Method, system and apparatus for matching sellers to a buyer over a network and for managing related information

• US 8,266,011 B2
• Filed: 10/12/2011
• Issued: 09/11/2012
• Est. Priority Date: 07/20/2006
• Status: Active Grant

• Current Assignee: Emma Group, Inc.
• Original Assignee: Torrenegra IP, LLC
• Inventors: Torrenegra, Alex Henriquez, Zapata, Tania
• Primary Examiner(s): Pond, Robert M.
• Application Number:
  US13/271,675
• Publication Number:
  US 20120030052A1
• Time in Patent Office: 335 Days
• Field of Search: 705/26, 705/27, 705/37
• US Class Current: 705/26.4
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: G06Q 30/0605 Supply or demand aggregation
• CPC Class Codes: G06Q 30/08 Auctions

Abstract

A method and system for facilitating a transaction between a buyer and one of a number of sellers is provided. The transaction is related to a project specified by the buyer. A set of sellers is identified. A flow control process operates over a number of successive processing cycles. In each processing cycle, sellers are selected from the set for classification as sellers of a particular type (“Invited Sellers”). The Invited Sellers increase incrementally over the number of successive processing cycles. The system automatically enables at least one action carried out by the Invited Sellers in each processing cycle. The duration of the processing cycles can vary. The number of Invited Sellers selected in each processing cycle can vary. In one embodiment, the number of Invited Sellers selected in each processing cycle depends upon a number of offers that are submitted by Invited Sellers and received by the buyer.

Claims

1. A computer-implemented method executed on a data processing system, the method for facilitating a transaction between a buyer and one of a number of sellers, the transaction related to a project specified by the buyer, the method comprising:

i) on the data processing system, defining a time period for the transaction;

ii) on the data processing system, identifying a set of sellers represented by data stored in a database of the data processing system;

iii) on the data processing system, generating data associated with the set of sellers of ii), wherein the data associated with a given seller is independent of any information specified by the given seller particularly for the project;

iv) on the data processing system, for each given processing cycle over a number of successive processing cycles that occur during the time period for the transaction of i), automatically classifying at least one seller of the set of sellers of ii) as belonging to a particular type associated with the project, wherein the automatic classification of iv) is based upon the data generated in iii) and irrespective of any information specified by sellers particularly for the project, and wherein the number of sellers classified as belonging to the particular type in iv) automatically increases incrementally over the number of successive processing cycles; and

v) on the data processing system, in each given processing cycle, automatically enabling submission of at least one offer related to the project by the sellers classified as belonging to the particular type in iv), whereby the at least one offer can be accepted by the buyer as part of the transaction related to project.

2. A method according to claim 1, wherein:

the duration of each given processing cycle varies over the number of successive processing cycles.

3. A method according to claim 2, wherein:

the duration of each given processing cycle is derived from a time period specified by the buyer.

4. A method according to claim 3, wherein:

the duration of each given processing cycle is derived from a predetermined constant that results in exponential reduction of said durations.

5. A method according to claim 1, wherein:

the number of sellers classified as belonging to the particular type in each given processing cycle in iv) depends upon a number of offers that are submitted by sellers classified as belonging to the particular type and received by the buyer for the project in previous processing cycles.

6. A method according to claim 1, wherein:

the number of sellers classified as belonging to the particular type in an initial processing cycle depends on a number of offers the buyer wishes to receive as dictated by input from the buyer.

7. A method according to claim 1, wherein:

the number of sellers classified as belonging to the particular type in each given processing cycle is constant over the processing cycles.

8. A method according to claim 1, further comprising:

on the data processing system, storing offers submitted by the sellers of the particular type and related information in a database for access by the buyer.

9. A method according to claim 8, wherein:

the information related to the offers includes multimedia files related to the offers.

10. A method according to claim 1, further comprising:

on the data processing system, enabling sellers classified as belonging to the particular type to access detailed information related to the project.

11. A method according to claim 1, further comprising:

on the data processing system, enabling communication between the sellers classified as belonging to the particular type and the buyer.

12. A method according to claim 1, further comprising:

automatically notifying the sellers of their classification to the particular type.

13. A method according to claim 1, further comprising:

automatically notifying the buyer of the sellers classified as belonging to the particular type.

14. A method according to claim 1, wherein:

the data associated with the set of sellers includes likelihood indices for the set of sellers, wherein the likelihood index associated with a given seller characterizes likelihood that the given seller will match the buyer'"'"'s needs for the project.

15. A method according to claim 14, further comprising:

on the data processing system, automatically ranking the sellers of the set according to the corresponding likelihood indices;

wherein the automatic classification of iv) selects higher ranked sellers before lower ranked sellers.

16. A method according to claim 15, wherein:

the likelihood index for a seller is derived from a number of index calculations.

17. A method according to claim 16, wherein:

said index calculations include a group of relevancy subindices and a group of priority subindicies.

18. A method according to claim 17, wherein:

said relevancy sub indices include at least one of;

a preferred-seller index, a geographical-proximity index, a keyword-matching index, and a relevant-samples-of-work index; and

said priority subindices include at least of;

a quality-assurance index, a preferred-seller popularity index, a proposal-history index, a seller-seniority index, and a pay-per-proposal submission index.

19. A method according to claim 1, wherein:

the data associated with the set of sellers includes profile data of the sellers, and the automatic classification of iv) matches requirements of the project as specified by the buyer to the profile data of sellers.

20. A method according to claim 19, wherein:

the automatic classification of iv) involves similarity analysis with respect to the requirements of the project and the profile data of sellers.

21. A data processing system for facilitating a transaction between a buyer and one of a number of sellers, the transaction related to a project, the data processing system comprising:

a database;

first means for interacting with a buyer over a communication network to specify a project and storing data related to the project in the database;

second means for interacting with a number of sellers over the communication network and storing data related to the sellers in the database, wherein certain data associated with a given seller is independent of any information specified by the given seller particularly for the project;

third means for defining a time period for the transaction;

fourth means for performing an automated process for identifying a set of sellers represented by the data stored in a database;

fifth means for automatically classifying at least one seller of the set of sellers as belonging to a particular type associated with the project over a number of successive processing cycles that occur during the time period for the transaction, wherein the automatic classification of the fifth means is based upon the certain data of the sellers stored in the database and irrespective of any information specified by sellers particularly for the project, and wherein the number of sellers classified as belonging to the particular type by the fifth means automatically increases incrementally over the number of successive processing cycles; and

sixth means that automatically enables submission of at least one offer related to the project by the sellers classified as belonging to the particular type, whereby the at least one offer can be accepted by the buyer as part of the transaction related to project.

22. A system according to claim 21, wherein:

the first, second, third, fourth, fifth and sixth means are realized by an application server operably coupled to the Internet.