Electronic brokerage system and methods of performing the same

• US 8,244,736 B2
• Filed: 07/29/2005
• Issued: 08/14/2012
• Est. Priority Date: 07/30/2004
• Status: Active Grant

• Current Assignee: Fastxchange Incorporated
• Original Assignee: Fastxchange Incorporated
• Inventors: Postel, Paul, Adimoolam, Murali, Chen, Jinbo
• Primary Examiner(s): TRUONG, CAM Y T
• Application Number:
  US11/193,176
• Publication Number:
  US 20060031085A1
• Time in Patent Office: 2,573 Days
• Field of Search: 707/748, 707/999.03, 705/40
• US Class Current: 707/748
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 30/0603 Catalogue ordering

Abstract

A method of determining an optimized supplier list for a consumer using a web based client-server system is described. The method provides for receiving a query from a user and assigning a policy to the query. A search step and an evaluation step are then performed. Additional optional operational steps are provided to enhance the users buying efficiency.

Claims

1. A computer-implemented method of determining a supplier list for a user using a web based server-client computer system, the method comprising:

under control of one or more computer systems configured with executable instructions,

(a) receiving a query from a user through a web portal, said query having information for one or more goods requested by the user;

(b) assigning a policy to said query, the policy having one or more search methods and one or more evaluation methods, said one or more search methods configured to perform a correlation operation on a partitioned database comprising data relating to suppliers and goods provided by the suppliers, the correlation operation determining one or more probability values for one or more suppliers to have the one or more goods, the partitioned database maintaining a separate data structure for each search method, wherein said one or more search methods further comprise a mathematical operation used to determine probabilities between variables;

(c) executing said one or more search methods to produce a correlation list between said one or more goods and said one or more suppliers based on the determined one or more probability values;

(d) evaluating said correlation list based on one or more predetermined evaluation criteria using said one or more evaluation methods;

(e) based on the correlation list evaluation, producing an optimized supplier list, wherein said optimized supplier list is returned to said user through said web portal; and

(f) obtaining valid quotes from the one or more suppliers identified on said optimized supplier list,

wherein the producing the correlation list includes computing a probability of obtaining a valid quote from the one or more suppliers based on prior iterations of the one or more search methods, and

producing the optimized supplier list includes computing a probability of obtaining a desired number of quotes based on removal of one or more of the identified suppliers, and if the probability of obtaining a desired number of quotes remains above a threshold set in the policy, then removing said one or more of the identified suppliers from the optimized supplier list.

2. The method of claim 1, further comprising:

(g) purchasing the one or more goods from at least one supplier identified by the optimized supplier list for delivery to the user.

3. The method of claim 1, wherein said partitioned database is a dynamic database.

4. The method of claim 3, wherein said dynamic database further comprises a plurality of associated data structures.

5. The method of claim 3, wherein said dynamic database is updated using information obtained through said one or more search methods.

6. The method of claim 3, wherein said dynamic database is updated using information obtained through said one or more evaluation methods.

7. The method of claim 1, further comprising creating an initial structure of at least one of the separate data structures of the partitioned database based on parsing of product data received from one or more of the suppliers.

8. The method of claim 1, wherein assigning a policy to said query includes parsing the query and converting data from the query to a set of canonical tokens.

9. The method of claim 1, wherein producing the optimized supplier list includes creating a hierarchical list of identified suppliers based on a parameter included in the assigned policy and removing lower ranked suppliers.

10. A non-transitory computer readable storage medium containing instructions for producing a supplier list that, when executed by a computer, cause the computer to:

(a) receive a query from a user through a web portal, said query having information for one or more goods requested by the user;

(b) assign a policy to said query, the policy having one or more search methods and one or more evaluation methods, said one or more search methods configured to perform a correlation operation on a partitioned database comprising data relating to suppliers and goods provided by the suppliers, the correlation operation determining one or more probability values for one or more suppliers to have the one or more goods, the partitioned database maintaining a separate data structure for each search method, wherein said one or more search methods further comprise a mathematical operation used to determine probabilities between variables;

(c) execute said one or more search methods to produce a correlation list between said one or more goods and one or more suppliers based on the determined one or more probability values;

(d) evaluate said correlation list based on one or more predetermined evaluation criteria using said one or more evaluation methods;

(e) based on the correlation list evaluation, producing an optimized supplier list, wherein said optimized supplier list is returned to said user through said web portal; and

(f) obtain valid quotes from the one or more suppliers identified on said optimized supplier list,

wherein the producing the correlation list includes computing a probability of obtaining a valid quote from the one or more suppliers based on prior iterations of the one or more search methods, and

produce the optimized supplier list includes computing a probability of obtaining a desired number of quotes based on removal of one or more of the identified suppliers, and if the probability of obtaining a desired number of quotes remains above a threshold set in the policy, then removing said one or more of the identified suppliers from the optimized supplier list.

11. The computer readable storage medium of claim 10, further comprising instructions for creating an initial structure of at least one of the separate data structures of the partitioned database based on parsing of product data received from one or more of the suppliers.

12. The computer readable storage medium of claim 10, wherein assigning a policy to said query includes parsing the query and converting data from the query to a set of canonical tokens.

13. The computer readable storage medium of claim 10, wherein producing the optimized supplier list includes creating a hierarchical list of identified suppliers based on a parameter included in the assigned policy and removing lower ranked suppliers.