Systems and methods for recommending a retail location

• US 9,836,778 B2
• Filed: 11/18/2016
• Issued: 12/05/2017
• Est. Priority Date: 11/29/2012
• Status: Active Grant

• Current Assignee: eBay Inc.
• Original Assignee: eBay Inc.
• Inventors: Lyman, Nate L
• Primary Examiner(s): VO, TUYEN KIM
• Application Number:
  US15/355,411
• Publication Number:
  US 20170069012A1
• Time in Patent Office: 382 Days
• Field of Search: 235375, 235383
• CPC Class Codes: G06K 19/06037 multi-dimensional coding
• CPC Class Codes: G06K 7/1417 2D bar codes
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 30/0629 for generating comparisons
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 30/0639 Item locations
• CPC Class Codes: H04W 4/023 using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds

Abstract

A method and a system are disclosed for generating a recommendation of a retail location on a network-based system. For example, a system may obtain a retail location definition associated with a geographic location. The geographic location may represent the retail location. The system then builds a scan event model from product scan messages received from a plurality of scanning devices located within the geographic location. The scan event model may include one or more scan events each being associated with a product definition and the retail location definition. Next, a recommendation query from the search device is received by the system. The recommendation query may include a product identifier and a query location. The system may generate a recommendation of the retail location based on determining that the product identifier and the query location match the one or more scan events of the scan event model.

Claims

1. A computer system comprising:

a processor;

a memory device holding an instruction set executable on the processor to cause the computer system to perform operations comprising;

a scan event handler, implemented by one or more processors, configured to build a scan event model from historical product scan messages previously received from a plurality of mobile scanning devices associated with a plurality of users, each of the historical product scan messages having a product association and a scan location association; and

a recommendation engine, implemented by the one or more processors, configured to;

receive a query from a specific mobile scanning device associated with a specific user, the query including a product identifier;

identify a subset of the historical scan messages, wherein each historical scan message in the subset is associated with a product definition that matches the received product identifier; and

generate a recommendation for the specific user based on a merchant identified by a plurality of different retail locations represented in the subset of historical scan messages.

2. The computer system of claim 1, wherein the received product identifier comprises a product name.

3. The computer system of claim 1, wherein to generate the recommendation, the recommendation engine is further configured to:

determine each historical scan message in the subset of historical scan messages is associated a number of different retail locations from a plurality of select retail locations of the merchant identified by a merchant definition; and

determine the number of different retail locations of the merchant satisfies an inference threshold number of scan messages.

4. The computer system of claim 3, the recommendation engine is further configured to:

based on satisfaction of the inference threshold number of scan messages, generate the recommendation for the specific user based on the merchant.

5. The computer system of claim 1, wherein the historical product scan messages are received responsive to each of the respective mobile scanning devices scanning a universal product code (UPC) attached to a product.

6. The computer system of claim 1, wherein a respective scan location association of a given historical product scan message corresponds to geo-location information from a respective mobile scanning device.

7. A non-transitory computer-readable medium storing executable instructions thereon, which, when executed by a processor, cause the processor to perform operations including:

building a scan event model from historical product scan messages previously received from a plurality of mobile scanning devices associated with a plurality of users, each of the historical product scan messages having a product association and a scan location association;

receiving a query from a specific mobile scanning device associated with a specific user, the query including a product identifier;

identifying a subset of the historical scan messages, wherein each historical scan message in the subset is associated with a product definition that matches the received product identifier; and

generating a recommendation for the specific user based on a merchant identified by a plurality of different retail locations represented in the subset of historical scan messages.

8. The non-transitory computer-readable medium of claim 7, wherein received product identifier comprises a product name.

9. The non-transitory computer-readable medium of claim 7, wherein generating the recommendation comprises:

determining each historical scan message in the subset of historical scan messages is associated a number of different retail locations from a plurality of select retail locations of the merchant identified by a merchant definition; and

determining the number of different retail locations of the merchant satisfies an inference threshold number of scan messages.

10. The non-transitory computer-readable medium of claim 9, further comprises:

based on satisfaction of the inference threshold number of scan messages, generating the recommendation for the specific user based on the merchant.

11. The non-transitory computer-readable medium of claim 10, wherein the query includes geo-location information from the specific mobile scanning device associated with the specific user.

12. The non-transitory computer-readable medium of claim 11, wherein one of the plurality of select retail locations of the merchant is proximate to the geo-location information from the specific mobile scanning device associated with the specific user.

13. The non-transitory computer-readable medium of claim 7, wherein a respective scan location association of a given historical product scan message corresponds to geo-location information from a respective mobile scanning device.

14. A computer-implemented method, comprising:

building a scan event model from historical product scan messages previously received from a plurality of mobile scanning devices associated with a plurality of users, each of the historical product scan messages having a product association and a scan location association;

receiving a query from a specific mobile scanning device associated with a specific user, the query including a product identifier;

identifying, by at least one hardware processor, a subset of the historical scan messages, wherein each historical scan message in the subset is associated with a product definition that matches the received product identifier; and

generating a recommendation for the specific user based on a merchant identified by a plurality of different retail locations represented in the subset of historical scan messages.

15. The computer-implemented method of claim 14, wherein the received product identifier comprises a product name.

16. The computer-implemented method of claim 14, wherein generating the recommendation comprises:

determining each historical scan message in the subset of historical scan messages is associated a number of different retail locations from a plurality of select retail locations of the merchant identified by a merchant definition; and

determining the number of different retail locations of the merchant satisfies an inference threshold number of scan messages.

17. The computer-implemented method of claim 16, further comprises:

based on satisfaction of the inference threshold number of scan messages, generating the recommendation for the specific user based on the merchant.

18. The computer-implemented method of claim 17, wherein the query includes geo-location information from the specific mobile scanning device associated with the specific user.

19. The computer-implemented method of claim 18, wherein one of the plurality of select retail locations of the merchant is proximate to the geo-location information from the specific mobile scanning device associated with the specific user.

20. The computer-implemented method of claim 14, wherein the historical product scan messages are received responsive to each of the respective mobile scanning devices scanning a universal product code (UPC) attached to a product.