Real-time Internet data mining system and method for aggregating, routing, enhancing, preparing, and analyzing web databases

• US 20070011224A1
• Filed: 09/12/2006
• Published: 01/11/2007
• Est. Priority Date: 10/22/1999
• Status: Abandoned Application

• Current Assignee: Jesus Mena
• Original Assignee: Jesus Mena
• Inventors: Mena, Jesus
• Application Number:
  US11/519,360
• Publication Number:
  US 20070011224A1
• Time in Patent Office: Days
• US Class Current: 709/200
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A real-time Internet data mining system comprising a database, data processing, clustering, segmentation, and classification algorithms, and a networking server. The system receives customer account data from subscriber servers and prepares it for analysis. The data is transmitted to third-party data depositories. The third-parties append selected consumer behavioral information matched by a key, such as a physical or an e-mail address. The appended information is returned to the data mining system where multiple algorithms analyze the accounts based on a desired prediction. The scored accounts and analyses are returned to the originating subscriber servers for use in marketing communications.

Claims

1. A data mining system comprising:

one or more subscriber servers for collecting information identifying a user and providing a first data set of user information,

one or more demographic databases having third party information relating to targeted market segments and providing a second data set of said third party information relating to targeted market segments; and

a processor in operative communication with the one or more subscriber servers and the one or more demographic databases and receiving said first data set from the one or more subscriber servers and said second data set from the one or more demographic databases,

said processor including a rule processor receiving said first data set and said second data set and applying said first and second data sets to one or more rules to determine a score predicting behavior relating to said collected information identifying said user;

wherein the processor receives the first data set of user information from one of the subscriber servers and generates a unique key corresponding to the collected information identifying a user; and

wherein the one or more subscriber servers are coupled to an Internet;

the one or more demographic databases are coupled to the Internet; and

the processor is coupled to the Internet.

2. The system according to claim 1 wherein the unique key is a member of the set consisting of an e-mail address, a postal address, a Social Security Number and a TCP/IP address.

3. The system according to claim 1 wherein said rules processor employs pattern recognition technologies selected from the set consisting of neural networks, machine-learning and genetic algorithms.

4. The system according to claim 1 wherein said processor communicates said key to said one or more demographics databases;

and

wherein said processor receives appended information associated with said key from said one or more demographics databases.

5. The system according to claim 4 wherein said score is generated by clustering, segmenting and classifying said appended information.

6. The system according to claim 4 wherein said appended information is a member of the set consisting of household information, demographic information and webographic information.

7. A method of mining data, said method comprising the steps of:

receiving from one or more subscriber servers user-identifying indicia and providing a first data set of user information;

generating from the user-identifying indicia a key which corresponds to values indexed by one or more demographic databases having third party information relating to targeted market segments;

communicating the key to the one or more demographic databases;

receiving from the one or more demographic databases demographic information relating to the user-identifying indicia and providing a second data set of said third party information relating to targeted market segments;

applying said first and second data sets to one or more rules to determine a score predicting behavior relating to the user-identifying indicia; and

communicating the predictive score to the one or more subscriber servers.

8. A method according to claim 7 further comprising the step of the subscriber server determining whether or not to offer a user a product based on the score.

9. A method according to claim 7 further comprising the step of the subscriber server determining at what price to offer a product to a user based on the score.

10. A method according to claim 7 wherein the score is a propensity to-purchase score indicating statistically a user'"'"'s propensity to make a purchase.

11. A method according to claim 7 wherein the score is determined using a neural network.

12. A method according to claim 7 wherein said third party information relating to targeted market segments includes household income, gender, age and occupation of the user.