Price estimation system

• US 10,453,103 B2
• Filed: 03/15/2013
• Issued: 10/22/2019
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: Home Depot Product Authority, LLC (The Home Depot, Inc.)
• Original Assignee: Home Depot Product Authority, LLC (The Home Depot, Inc.)
• Inventors: Miller, Robert L., Schulte, John S., Illouz, Mika
• Primary Examiner(s): Civan, Ethan D
• Application Number:
  US13/837,342
• Publication Number:
  US 20140279167A1
• Time in Patent Office: 2,412 Days
• Field of Search: 705 261- 272
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: G06Q 30/0611 Request for offers or quotes

Abstract

A method of operating a price estimation system includes obtaining service requests from consumers, and obtaining bids from service professionals based on the service requests obtained from the consumers. The method additionally includes generating a training set based on the bids and the service requests, and generating a model for generating price estimates based on the training set. The method also includes employing the model to generate the price estimates based on additional service requests provided by additional consumers. In some aspects, the method further includes communicating the price estimates to the additional consumers during performance of a process for obtaining the additional service requests from the additional consumers. In other aspects, the method further includes communicating the price estimates to additional service professionals during performance of a process for obtaining additional bids from the additional service professionals based on the additional service requests.

Claims

1. A method of operating a price estimation system, the method comprising:

obtaining service requests from consumers;

obtaining bids from service professionals based on the service requests obtained from the consumers;

generating a training set based on the bids and the service requests, wherein generating the training set includes;

establishing an empty training set data structure;

retrieving, from a database, a plurality of service requests R and bid data B(R) comprising bids b associated with the service requests;

for each service request R in the plurality of service requests;

for each bid b in the bid data B(R) associated with the service request R;

adding to the training set data structure an entry comprising the service request R and the bid b;

generating a model for generating price estimates by applying a machine learning algorithm to the training set;

employing the model to generate the price estimates based on additional service requests provided by additional consumers; and

communicating the price estimates over a communications network to at least one of;

a user interface accessible by the additional consumers during performance of a process for obtaining the additional service requests from the additional consumers, or

a user interface accessible by additional service professionals during performance of a process for obtaining additional bids from the additional service professionals based on the additional service requests.

2. The method of claim 1, wherein the process for obtaining the additional service requests includes prompting a consumer to make at least one of service category selection or a specific service selection.

3. The method of claim 2, wherein the process for obtaining the additional service requests from consumers includes:

receiving at least one of the service category selection or the specific service selection;

prompting the consumer to make one or more specific service detail selections based on the specific service selection; and

receiving one or more specific service detail selections from the consumer,

wherein prompting the consumer to make one or more specific service detail selections is further based on one or more previously received specific service detail selections.

4. The method of claim 3, wherein employing the model to generate the price estimates includes performing generation of a new price estimate upon receipt of each of the service category selection, the specific service selection, and the one or more specific service detail selections.

5. The method of claim 4, wherein communicating the price estimates to the additional consumers includes displaying the new price estimate to the consumer upon generation thereof.

6. The method of claim 1, wherein obtaining bids from service professionals includes:

communicating service request information to the service professionals; and

receiving the bids from the service professionals.

7. The method of claim 1, wherein the process for obtaining additional bids from the additional service professionals includes:

communicating additional service request information to a service professional;

receiving a bid from the service professional; and

generating a price estimate based on the additional service request information.

8. The method of claim 7, further comprising:

communicating the price estimate to the service professional with the additional service request information.

9. The method of claim 7, further comprising:

comparing the bid from the service professional to one or more thresholds associated with the price estimate to perform detection of an out of range bids; and

informing the service professional of the detection of the out of range bid.

10. The method of claim 6, wherein receiving the bids from service professionals includes receiving at least one of a flat rate quote, a range quote, or an onsite required indication.

11. The method of claim 1, wherein generating the model includes applying the machine learning algorithm to the entries in the training set data structure to obtain a model M that approximates each bid b for each service request R in the training set data structure.

12. The method of claim 11, wherein employing the model to generate the price estimates includes applying the model M to a partially completed service request to generate a price estimate.

