Computer implemented marketing system

• US 6,236,977 B1
• Filed: 01/04/1999
• Issued: 05/22/2001
• Est. Priority Date: 01/04/1999
• Status: Expired due to Term

• Current Assignee: Trident Holdings, Inc.
• Original Assignee: Realty One Incorporated (Trident Holdings, Inc.)
• Inventors: Verba, Stephen M., Ciepiel, Anthony M.
• Primary Examiner(s): Trammell, James P.
• Assistant Examiner(s): Tesfamariam, Mussie
• Application Number:
  US09/225,283
• Time in Patent Office: 869 Days
• Field of Search: 705/37, 705/10, 705/14, 705/26, 705/27, 705/44, 706/925
• US Class Current: 705/7.32
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0239 Online discounts or incentives
• CPC Class Codes: G06Q 30/0245 Surveys
• CPC Class Codes: G06Q 30/0271 Personalized advertisement
• CPC Class Codes: G06Q 30/0277 Online advertisement
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Abstract

A state-event engine manages behavior over time in response to events in deterministic fashion from user-supplied rules and policies for a set of dynamic objects. The campaign engine selectively generates and stores a campaign population, representing different types of marketing campaigns. A collection of dynamic object data stores maintains data on the corresponding marketing agent population, resource population, and listing population. A matching process orders members of the listing populations based on the target of at least two members of the campaign population such that a set of offers to buy and offers to sell the same resources is created. A prediction engines processes historical data to predict how campaigns can best match buyer to seller. Software agents negotiate on behalf of buyer and seller identify potential deals. The system is capable of being implemented using computer network and web-based technology.

Claims

1. A computer-implemented self-optimizing marketing system comprising:

a campaign engine for selectively generating and storing a campaign population, said campaign population having members comprising stored first data representing a plurality of marketing campaigns each campaign characterized by a plurality of campaign attributes including a plurality of campaign activities;

said campaign engine having processing functionality to assemble campaign population members from said campaign activities;

a customer population data store for storing a customer population, said customer population having members comprising stored second data representing a plurality of customers and potential customers, characterized by a plurality of customer attributes; and

an optimization engine for accessing said first and second data to optimize at least one of said campaign population and said customer population,

said optimization engine including a scoring system for ordering the members of at least one of said campaign population and said customer population, said scoring system employing adaptive scoring process that alters said scoring process based upon relations among at least some of said campaign attributes and said customer attributes.

2. The system of claim 1 further comprising an agent population data store for storing an agent population, said agent population having members comprising stored third data representing a plurality of agents each agent characterized by a plurality of agent attributes, and

wherein said scoring system orders the members of said agent population based upon relations among at least some of said customer attributes and said agent attributes.

3. The system of claim 1 wherein said optimization engine employs genetic algorithm.

4. The system of claim 1 wherein said campaign engine includes prediction engine and optimization engine.

5. A computer-implemented marketing system comprising:

a state-event engine which manages behavior over time of a plurality of dynamic objects in response to events in deterministic fashion from user-supplied rules, wherein said dynamic objects include;

a campaign engine for selectively generating and storing a campaign population, said campaign population having members comprising stored first data representing a plurality of marketing campaigns;

said campaign engine storing campaign activities data and having processing functionality to assemble campaign population members from said campaign activities;

a listing population data store for storing a listing population of offers to buy and sell resource items, said listing population having members comprising stored second data representing resource items having associated offers to buy or sell;

a matching process for ordering the members of said listing population based on members of said campaign population such that a set of both offers to buy and offers to sell the same resource item is created.

6. The system of claim 5 wherein said dynamic objects further include:

a marketing agent population data store for storing a population of marketing agents associated with at least one of said resource items.

7. The system of claim 6 wherein said population of marketing agents includes at least one software agent.

8. The system of claim 6 wherein said population of marketing agents has members comprising stored third data representing marketing agent types and marketing agent attributes.

9. The system of claim 5 wherein said dynamic objects further include:

a resource population data store for storing a population of resource items comprising fourth data representing resource item types and resource item attributes.

10. The system of claim 5 wherein said resource items are selected from the group consisting of real estate properties, goods and services.

11. The system of claim 9 wherein said resource item attributes include cost of an associated resource.

12. The system of claim 5 wherein at least one of said dynamic objects has an interface for interaction via a computer network.

13. The system of claim 5 further comprising scoring process mechanism for creating a uniform measure of value for applying to said resource items.

14. The system of claim 5 further comprising scoring process mechanism for creating a uniform measure of value for applying to said resource items based on rules specified by users identified from said listing population and said campaign population.

15. The system of claim 5 further comprising scoring process mechanism for creating a uniform measure of value for applying to said resource items based on a weighted combination of rule-based values and predicted values.

