Self learning method and system for managing a third party subsidy offer

• US 20090182627A1
• Filed: 03/11/2009
• Published: 07/16/2009
• Est. Priority Date: 11/14/2001
• Status: Abandoned Application

• 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
• Application Number:
  US12/381,350
• Publication Number:
  US 20090182627A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06N 5/025 Extracting rules from data
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• 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 30/0601 Electronic shopping [e-shopping]

Abstract

A self-learning computer-based system for managing a third party subsidy offer, including: 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 for the computer for receiving an order, the order including an item or service offered by a first business entity; and a processor for the computer. The processor is for: generating, using the AIP and the first metric, an agreement with a second business entity; and generating an incentive, using the AIP and the second metric, the rewarding of the incentive conditional upon acceptance of the agreement. The interface element is for transmitting the agreement and the incentive for presentation. In one embodiment, the processor compiles operational data regarding profitability of the first business entity and modifies the first or second metric using the operational data and the AIP.

Claims

1. A self-learning computer-based method for managing a third party subsidy offer, comprising:

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

receiving, using an interface element for the at least one specially programmed general-purpose computer, an order, the order including an item or service offered by a first business entity;

generating, using a processor for the at least one specially programmed general-purpose computer, the AIP, and the first metric, an agreement with a second business entity;

generating an incentive using the processor, the AIP, and the second metric, the rewarding of the incentive conditional upon acceptance of the agreement; and

,

transmitting, using the interface element, the agreement and the incentive for presentation.

2. The method of claim 1 further comprising:

compiling, using the processor, operational data regarding profitability of the first business entity; and

,

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

3. The method of claim 1 further comprising:

identifying, using the processor, a customer associated with the order;

compiling, using the processor, a history of transactions conducted by the customer; and

,

modifying the first or second metric using the AIP and the history of transactions.

4. The method of claim 1 further comprising:

identifying, using the processor, a customer associated with the order; and

,

compiling, using the processor, a history of transactions conducted by the customer; and

,

wherein generating the agreement includes using the history of transactions;

or, wherein generating the incentive includes using the history of transactions.

5. The method of claim 4 wherein the history of transactions includes an incentive previously presented to the customer or an agreement previously presented to the customer;

and wherein generating the agreement includes modifying the agreement previously presented to the customer;

or, wherein generating the incentive includes modifying the incentive previously presented to the customer.

6. The method of claim 1 further comprising storing in the memory element a performance metric, wherein receiving an order includes receiving a plurality of orders including respective items or services offered by the first business entity and wherein transmitting the agreement and the incentive for presentation includes transmitting, responsive to receiving each order in the plurality of orders and using the interface element, the agreement and the incentive for presentation, the method further comprising:

receiving, for said each order and using the interface, a response message including acceptance or rejection of the agreement or the incentive;

compiling, using the processor, a response history based on the response messages for the plurality of orders; and

,

modifying the agreement or the incentive using the processor, the AIP, the performance metric, and the response history.

7. The method of claim 1 wherein the agreement includes a requirement, a time period for complying with the requirement, and a penalty for failure to comply with the requirement, and the method further comprising:

compiling, using the processor, a history of compliance with the requirement; and

,

modifying the agreement, the incentive, the requirement, the time period, or the penalty using the processor, the AIP and the history of compliance.

9. The method of claim 6 further comprising compiling, using the processor, respective operational data regarding profitability of the first or second business entities and wherein modifying the agreement or the incentive includes using the operational data.

10. The method of claim 6 further comprising:

identifying, using the processor, a respective customer associated with said each order; and

,

compiling, using the processor, a history of transactions conducted by the respective customers associated with said each order and wherein modifying the agreement or the incentive includes using the history of transactions.

11. The method of claim 6 further comprising:

identifying, using the processor, a respective customer associated with said each order;

compiling, using the processor, a history of transactions conducted by the respective customers associated with said each order; and

,

modifying the first metric using the processor, the AIP, and the history of transactions.

13. The system of claim 10 wherein the processor is for:

compiling operational data regarding profitability of the first business entity; and

,

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

14. The system of claim 10 wherein the processor is for:

identifying a customer associated with the order;

compiling a history of transactions conducted by the customer; and

,

modifying the first or second metric using the AIP and the history of transactions.

15. The system of claim 10 wherein the processor is for:

identifying a customer associated with the order; and

,

compiling a history of transactions conducted by the customer; and

wherein generating the agreement includes using the history of transactions;

or, wherein generating the incentive includes using the history of transactions.

16. The system of claim 13 wherein the history of transactions includes an incentive previously presented to the customer or an agreement previously presented to the customer;

and wherein generating the agreement includes modifying the agreement previously presented to the customer;

or, wherein generating the incentive includes modifying the incentive previously presented to the customer.

18. The system of claim 15 wherein the processor is for compiling respective operational data regarding profitability of the first or second business entities and wherein modifying the agreement or the incentive includes using the operational data.

19. The system of claim 15 wherein the processor is for:

identifying a respective customer associated with said each order; and

,

compiling a history of transactions conducted by the respective customers and wherein modifying the agreement or the incentive includes using the history of transactions.

20. system of claim 15 wherein the processor is for:

identifying a respective customer associated with said each order;

compiling a history of transactions conducted by the respective customers associated with said each order; and

,

modifying the first metric using the AIP and the history of transactions.

8. A self-learning computer-based method for managing a third party subsidy offer, comprising:

storing, in a memory element for at least one specially-programmed general purpose computer, an artificial intelligence program (AIP), a performance metric, an agreement with a first business entity, and an incentive conditional upon acceptance of the agreement;

receiving, using an interface element for the at least one specially programmed general-purpose computer, a plurality of orders including respective items or services offered by a second business entity;

transmitting, responsive to receiving each order in the plurality of orders and using the interface element, the agreement and the incentive for presentation;

receiving, for said each order and using the interface, a response message including acceptance or rejection of the agreement or the incentive;

compiling, using the processor, a response history based on the response messages for the plurality of orders; and

,

modifying the agreement or the incentive using the processor, the AIP, the first metric, and the response history.

12. A self-learning computer-based system for managing a third party subsidy offer, 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 for the at least one specially programmed general-purpose computer for receiving an order, the order including an item or service offered by a first business entity; and

,

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

generating, using the AIP and the first metric, an agreement with a second business entity; and

,

generating an incentive, using the AIP and the second metric, the rewarding of the incentive conditional upon acceptance of the agreement; and

wherein the interface element is for transmitting the agreement and the incentive for presentation.

17. A self-learning computer-based system for managing a third party subsidy offer, comprising:

a memory element for at least one specially-programmed general purpose computer for storing an artificial intelligence program (AIP), a first metric, an agreement with a first business entity, and an incentive conditional upon acceptance of the agreement;

an interface element for the at least one specially programmed general-purpose computer for;

receiving a plurality of orders including respective items or services offered by a second business entity;

transmitting, responsive to receiving each order in the plurality of orders, the agreement and the incentive for presentation; and

,

receiving, for said each order, a response message including acceptance or rejection of the agreement or the incentive; and

,

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

compiling a response history based on the response messages for the plurality of orders; and

,

modifying the agreement or the incentive using the AIP, the first metric, and the response history.