Self learning method and system for managing a group reward system

• US 9,324,023 B2
• Filed: 07/17/2012
• Issued: 04/26/2016
• Est. Priority Date: 11/14/2001
• Status: Expired due to Fees

• 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: Otto, Jonathan, Van Luchene, Andrew
• Primary Examiner(s): Holmes, Michael B
• Application Number:
  US13/551,581
• Publication Number:
  US 20130024263A1
• Time in Patent Office: 1,379 Days
• Field of Search: 706/12
• US Class Current: 1/1
• 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 computer 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.

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 or second 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 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.

4. The method of claim 1 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.

5. 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.

6. The method of claim 1 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.

11. 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 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; and

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

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 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 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 system of claim 7 wherein the processor is for:

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

,

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

9. The system of claim 7 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 wherein;

generating the first agreement includes using the history;

or,

generating the incentive includes using the history.

10. The system of claim 7 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.

12. The system of claim 7 wherein the processor is for:

storing a third metric in the memory element;

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 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.