16. The system of claim 5 further comprising prediction engine that accesses said listing population data store and includes an historical database of previous actual and predicted resource listing values.

17. The system of claim 5 further comprising prediction engine that generates individual predictions with an associated confidence measure whereby individual predictions are weighted.

18. The system of claim 5 further comprising optimization engine that implements an automated exchange process between offers to sell and offers to buy.

19. The system of claim 18 wherein said exchange process is based on user-specified rules extracted from said first data.

20. The system of claim 18 wherein said exchange process is based on user-specified rules comprising a specificity score representing the degree of match between a first resource item attribute set corresponding to the buyer and a second resource item attribute set corresponding to the seller.

21. The system of claim 18 wherein said exchange process is based on user-specified rules comprising a specificity score representing an importance weight.

22. The system of claim 5 further comprising optimization engine that sequentially and iteratively orchestrates the manner of bid offerings and bid acceptance in an exchange process between offers to sell and offers to buy.

23. The system of claim 22 wherein said exchange process seeks the best possible bid for each resource item using a predefined rule set provided by a sponsor of the a campaign.

24. The system of claim 5 further comprising a prediction engine having an historical pattern data store and a means for automatically obtaining data to about consummated exchanges between buyer and seller to for updating said historical pattern data store.

25. The system of claim 24 wherein said prediction engine includes a scoring process that adjusts the weight given to predicted values to reflect increased confidence over time as the system is used.

26. A computer-implemented real estate marketing system to facilitate transactions among real estate agents, at least one real estate broker and members of the public, comprising:

a campaign engine for selectively generating a plurality of marketing campaigns each characterized by a plurality of campaign attributes, said campaign engine having processing functionality to assemble said marketing campaigns from a set of stored activities;

at least one data structure for access by said campaign engine for the storing and retrieving of;

(a) said campaign attributes,

(b) rules representing user-defined objectives of the marketing system,

(c) customer attributes reflecting the readiness and desires of selected members of the public with respect to real estate, and

(d) leads corresponding to selected members of the public;

said campaign engine having an agent interface by which said real estate agents may specify and store rules to be reflected in at least one marketing campaign and by which said real estate agents may retrieve leads representing selected members of the public;

said campaign engine having a broker interface by which said real estate broker may specify and store rules to be reflected in at least one marketing campaign and by which selected ones of said stored campaign attributes may be retrieved and communicated to said broker;

said campaign engine having a customer interface for the input and storing of customer attributes;

said campaign engine having a prediction engine that accesses said data structure to predict, based on said customer attributes, how said campaign attributes and leads may be modified to improve conformance to said rules and to predict which selected members of the public are most likely to engage in real estate transactions, thereby making marketing campaigns more attractive to members of the public and generating leads for said real estate agents.

27. A computer-implemented self-optimizing marketing system comprising:

a campaign engine for selectively generating and storing a campaign population on behalf of a campaign sponsor, said campaign population having members comprising stored first data representing a plurality of marketing campaigns each campaign characterized by a plurality of campaign attributes, including attributes reflecting available resources and the costs associated with those resources;

said campaign engine having an interface for input of the campaign sponsor'"'"'s desires with respect to a campaign;

said campaign engine further having processing functionality that seeks to marshal said available resources to maximize the campaign sponsor'"'"'s desires being met while minimizing the cost to do so;

a customer population data store for storing a customer population, said customer population having members comprising stored second data representing a plurality of customers and potential customers, characterized by a plurality of customer attributes; and

an optimization engine for accessing said first and second data to optimize at least one of said campaign population and said customer population,

said optimization engine including a scoring system for ordering the members of at least one of said campaign population and said customer population, said scoring system employing adaptive scoring process that alters said scoring process based upon relations among at least some of said campaign attributes and said customer attributes.

28. The system of claim 27 further comprising an agent population data store for storing an agent population, said agent population having members comprising stored third data representing a plurality of agents each agent characterized by a plurality of agent attributes, and

wherein said scoring system orders the members of said agent population based upon relations among at least some of said customer attributes and said agent attributes.

29. The system of claim 27 wherein said optimization engine employs genetic algorithm.

30. The system of claim 27 wherein said campaign engine includes prediction engine and optimization engine.

31. A computer-implemented marketing system comprising:

a state-event engine which manages behavior over time of a plurality of dynamic objects in response to events in deterministic fashion from user-supplied rules, wherein said dynamic objects include;

a campaign engine for selectively generating and storing a campaign population, said campaign population having members comprising stored first data representing a plurality of marketing campaigns, said first data including data reflecting available resources and the costs associated with those resources;

said campaign engine having an interface for input of the campaign sponsor'"'"'s desires with respect to a campaign;

said campaign engine further having processing functionality that seeks to marshal said available resources to maximize the campaign sponsor'"'"'s desires being met while minimizing the cost to do so;

a listing population data store for storing a listing population of offers to buy and sell resource items, said listing population having members comprising stored second data representing resource items having associated offers to buy or sell;

a matching process for ordering the members of said listing population based on members of said campaign population such that a set of both offers to buy and offers to sell the same resource item is created.

