Inferring categories in a product taxonomy using a replacement model

Patent metadata

Field	Value
Publication number	US-2022292567-A1
Application number	US-202117196855-A
Country	US
Kind code	A1
Filing date	Mar 9, 2021
Priority date	Mar 9, 2021
Publication date	Sep 15, 2022
Grant date	—

Abstract

An online concierge system accesses a hierarchical taxonomy of products each labeled with a category of the hierarchical taxonomy. The online concierge system receives, from an inventory database, an unlabeled product, which not included in the hierarchical taxonomy. The online concierge system inputs the unlabeled product to a replacement model. The replacement model is trained to output, for each of one or more labeled products from the hierarchical taxonomy, a likelihood that a user would select the labeled product as a replacement for an input product. The online concierge system selects a labeled product from the one or more labeled products based on the likelihoods. The online concierge system adds the unlabeled product to a category of the hierarchical taxonomy based on the selected labeled product.

First claim

What is claimed is:

1 . A computer-implemented method comprising:
accessing a hierarchical taxonomy of products in an online concierge system, each product labeled with a category of the hierarchical taxonomy; receiving, from an inventory database, an unlabeled product, the unlabeled product not included in the hierarchical taxonomy; inputting the unlabeled product to a replacement model, wherein the replacement model is trained to output, for each of one or more labeled products from the hierarchical taxonomy, a likelihood that a user would select the labeled product as a replacement for an input product; selecting a labeled product from the one or more labeled products based on the likelihoods; and adding the unlabeled product to a category of the hierarchical taxonomy based on the selected labeled product.

2 . The computer-implemented method of claim 1 , wherein the selected labeled product has a highest likelihood of the likelihoods.

3 . The computer-implemented method of claim 1 , wherein the one or more labeled products are selected for input to the replacement model based on having one or more of the same characteristics as the unlabeled product.

4 . The computer-implemented method of claim 1 , wherein the replacement model is a machine learning model trained on historical data describing products selected by customers as replacements for an unavailable product.

5 . The computer-implemented method of claim 1 , wherein the replacement model is a query system that queries a graph database of replacements for products in the online concierge system.

6 . The computer-implemented method of claim 1 , wherein adding the unlabeled product to the category of the hierarchical taxonomy comprises:
sending, to a mobile device of a moderator, the category for display; and responsive to receiving confirmation from the moderator via the mobile device, adding the unlabeled product to the category.

7 . The computer-implemented method of claim 1 , wherein a subset of the hierarchical taxonomy was manually labeled by a moderator.

8 . A non-transitory computer-readable storage medium comprising instructions executable by a processor, the instructions comprising:
accessing a hierarchical taxonomy of products in an online concierge system, each product labeled with a category of the hierarchical taxonomy; receiving, from an inventory database, an unlabeled product, the unlabeled product not included in the hierarchical taxonomy; inputting the unlabeled product to a replacement model, wherein the replacement model is trained to output, for each of one or more labeled products from the hierarchical taxonomy, a likelihood that a user would select the labeled product as a replacement for an input product; selecting a labeled product from the one or more labeled products based on the likelihoods; and adding the unlabeled product to a category of the hierarchical taxonomy based on the selected labeled product.

9 . The non-transitory computer-readable storage medium of claim 8 , wherein the selected labeled product has a highest likelihood of the likelihoods.

10 . The non-transitory computer-readable storage medium of claim 8 , wherein the one or more labeled products are selected for input to the replacement model based on having one or more of the same characteristics as the unlabeled product.

11 . The non-transitory computer-readable storage medium of claim 8 , wherein the replacement model is a machine learning model trained on historical data describing products selected by customers as replacements for an unavailable product.

12 . The non-transitory computer-readable storage medium of claim 8 , wherein the replacement model is a query system that queries a graph database of replacements for products in the online concierge system.

13 . The non-transitory computer-readable storage medium of claim 8 , wherein the instructions for adding the unlabeled product to the category of the hierarchical taxonomy comprise:
sending, to a mobile device of a moderator, the category for display; and responsive to receiving confirmation from the moderator via the mobile device, adding the unlabeled product to the category.

14 . The non-transitory computer-readable storage medium of claim 8 , wherein a subset of the hierarchical taxonomy was manually labeled by a moderator.

15 . A computer system comprising:
a computer processor; and a non-transitory computer-readable storage medium storage instructions that when executed by the computer processor perform actions comprising:
accessing a hierarchical taxonomy of products in an online concierge system, each product labeled with a category of the hierarchical taxonomy;
receiving, from an inventory database, an unlabeled product, the unlabeled product not included in the hierarchical taxonomy;
inputting the unlabeled product to a replacement model, wherein the replacement model is trained to output, for each of one or more labeled products from the hierarchical taxonomy, a likelihood that a user would select the labeled product as a replacement for an input product;
selecting a labeled product from the one or more labeled products based on the likelihoods; and
adding the unlabeled product to a category of the hierarchical taxonomy based on the selected labeled product.

16 . The computer system of claim 15 , wherein the selected labeled product has a highest likelihood of the likelihoods.

17 . The computer system of claim 15 , wherein the one or more labeled products are selected for input to the replacement model based on having one or more of the same characteristics as the unlabeled product.

18 . The computer system of claim 15 , wherein the replacement model is a machine learning model trained on historical data describing products selected by customers as replacements for an unavailable product.

19 . The computer system of claim 15 , wherein the replacement model is a query system that queries a graph database of replacements for products in the online concierge system.

20 . The computer system of claim 15 , wherein adding the unlabeled product to the category of the hierarchical taxonomy comprises:
sending, to a mobile device of a moderator, the category for display; and responsive to receiving confirmation from the moderator via the mobile device, adding the unlabeled product to the category.

Assignees

• Maplebear Inc Dba Instacart

Inventors

• Prasad Shishir Kumar
• Rao Karikurve Sharath
• Pawar Abhay

Classifications

• G06Q30/0631 Primary

  Recommending goods or services · CPC title

• G06Q10/087 Primary

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

• G06F16/24573

  using data annotations, e.g. user-defined metadata · CPC title

• G06F16/285

  Clustering or classification · CPC title

• G06N20/00

  Machine learning · CPC title