Self learning method and system for managing a group reward system

• US 20090171769A1
• Filed: 02/12/2009
• Published: 07/02/2009
• Est. Priority Date: 11/14/2001
• Status: Active Grant

• Current Assignee: RetailDNA LLC (Walker Innovation Inc. (f/k/a Patent Properties, Inc.))
• Original Assignee: RetailDNA LLC (Walker Innovation Inc. (f/k/a Patent Properties, Inc.))
• Inventors: VAN LUCHENE, Andrew, OTTO, Jonathan
• Granted Patent: US 8,224,760 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06N 3/12 using genetic models
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0215 Including financial accounts
• CPC Class Codes: G06Q 30/0224 based on user history
• CPC Class Codes: G06Q 30/0635 Processing of requisition or of purchase orders
• CPC Class Codes: G06Q 40/12 Accounting

Abstract

A system for managing a purchase agreement, including: a memory element for at least one specially-programmed general purpose computer for storing an artificial intelligence program (AIP) and a purchase agreement between a customer and at least one business entity, the purchase agreement including at least one requirement regarding at least one retail transaction between the customer and the business entity; a processor in the specially-programmed general purpose computer for: compiling a purchasing history for the customer with respect to the business entity and the purchase agreement, the memory element for storing the purchasing history, and modifying, using the purchasing history and the AIP, the at least one requirement to increase revenue or profitability of the business entity; and an interface element in the specially-programmed general purpose computer for transmitting the modified at least one requirement for presentation to the customer.

Claims

1. A self-learning computer-based method for managing a purchase agreement, comprising:

storing, in a memory element for at least one specially-programmed general purpose compute an artificial intelligence program (AIP), first, second, and third metrics, and first data identifying a first plurality of customers;

selecting, using a processor in the at least one specially-programmed general purpose computer, second data from the first data, the second data identifying a second plurality of customers from the first plurality of customers and the selecting including use of the first metric and the AIP;

generating, using the processor, the second metric, and the AIP, an agreement between the second plurality of customers and at least one business entity, the purchase agreement including at least one requirement regarding retail transactions between the second plurality of customers and the at least one business entity;

generating an incentive using the processor, the third metric, and the AIP, the rewarding of the incentive conditional upon satisfaction of the at least one requirement; and

,

transmitting, using an interface element in the at least one specially-programmed general purpose computer, the agreement and the incentive for presentation to at least one customer from the second plurality of customers.

2. The method of claim 1 further comprising:

compiling, using the processor, operational data regarding profitability of the at least one business entity; and

,

creating the first, second, or third metric, using the processor, the operational data, and the AIP.

3. The method of claim 1 further comprising:

compiling, using the processor, a history of transactions conducted by the first plurality of customers; and

,

creating, using the processor, the AIP, and the history, the first, second, or third metric;

or,

wherein selecting the second plurality of customers includes using the history.

4. The method of claim 1 further comprising:

compiling, using the processor, a history of transactions conducted by the second plurality of customers; and

,

modifying the agreement using the processor, the AIP, and the history;

or,

modifying the incentive using the processor, the AIP, and the history.

5. The method of claim 1 further comprising compiling, using the processor, a history of transactions conducted by the second plurality of customers;

and wherein;

generating the agreement includes using the history;

or,

generating the incentive includes using the history.

6. The method of claim 1 wherein the at least one requirement includes respective requirements for at least two customers in the first plurality of customers and wherein at least two of the respective requirements are different.

7. A self-learning computer-based method for managing a purchase agreement, comprising:

storing, in a memory element for at least one specially-programmed general purpose compute an artificial intelligence program (AIP) and first and second metrics;

receiving, using an interface element in the at least one specially-programmed general purpose computer, a request identifying a first plurality of customers and a desired incentive;

generating, using the processor, the AIP, and the first metric, a first agreement between the first plurality of customers and at least one business entity, the first agreement including at least one first requirement regarding a first plurality of retail transactions between the first plurality of customers and the at least one business entity;

generating a first incentive using the processor, the desired incentive, the second metric, and the AIP, the rewarding of the first incentive conditional upon satisfaction of the at least one first requirement; and

,

transmitting, using an interface element in the at least one specially-programmed general purpose computer, the first agreement and the first incentive for presentation to at least one customer from the first plurality of customers.

8. The method of claim 7 further comprising:

compiling, using the processor, operational data regarding profitability of the at least one business entity; and

,

creating the first or second metric using the processor, the operational data, and the AIP.

9. The method of claim 7 further comprising:

compiling, using the processor, a history of transactions conducted by the first plurality of customers; and

,

creating the first or second metric using the processor, the AIP, and the history;

or wherein;

generating the first agreement includes using the history;

or,

generating the incentive includes using the history.

10. The method of claim 7 further comprising:

compiling, using the processor, a history of transactions conducted by the first plurality of customers; and

,

modifying the first agreement using the processor, the AIP, and the history;

or,

modifying the incentive using the processor, the AIP, and the history.