32. The system of claim 31 wherein said dynamic objects further include:

a marketing agent population data store for storing a population of marketing agents associated with at least one of said resource items.

33. The system of claim 32 wherein said population of marketing agents includes at least one software agent.

34. The system of claim 32 wherein said population of marketing agents has members comprising stored third data representing marketing agent types and marketing agent attributes.

35. The system of claim 31 wherein said dynamic objects further include:

a resource population data store for storing a population of resource items comprising fourth data representing resource item types and resource item attributes.

36. The system of claim 31 wherein said resource items are selected from the group consisting of real estate properties, goods and services.

37. The system of claim 35 wherein said resource item attributes include cost of an associated resource.

38. The system of claim 31 wherein at least one of said dynamic objects has an interface for interaction via a computer network.

39. The system of claim 31 further comprising scoring process mechanism for creating a uniform measure of value for applying to said resource items.

40. The system of claim 31 further comprising scoring process mechanism for creating a uniform measure of value for applying to said resource items based on rules specified by users identified from said listing population and said campaign population.

41. The system of claim 31 further comprising scoring process mechanism for creating a uniform measure of value for applying to said resource items based on a weighted combination of rule-based values and predicted values.

42. The system of claim 31 further comprising prediction engine that accesses said listing population data store and includes an historical database of previous actual and predicted resource listing values.

43. The system of claim 31 further comprising prediction engine that generates individual predictions with an associated confidence measure whereby individual predictions are weighted.

44. The system of claim 31 further comprising optimization engine that implements an automated exchange process between offers to sell and offers to buy.

45. The system of claim 44 wherein said exchange process is based on user-specified rules extracted from said first data.

46. The system of claim 44 wherein said exchange process is based on user-specified rules comprising a specificity score representing the degree of match between a first resource item attribute set corresponding to the buyer and a second resource item attribute set corresponding to the seller.

47. A computer-implemented self-optimizing marketing system comprising:

a campaign engine for selectively generating and storing a campaign population, said campaign population having members comprising stored first data representing a plurality of marketing campaigns each campaign characterized by a plurality of campaign attributes;

a customer population data store for storing a customer population, said customer population having members comprising stored second data representing a plurality of customers and potential customers, characterized by a plurality of customer attributes;

an optimization engine for accessing said first and second data to optimize at least one of said campaign population and said customer population,

said optimization engine including a scoring system for ordering the members of at least one of said campaign population and said customer population, said scoring system employing adaptive scoring process that alters said scoring process based upon relations among at least some of said campaign attributes and said customer attributes; and

an agent population data store for storing an agent population, said agent population having members comprising stored third data representing a plurality of agents each agent characterized by a plurality of agent attributes,

wherein said scoring system orders the members of said agent population based upon relations among at least some of said customer attributes and said agent attributes.

48. A computer-implemented marketing system comprising:

a state-event engine which manages behavior over time of a plurality of dynamic objects in response to events in deterministic fashion from user-supplied rules, wherein said dynamic objects include;

a campaign engine for selectively generating and storing a campaign population, said campaign population having members comprising stored first data representing a plurality of marketing campaigns;

a listing population data store for storing a listing population of offers to buy and sell resource items, said listing population having members comprising stored second data representing resource items having associated offers to buy or sell;

a matching process for ordering the members of said listing population based on members of said campaign population such that a set of both offers to buy and offers to sell the same resource item is created; and

a scoring process mechanism for creating a uniform measure of value for applying to said resource items.

49. The system of claim 48 wherein said scoring process mechanism for creating a uniform measure of value for applying to said resource items based on rules specified by users identified from said listing population and said campaign population.

50. The system of claim 48 wherein said scoring process mechanism for creating a uniform measure of value for applying to said resource items based on a weighted combination of rule-based values and predicted values.

51. The system of claim 48 further comprising optimization engine that implements an automated exchange process between offers to sell and offers to buy wherein said exchange process is based on user-specified rules comprising a specificity score representing the degree of match between a first resource item attribute set corresponding to the buyer and a second resource item attribute set corresponding to the seller.

52. The system of claim 48 further comprising optimization engine that implements an automated exchange process between offers to sell and offers to buy wherein said exchange process is based on user-specified rules comprising a specificity score representing an importance weight.

53. The system of claim 48 further comprising a prediction engine having an historical pattern data store and a means for automatically obtaining data to about consummated exchanges between buyer and seller to for updating said historical pattern data store and wherein said prediction engine includes a scoring process that adjusts the weight given to predicted values to reflect increased confidence over time as the system is used.