system, the retailer's first numbering system is stored within the database;
determine a first group identifier associated with the first supplier identifier, wherein the first group identifier identifies a plurality of items in a retailer's second numbering system within the database, each item of the plurality of items are assigned to the first group identifier, and the plurality of items includes the first item;
determine a second retailer identifier associated with the first group identifier based on group identification association data identifying a set of group identifiers and their associated set of retailer identifiers, each retailer identifier of the set of retailer identifiers corresponding to a retail item within the database; and
determine whether to associate the first supplier identifier with the second retailer identifier in the retailer's first numbering system based on whether the second retailer identifier matches the first retailer identifier.

2 . The system of claim 1 , wherein determining whether to associate the first supplier identifier with the second retailer identifier comprises:
determining that the second retailer identifier does not match the first retailer identifier; disassociating the first supplier identifier from the first retailer identifier in the retailer's first numbering system; and associating the first supplier identifier to the second retailer identifier in the retailer's first numbering system.

3 . The system of claim 1 , wherein the computing device is configured to:
determine that the first group identifier does not have a matching entry in the retailer's first numbering system; generate the matching entry in the retailer's first numbering system; and associate the first supplier identifier with the generated matching entry in the retailer's first numbering system.

4 . The system of claim 1 , wherein determining whether to associate the first supplier identifier with the second retailer identifier in the retailer's first numbering system comprises:
determine at least a first attribute for the first item; determine a second supplier identifier associated to the second retailer identifier; determine at least a second attribute of a second item corresponding to the second supplier identifier; determine at least one anomaly to associating the first supplier identifier with the second retailer identifier based on the at least first attribute and the at least second attribute; and determining not to associate the first supplier identifier with the second retailer identifier based on the at least one anomaly.

5 . The system of claim 4 , wherein determining the at least one anomaly comprises executing a machine learning model based on the at least first attribute and the at least second attribute.

6 . The system of claim 5 , wherein:
the retailer's first numbering system associates a plurality of supplier identifiers to a first plurality of group identifiers and a plurality of retailer identifiers; and the retailer's second numbering system associates a second plurality of group identifiers and at least a portion of the plurality of supplier identifiers.

7 . The system of claim 1 , wherein determining whether to associate the first supplier identifier with the second retailer identifier comprises:
determining that the second retailer identifier matches the first retailer identifier; determining at least a first attribute for the first item; determining a second supplier identifier associated to the first retailer identifier and a third supplier identifier associated to the second retailer identifier; determining at least a second attribute of a second item corresponding to the second supplier identifier and a third attribute of a third item corresponding to the third supplier identifier; determine a first number of anomalies when associating the first supplier identifier with the first retailer identifier based on the at least first attribute and the at least second attribute; determine a second number of anomalies when associating the first supplier identifier with the second retailer identifier based on the at least first attribute and the at least third attribute; and determine whether to associate the first supplier identifier with the second retailer identifier based on the first number of anomalies and the second number of anomalies.

8 . The system of claim 7 , wherein:
determining the first number of anomalies comprises executing a machine learning model based on the at least first attribute and the at least second attribute; and determining the second number of anomalies comprises executing the machine learning model based on the at least first attribute and the at least third attribute.

9 . A method comprising:
receiving a first supplier identifier that identifies a first item in a first supplier's numbering system; determining a first retailer identifier associated with the first supplier identifier, wherein the first retailer identifier identifies the first item in a retailer's first numbering system, the retailer's first numbering system is stored within the database; determining a first group identifier associated with the first supplier identifier, wherein the first group identifier identifies a plurality of items in a retailer's second numbering system within the database, each item of the plurality of items are assigned to the first group identifier, and the plurality of items includes the first item; determining a second retailer identifier associated with the first group identifier based on group identification association data identifying a set of group identifiers and their associated set of retailer identifiers, each retailer identifier of the set of retailer identifiers corresponding to a retail item within the database; and determining whether to associate the first supplier identifier with the second retailer identifier in the retailer's first numbering system based on whether the second retailer identifier matches the first retailer identifier.

10 . The method of claim 9 wherein determining whether to associate the first supplier identifier with the second retailer identifier comprises:
determining that the second retailer identifier does not match the first retailer identifier;
disassociating the first supplier identifier from the first retailer identifier in the retailer's first numbering system; and
associating the first supplier identifier to the second retailer identifier in the retailer's first numbering system.

11 . The method of claim 9 further comprising:
determining that the first group identifier does not have a matching entry in the retailer's first numbering system;
generating the matching entry in the retailer's first numbering system; and
associating the first supplier identifier with the generated matching entry in the retailer's first numbering system.

12 . The method of claim 9 , wherein determining whether to associate the first supplier identifier with the second retailer identifier in the retailer's first numbering system comprises:
determining at least a first attribute for the first item; determining a second supplier identifier asso

Assignees

• Walmart Apollo Llc

Inventors

• Deshmukh Swati Ajit
• AKMAL Mohammad Zohaib
• Swami Abhinav
• Vutukuri Revanth

Classifications

• G06N20/00

  Machine learning · CPC title

• G06Q10/087 Primary

  Inventory or stock management, e.g. order filling, procurement or balancing against orders · CPC title

• G06Q10/067

  Enterprise or organisation modelling · CPC title

• G06Q10/06315

  Needs-based resource requirements planning or analysis · CPC title