11. The method of claim 7 wherein the at least one requirement includes respective requirements for at least two customers in the first plurality of customers and wherein at least two of the respective requirements are different.

12. The method of claim 7 further comprising:

storing a third metric in the memory element;

compiling, using the processor, a history of transactions conducted by the first plurality of customers;

selecting, using the processor, the history, the third metric, and the AIP, a second plurality of customer from the first plurality of customers, the second plurality including less than all of the first plurality;

generating, using the processor, the AIP, and the first metric, a second agreement between the second plurality of customers and the at least one business entity, the second agreement including at least one second requirement regarding a second plurality of retail transactions between the second plurality of customers and the at least one business entity; and

,

generating a second incentive using the processor, the AIP, the desire incentive, and the second metric, the rewarding of the second incentive conditional upon satisfaction of the at least one second requirement.

13. A self-learning computer-based method for managing a purchase agreement, comprising:

storing, in a memory element for at least one specially-programmed general purpose computer;

an artificial intelligence program (AIP);

a purchase agreement between a plurality of customers and at least one business entity, the purchase agreement including a requirement regarding retail transactions between the plurality of customers and the at least one business entity;

an incentive, the rewarding of the incentive conditional upon completion of the requirement; and

,

a value of the incentive;

compiling, using a processor in the at least one specially-programmed general purpose computer, a history of transactions between the plurality of customers and the at least one business entity; and

,

determining, using the processor, compliance of transactions included in the history of transactions with the requirement;

or,

determining, using the processor, profitability of the at least one business entity with respect to the history; and

,

modifying, using the processor, the AIP, and the history, the value of the incentive to encourage compliance of the plurality of customers with the requirement;

or,

modifying, using the processor, the AIP, and the history, the incentive to increase profitability of the at least one business entity with respect to future transactions between the plurality of customers and the at least one business entity.

14. A self-learning computer-based method for managing a purchase agreement, comprising:

storing an artificial intelligence program (AIP) in a memory element for at least one specially-programmed general purpose computer;

generating, using a processor in at least one specially-programmed general purpose computer and the AIP, first and second purchase agreements between a customer and at least one business entity, the first and second purchase agreements including first and second requirements, respectively, regarding at least one first and second retail transaction, respectively, between the customer and the at least one business entity;

compiling, using a processor in the at least one specially-programmed general purpose computer, a purchasing history for the customer with respect to the at least one business entity and the first and second purchase agreements;

determining, using the processor and the first and second purchasing histories, a relative profitability of the first and second purchasing agreements;

modifying, using the processor, the relative profitability, and the AIP, the first or second requirement to increase revenue or profitability of the at least one business entity; and

,

transmitting, using an interface element in the at least one specially-programmed general purpose computer, the modified first or second requirement for presentation to the customer.

15. The method of claim 14 further comprising:

receiving, using the interface element, an offer from the customer to enter into a third purchase agreement with the at least one business entity, the third purchase agreement including a third requirement regarding at least one third retail transaction between the customer and the at least one business entity;

determining, using the processor, a profitability of the third purchase agreement;

for a profitability less than a specified value, modifying the at least one third requirement using the processor and the AIP; and

,

transmitting, using the interface element, the modified third requirement for presentation to the customer.

16. A self-learning computer-based system for managing a purchase agreement, comprising:

a memory element for at least one specially-programmed general purpose computer for storing;

an artificial intelligence program (AIP);

first, second, and third metrics; and

first data identifying a first plurality of customers;

a processor in the at least one specially-programmed general purpose computer for;

selecting, using the first metric and the AIP, second data from the first data, the second data identifying a second plurality of customers from the first plurality of customers;

generating, using the second metric and the AIP, an agreement between the second plurality of customers and at least one business entity, the purchase agreement including at least one requirement regarding a plurality of retail transactions between the second plurality of customers and the at least one business entity;

generating, using the third metric and the AIP, an incentive, the rewarding of the incentive conditional upon satisfaction of the at least one requirement; and

,

an interface element in the at least one specially-programmed general purpose computer for transmitting the agreement and the incentive for presentation to at least one customer from the second plurality of customers.

17. The system of claim 16 wherein the processor is for:

compiling operational data regarding profitability of the at least one business entity; and

,

creating the first, second, or third metric using the operational data and the AIP.

18. The system of claim 16 wherein the processor is for:

compiling a history of transactions conducted by the first plurality of customers; and

,

creating the first, second, or third metric using the AIP and the history;

or,

selecting the second plurality of customers using the history.

19. The system of claim 16 wherein the processor is for:

compiling a history of transactions conducted by the second plurality of customers; and

,

modifying the agreement using the AIP and the history;

or,

modifying the incentive using the AIP and the history.

20. The system of claim 16 wherein the processor is for:

compiling a history of transactions conducted by the second plurality of customers; and

,

generating the first agreement using the history;

or,

generating the incentive using the history.

