Optimize shopping route using purchase embeddings

Patent metadata

Field	Value
Publication number	US-11869063-B2
Application number	US-202117365311-A
Country	US
Kind code	B2
Filing date	Jul 1, 2021
Priority date	Jul 1, 2021
Publication date	Jan 9, 2024
Grant date	Jan 9, 2024

Abstract

Aspects described herein may relate to methods, systems, and apparatuses that provide new capabilities for recommending purchases to a user based on the user's past purchasing history and the purchase history of others. A new descriptor referred to as “purchase embeddings” is disclosed, which are data records in a new multi-dimensional space for describing and tracking purchases of goods and services.

First claim

We claim:

1. A computer implemented method for suggesting alternative sources of goods or services, the method comprising:
receiving information identifying purchases, by a user, of a first plurality of items, wherein the information comprises data records in a multi-dimensional space relating to the first plurality of items;
identifying, based on the information, a correlation between the purchases of the first plurality of items, wherein the correlation shows a purchasing pattern of the user within a first geographic region;
detecting a second geographic region that includes a location of the user;
querying one or more databases for descriptions of a second plurality of items;
calculating, based on the descriptions and the received information identifying purchases, measures of similarity between the first and the second pluralities of items;
identifying, using a machine learning model and based on the descriptions and the calculating, a subset of the second plurality of items that are available for purchase within the second geographic region and that have measures of similarity to the first plurality of items that exceed predetermined thresholds;
generating, based on the identifying of the correlation and the identifying of the subset, a proposed pattern for purchasing the subset within the second geographic region, wherein the proposed pattern provides one or more vendor locations within the second geographic region at which the subset is sold;
sending, based on the proposed pattern exceeding a rating based on one or more metrics relating to the proposed pattern and to a device associated with the user, instructions with the proposed pattern for purchasing the subset;
receiving purchase information relating to the proposed pattern for purchasing the subset; and
updating the machine learning model based on the received purchase information and the generated proposed pattern.

2. The computer implemented method of claim 1 , further comprising:
limiting the one or more vendor locations to within a predetermined distance of the location of the user;
limiting the one or more vendor locations to a single common location;
limiting a distance between the one or more vendor locations;
providing, in the proposed pattern, a sequence to purchase items in the subset;
providing in the proposed pattern a proposed route between the one or more vendor locations; or
providing in the proposed pattern a time frame within when to purchase the subset.

3. The computer implemented method of claim 1 , further comprising:
identifying a possible location where one item in the subset may be purchased; and
selecting, based on one or more criteria, the possible location as one of the one or more vendor locations, wherein the one or more criteria include:
the possible location being identified as a small business,
a toll road being excluded in a route to the possible location,
the possible location being within a predefined distance to the location of the user,
the possible location being within a predefined distance to another one of the one or more vendor locations, or the possible location being identical to another one of the one or more vendor locations.

4. The computer implemented method of claim 1 , further comprising:
identifying possible locations where one item of the subset may be purchased;
receiving, via the device, a user preference; and
selecting, based on the user preference, one of the possible locations as one of the one or more vendor locations.

5. The computer implemented method of claim 1 , further comprising:
comparing the proposed pattern to the purchasing pattern; and
evaluating, based on the comparing, the proposed pattern according to one or more metrics, wherein the sending of the instructions to the device is based on the proposed pattern exceeding a predetermined rating according to the one or more metrics.

6. The computer implemented method of claim 5 , wherein the proposed pattern exceeding the predetermined rating indicates a travel time, a travel distance, a purchase cost, or a sales tax being lower for the subset than for the first plurality of items.

7. The computer implemented method of claim 1 , further comprising
ranking the measures of similarity between the first and the second pluralities of items, wherein the predetermined thresholds comprise a predetermined minimum ranking.

8. The computer implemented method of claim 1 , wherein the descriptions of the second plurality of items identify at least one of:
one or more geographic regions where one of the second plurality of items may be purchased,
a vendor location where one of the second plurality of items may be purchased,
a vendor identified as a small business where one of the second plurality of items may be purchased,
a class of goods or services of one of the second plurality of items, a similar item to one of the second plurality of items, or
an alternate name for one of the second plurality of items.

9. The computer implemented method of claim 1 , further comprising:
identifying a first time frame when the purchases of the first plurality of items occurred; and
including, based on the first time frame, a second time frame in the proposed pattern of when to purchase the subset.

10. The computer implemented method of claim 1 , wherein the correlation between the purchases of the first plurality of items is based on:
a common class of goods or services of two of the first plurality of items;
distances traveled for the purchases of the first plurality of items;
amounts paid for the purchases of the first plurality of items;
geographic areas where the purchases of the first plurality of items occurred;
time frames when the purchases of the first plurality of items occurred;
frequencies of repeated purchases of the first plurality of items;
durations between two of the purchases of the first plurality of items;
distances between where the purchases of the first plurality of items occurred; or
methods of payment for the purchases of the first plurality of items.

11. The method of claim 1 , wherein the multi-dimensional space comprises a tulple.

12. A server comprising:
at least one computer processor; and
computer memory comprising computer-executable instructions that when executed by the at least one computer processor, cause the server to:
receive information identifying purchases, by a user, of a first plurality of items, wherein the information comprises data records in a multi-dimensional space relating to the first plurality of items;
identify, based on the information, a correlation between the purchases of the first plurality of items, wherein the correlation shows a purchasing pattern of the user within a first geographic region;
detect a second geographic region that includes a location of the user;
query one or more databases for descriptions of a second plurality of items;
calculate, based on the descriptions, measures of similarity between the first and the second pluralities of items;
identify, using a machine learning model and based on the descriptions and the calculating, a subset of the second plurality of items that are available for purchase within the second geographic region and that have measures of similarity to the first plurality of items that exceed predetermined thresholds;
generate, based on the identifying of the correlation and the identifying of the subset, a proposed pattern for purchasing the subset within the second geographic region, wherein the proposed pattern provides one or more vendor locations within the second geographic region at which the subset is sold;
send, based on the proposed patt

Assignees

• Capital One Services Llc

Inventors

• Key Kate
• Truong Anh
• Goodsitt Jeremy
• Rafferty Galen
• Walters Austin
• Pham Vincent

Classifications

• G06Q30/0631 Primary

  Recommending goods or services · CPC title

• G01C21/3407

  specially adapted for specific applications · CPC title

• G01C21/3461

  Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types or segments such as motorways, toll roads or ferries · CPC title

• G06F16/245

  Query processing · CPC title

• G06F16/29

  Geographical information databases · CPC title