Method and system for selection, filtering or presentation of available sales outlets

• US 9,189,800 B2
• Filed: 09/16/2014
• Issued: 11/17/2015
• Est. Priority Date: 07/01/2011
• Status: Active Grant

• Current Assignee: TrueCar, Inc.
• Original Assignee: TrueCar, Inc.
• Inventors: McBride, Jason, Sullivan, Thomas J., Swinson, Michael D., Wang, Zixia
• Primary Examiner(s): Starks, Wilbert L
• Application Number:
  US14/488,062
• Publication Number:
  US 20150006250A1
• Time in Patent Office: 427 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 7/00 Computing arrangements based on specific mathematical models
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: G06Q 30/0629 for generating comparisons
• CPC Class Codes: G06Q 30/08 Auctions

Abstract

Embodiments disclosed herein provide systems and methods for the filtering, selection and presentation of vendors accounting for both user characteristics and vendor characteristics, such that the systems and methods may be used by both customer and vendor alike to better match customer needs with the resource-constrained vendors with whom a successful sale has a higher probability of occurring. Embodiments may include filtering, selecting and/or presenting vendors to a user sorted by the probability that the particular vendor will possess the characteristics that appeal to a particular customer and therefore result in a large probability of sale and suppress presentation of those vendors that are unlikely to be selected by the customer since their characteristics are less consistent with those needed by the customer and, therefore, are unlikely to result in a sale.

Claims

1. A system, comprising:

at least one processor;

non-transitory computer memory; and

stored instructions translatable by the at least one processor, the stored instructions when translated by the at least one processor perform;

for each vendor in a set of vendors;

determining a first probability of a vendor selling a product to a user interested in purchasing the product given that the vendor is presented in the set of vendors;

determining a second probability of the user buying the product from the vendor given a historical preference of the user;

determining a third probability of closing a sale where the third probability is a function of the first and second probabilities; and

determining an expected revenue using the third probability;

filtering the set of vendors based at least in part on the expected revenue to produce a filtered list of vendors, the filtered list of vendors comprising a subset of the set of vendors; and

presenting the filtered list of vendors to the user interested in purchasing the product via a user interface on a user device associated with the user.

2. The system of claim 1, wherein the filtered list of vendors presented to the user is sorted based at least in part on the third probability associated with each vendor in the filtered list of vendors.

3. The system of claim 1, wherein the filtered list of vendors presented to the user is sorted based at least in part on an expected vendor revenue associated with each vendor in the filtered list of vendors.

4. The system of claim 1, wherein the filtered list of vendors presented to the user is sorted based at least in part on an expected entity revenue associated with an entity not in the filtered list of vendors.

5. The system of claim 1, wherein the expected revenue is determined based at least in part on a substitutability associated with the product.

6. The system of claim 1, wherein the first probability is determined based at least in part on features of the vendor relative to other vendors in a geographic region.

7. The system of claim 1, wherein the second probability is determined based at least in part on historical sales between the vendor and other users in a geographic region.

8. A method, comprising:

at a computer having at least one processor, non-transitory computer memory, and stored instructions translatable by the at least one processor;

for each vendor in a set of vendors;

determining a first probability of a vendor selling a product to a user interested in purchasing the product given that the vendor is presented in the set of vendors;

determining a second probability of the user buying the product from the vendor given a historical preference of the user;

determining a third probability of closing a sale where the third probability is a function of the first and second probabilities; and

determining an expected revenue using the third probability;

the computer filtering the set of vendors based at least in part on the expected revenue to produce a filtered list of vendors, the filtered list of vendors comprising a subset of the set of vendors; and

the computer presenting the filtered list of vendors to the user interested in purchasing the product via a user interface on a user device associated with the user.

9. The method according to claim 8, wherein the filtered list of vendors presented to the user is sorted based at least in part on the third probability associated with each vendor in the filtered list of vendors.

10. The method according to claim 8, wherein the filtered list of vendors presented to the user is sorted based at least in part on an expected vendor revenue associated with each vendor in the filtered list of vendors.

11. The method according to claim 8, wherein the filtered list of vendors presented to the user is sorted based at least in part on an expected entity revenue associated with an entity not in the filtered list of vendors.

12. The method according to claim 8, wherein the expected revenue is determined based at least in part on a substitutability associated with the product.

13. The method according to claim 8, wherein the first probability is determined based at least in part on features of the vendor relative to other vendors in a geographic region;

and wherein the second probability is determined based at least in part on historical sales between the vendor and other users in the geographic region.

14. A computer program product comprising at least one non-transitory computer readable medium storing instructions translatable by at least one processor to perform:

for each vendor in a set of vendors;

determining a first probability of a vendor selling a product to a user interested in purchasing the product given that the vendor is presented in the set of vendors;

determining a second probability of the user buying the product from the vendor given a historical preference of the user;

determining a third probability of closing a sale where the third probability is a function of the first and second probabilities; and

determining an expected revenue using the third probability;

filtering the set of vendors based at least in part on the expected revenue to produce a filtered list of vendors, the filtered list of vendors comprising a subset of the set of vendors; and

presenting the filtered list of vendors to the user interested in purchasing the product via a user interface on a user device associated with the user.

15. The computer program product of claim 14, wherein the filtered list of vendors presented to the user is sorted based at least in part on the third probability associated with each vendor in the filtered list of vendors.

16. The computer program product of claim 14, wherein the filtered list of vendors presented to the user is sorted based at least in part on an expected vendor revenue associated with each vendor in the filtered list of vendors.

17. The computer program product of claim 14, wherein the filtered list of vendors presented to the user is sorted based at least in part on an expected entity revenue associated with an entity not in the filtered list of vendors.

18. The computer program product of claim 14, wherein the expected revenue is determined based at least in part on a substitutability associated with the product.

19. The computer program product of claim 14, wherein the first probability is determined based at least in part on features of the vendor relative to other vendors in a geographic region.

20. The computer program product of claim 14, wherein the second probability is determined based at least in part on historical sales between the vendor and other users in a geographic region.