21. The system of claim 16 wherein the at least one requirement includes respective requirements for at least two customers in the first plurality of customers and wherein at least two of the respective requirements are different.

22. A self-learning computer-based system for managing a purchase agreement, comprising:

a memory element for at least one specially-programmed general purpose computer for storing;

an artificial intelligence program (AIP); and

first and second metrics;

an interface element in the at least one specially-programmed general purpose computer adapted for receiving a request identifying a first plurality of customers and a desired incentive; and

,

a processor in the at least one specially-programmed general purpose computer for;

generating, using the AIP and the first metric, a first agreement between the first plurality of customers and at least one business entity, the first agreement including at least one first requirement regarding a first plurality of retail transactions between the first plurality of customers and the at least one business entity; and

,

generating a first incentive using the desired incentive, the second metric, and the AIP, the rewarding of the first incentive conditional upon satisfaction of the at least one first requirement, wherein the interface element is for transmitting the first agreement and the first incentive for presentation to at least one customer from the first plurality of customers.

23. The system of claim 22 wherein the processor is for:

compiling operational data regarding profitability of the at least one business entity; and

,

creating, using the operational data and the AIP, the first or second metric.

24. The system of claim 22 wherein the processor is for:

compiling a history of transactions conducted by the first plurality of customers; and

,

creating the first or second metric using the AIP and the history;

or,

generating the first agreement using the history;

or,

generating the incentive using the history.

25. The system of claim 22 wherein the processor is for:

compiling a history of transactions conducted by the first plurality of customers; and

,

modifying the first agreement using the AIP and the history;

or,

modifying the incentive using the AIP and the history.

26. The system of claim 22 wherein the at least one requirement includes respective requirements for at least two customers in the first plurality of customers and wherein at least two of the respective requirements are different.

27. The system of claim 22 wherein the memory element is for storing a third metric and wherein the processor is for:

compiling a history of transactions conducted by the first plurality of customers;

selecting, using the history, the third metric, and the AIP, a second plurality of customer from the first plurality of customers, the second plurality including less than all of the first plurality;

generating, using AIP and the first metric, a second agreement between the second plurality of customers and the at least one business entity, the second agreement including at least one second requirement regarding a second plurality of retail transactions between the second plurality of customers and the at least one business entity; and

,

generating a second incentive using the desire incentive, the second metric, and the AIP, the rewarding of the second incentive conditional upon satisfaction of the at least one second requirement.

30. The system of claim 22 wherein the interface element is for receiving an offer from the customer to enter into a third purchase agreement with the at least one business entity, the third purchase agreement including a third requirement regarding at least one third retail transaction between the customer and the at least one business entity, wherein the processor is for:

determining a profitability of the third purchase agreement; and

for a profitability less than a specified value, modifying the at least one third requirement using the AIP, and wherein the interface element is for transmitting the modified third requirement for presentation to the customer.

28. A self-learning computer-based system for managing a purchase agreement, comprising:

a memory element for at least one specially-programmed general purpose computer for storing;

an artificial intelligence program (AIP);

a purchase agreement between a plurality of customers and at least one business entity, the purchase agreement including a requirement regarding retail transactions between the plurality of customers and the at least one business entity;

an incentive, the rewarding of the incentive conditional upon completion of the requirement; and

,

a value of the incentive;

a processor for the at least one specially-programmed general purpose computer for;

compiling a history of transactions between the plurality of customers and the at least one business entity; and

,

determining compliance of transactions included in the history of transactions with the requirement;

or,

determining profitability of the at least one business entity with respect to the history; and

,

modifying, using the AIP and the history, the value of the incentive to encourage compliance of the plurality of customers with the requirement;

or,

modifying, using the AIP and the history, the incentive to increase profitability of the at least one business entity with respect to future transactions between the plurality of customers and the at least one business entity; and

,

an interface element in the at least one specially-programmed general purpose computer for transmitting the modified value or the modified incentive for presentation to at least one customer from the plurality of customers.

29. A self-learning computer-based system for managing a purchase agreement, comprising:

a memory element for at least one specially-programmed general purpose computer for storing an artificial intelligence program (AIP);

a processor for at least one specially-programmed general purpose computer for;

generating, using the AIP, first and second purchase agreements between a customer and at least one business entity, the first and second purchase agreements including first and second requirements, respectively, regarding at least one first and second retail transaction, respectively, between the customer and the at least one business entity;

compiling a purchasing history for the customer with respect to the at least one business entity and the first and second purchase agreements;

determining, using the first and second purchasing histories, a relative profitability of the first and second purchasing agreements; and

,

modifying, using the relative profitability and the AIP, the first or second requirement to increase revenue or profitability of the at least one business entity; and

,

an interface element in the at least one specially-programmed general purpose computer for transmitting the modified first or second requirement for presentation to the customer.