13. The method of claim 1, wherein generating the training set further includes:

retrieving, from the database, characteristics of consumers U(R) making the service requests R and characteristics of service professionals P(b) making bids b responsive to the service requests R;

for each of the entries in the training set data structure comprising each service request R and each bid b;

adding to each respective entry the characteristics of the consumer U(R) and the characteristics of the service professionals P(b).

14. The method of claim 13, wherein generating the model to generate price estimates includes applying the machine learning algorithm to the entries in the training set data structure to obtain a model M that approximates each bid b and characteristics of service professionals P(b) for each service request R and characteristics of consumer U(R) in the training set data structure.

15. A system of operating a price estimation system, the system comprising:

a memory;

a processor coupled to the memory, the processor configured to;

obtain service requests from consumers;

obtain bids from service professionals based on the service requests obtained from the consumers;

generate a training set based on the bids and the service requests, whereby the processor is further configured to;

establish an empty training set data structure;

retrieve, from a database, a plurality of service requests R and bid data B(R) comprising bids b associated with the service requests R;

for each service request R in the plurality of service requests;

for each bid b in the bid data B(R) associated with the service request R;

 

add to the training set data structure an entry comprising the service request R and the bid b;

generate a model for generating price estimates by applying a machine learning algorithm to the training set;

employ the model to generate the price estimates based on additional service requests provided by additional consumers; and

communicate the price estimates to at least one of;

the additional consumers during performance of a process for obtaining the additional service requests from the additional consumers, or

additional service professionals during performance of a process for obtaining additional bids from the additional service professionals based on the additional service requests.

16. The system of claim 15, wherein the processor is further configured to:

prompt a consumer to make at least one of service category selection or a specific service selection.

17. The system of claim 16, wherein the processor is further configured to:

receive at least one of the service category selection or the specific service selection;

prompt the consumer to make one or more specific service detail selections based on the specific service selection; and

receive one or more specific service detail selections from the consumer,

wherein prompting the consumer to make one or more specific service detail selections is further based on one or more previously received specific service detail selections.

18. The system of claim 17, wherein the processor is further configured to:

generate a new price estimate upon receipt of each of the service category selection, the specific service selection, and the one or more specific service detail selections.

19. The system of claim 18, wherein the processor is further configured to:

display the new price estimate to the consumer upon generation thereof.

20. The system of claim 15, wherein the processor is further configured to:

communicate service request information to the service professionals; and

receive the bids from the service professionals.

21. The system of claim 15, wherein the processor is further configured to:

communicate additional service request information to a service professional;

receive a bid from the service professional; and

generate a price estimate based on the additional service request information.

22. The system of claim 21, wherein the processor is further configured to:

communicate the price estimate to the service professional with the additional service request information.

23. The system of claim 21, wherein the processor is further configured to:

compare the bid from the service professional to one or more thresholds associated with the price estimate to perform detection of an out of range bid; and

inform the service professional of the detection of the out of range bid.

24. The system of claim 20, wherein the processor is further configured to:

receive at least one of a flat rate quote, a range quote, or an onsite required indication.

25. The system of claim 15, wherein the processor is further configured to:

apply the machine learning algorithm to the entries in the training set data structure to obtain a model M that approximates each bid b for each service request R in the training set data structure.

26. The system of claim 25, wherein the processor is further configured to:

apply the model M to a partially completed service request to generate a price estimate.

27. The system of claim 15, wherein the processor is further configured to generate the training set by:

retrieving, from the database, characteristics of a consumer U(R) making the requests R and characteristics of service professionals P(b) making the bids b;

for each of the entries in the training set data structure comprising each service request R and each bid b;

adding to each respective entry the characteristics of the consumer U(R) and the characteristics of the service professionals P(b).

28. The system of claim 27, wherein the processor is further configured to:

apply the machine learning algorithm to the entries in the training set data structure to obtain a model M that approximates each bid b and characteristics of service professionals P(b) for each service request R and characteristics of consumer U(R) in the training set data structure.