Patent ID: 11875304
Assignee: WALMART APOLLO, LLC
Field: IT methods for management (Electrical engineering)
Classification: CPC G | IPC G

Claim 6:
7. A computer-implemented method comprising:
receiving, by a processor, first supplier data including a first supplier identifier that identifies a first item in a first supplier's numbering system;
storing, by the processor, the first supplier data in a database, wherein the database includes a hierarchical structure defined by a plurality of supplier-group associations, a plurality of supplier-item associations, and a plurality of group-item associations;
determining, by the processor, a first retailer identifier associated with the first supplier identifier, wherein the first retailer identifier identifies the first item in a first retail numbering system, and wherein the first retail identifier corresponds to first item data stored within the database;
generating, by the processor, a first supplier-item association defining a hierarchical relationship between the first supplier data and the first item data in the database;
determining, by the processor, a first group identifier associated with the first supplier identifier, wherein the first group identifier identifies a plurality of items in a second retail numbering system, wherein the first group identifier corresponds to first group data stored within the database, wherein each item of the plurality of items are assigned to the first group identifier, and wherein the plurality of items includes the first item;
generating, by the processor, a first supplier-group association defining a hierarchical relationship between the first supplier data and first group data in the database;
determining, by the processor, a second retailer identifier associated with the first group identifier based on one or more group-item associations defining a hierarchical relationship between at least the first group identifier and an associated set of retailer identifiers, wherein the second retailer identifier corresponds to second item data within the database;
training, by the processor, a machine learning model to detect anomalies in the hierarchical structure of the database, wherein the machine learning model is trained by a supervised training dataset based on attributes of items in the plurality of items, wherein the machine learning model is configured to generating an anomaly score between the first item data and the second item data, and wherein the machine learning model is configured to generate the anomaly score by:
generating a first word embedding representative of at least one attribute of the first item data;
generating a second word embedding representative of the at least one attribute of the second item data; and
comparing the first word embedding and the second word embedding to generate the anomaly score;

generate, by the processor, a second supplier-item association defining a hierarchical relationship between first supplier data and the second item data when the second retailer identifier matches the first retailer identifier, and when the second retailer identifier does not match the first retailer identifier and the anomaly score is below a predetermined threshold;
in response to determining the anomaly score is equal to or above the predetermined threshold, transmitting, by the processor, an electronic communication identifying the first item data and the second item data; and
training, by the processor, an additional machine learning model to detect anomalies in the hierarchical structure of the database, wherein the additional machine learning model is trained by a supervised training dataset based on attributes of items in the plurality of items and at least the second supplier-item association.