OPEN MEDIA EXCHANGE PLATFORMS

Abstract

Methods, systems, and apparatus, including computer program products, for identifying a set of business entities eligible to participate in a transaction involving an online advertisement space; generating a graph of the set of business entities using pre-stored information defining relationships between the business entities in the set, each business entity in the set being represented by a node of the graph, each relationship between a pair of identified business entities being represented by an edge of the graph, one of the nodes of the graph being designated as a source node, and one or more of the nodes of the graph being designated as sink nodes; and performing a series of decision processes to identify one of the business entities in the set represented by a sink node to execute the transaction with the business entity represented by the source node.

Description

DESCRIPTION OF DRAWINGS

FIG. 1 shows a block diagram of an open exchange environment.

FIG. 2 is a flowchart of a process for facilitating an online advertisement transaction.

FIG. 3 schematically depicts an environment in which a transaction management system provides a platform for facilitating online advertisement transactions; and

FIGS. 4-8 each schematically depict a hierarchical auction process.

Claims

1. A computer-implemented method comprising: identifying a set of business entities eligible to participate in a transaction involving an online advertisement space;generating a graph of the set of business entities using pre-stored information defining relationships between the business entities in the set, each business entity in the set being represented by a node of the graph, each relationship between a pair of identified business entities being represented by an edge of the graph, one of the nodes of the graph being designated as a source node, and one or more of the nodes of the graph being designated as sink nodes; andperforming a series of decision processes to identify one of the business entities in the set represented by a sink node to execute the transaction with the business entity represented by the source node.

2. The method of claim 1, wherein the performing comprises: for a particular sink node of the graph, applying a pathing algorithm to determine a path between the source node and the particular sink node.

3. The method of claim 2, wherein the pathing algorithm comprises a shortest path algorithm.

4. The method of claim 2, wherein the path between the source node and the particular sink node passes through no other nodes of the graph.

5. The method of claim 4, wherein: the source node represents a provider of the online advertisement space; andthe particular sink node represents a potential consumer of the online advertisement space.

6. The method of claim 5, wherein the particular sink node is associated with at least one advertisement creative dimensioned to fit the online advertisement space.

7. The method of claim 5, wherein the particular sink node is associated with at least one advertisement campaign having at least one advertisement creative dimensioned to fit the online advertisement space.

8. The method of claim 5, wherein the provider of the online advertisement space is a host of a web page having the online advertisement space.

9. The method of claim 2, wherein the path between the source node and the particular sink node passes through one or more interior nodes of the graph.

10. The method of claim 9, wherein: the source node represents a provider of the online advertisement space;each of the one or more interior nodes represents an intermediary to facilitate the transaction; andthe particular sink node represents a potential consumer of the online advertisement space.

11. The method of claim 10, wherein each intermediary is an advertisement network, an advertisement broker, an advertisement agency, or an advertiser.

12. The method of claim 10, wherein the particular sink node is associated with at least one advertisement creative dimensioned to fit the online advertisement space.

13. The method of claim 10, wherein the particular sink node is associated with at least one advertisement campaign having at least one advertisement creative dimensioned to fit the online advertisement space.

14. The method of claim 10, wherein the provider of the online advertisement space is a host of a web page having the online advertisement space.

15. The method of claim 1, wherein the series of decision processes are performed recursively at the non-sink nodes of the graph, the non-sink nodes comprising the source node and one or more interior nodes.

16. The method of claim 15, wherein each of the series of decision processes comprises a comparison of bid prices associated with nodes directly coupled to the non-sink node at which the decision process is being performed.

17. The method of claim 1, wherein at least one of the decision processes of the series comprises a comparison of at least two bid prices associated with nodes of the graph.

18. The method of claim 17, wherein the at least two bid prices are normalized in accordance with one or more pricing models prior to the comparison.

19. The method of claim 17, wherein the at least two bid prices are normalized in accordance with one or more predictive metrics associated with respective advertisement creatives prior to the comparison.

20. The method of claim 17, wherein the at least two bid prices are associated with nodes of the graph that are children of the node at which the decision process is being performed.

21. The method of claim 1, wherein performing the series of decision processes comprises: comparing bids associated with nodes of the graph.

22. The method of claim 21, wherein comparing bids comprises: comparing bids based on one or more of the following metrics: a priority metric, a normalized price metric, an advertiser value metric, and a percentage delivered metric.

23. The method of claim 1, wherein performing the series of decision processes comprises: performing a comparison of at least two bids associated with nodes of the graph based on a normalized price metric; andif the comparison yields a tie result, performing one or more additional comparisons of the at least two bids associated with nodes of the graph based on one or more of the following metrics: a priority metric, an advertiser value metric, and a percentage delivered metric, until a tie-breaking result is yielded.

24. The method of claim 1, wherein performing the series of decision processes comprises: performing a comparison of at least two bids associated with nodes of the graph based on a normalized price metric; andif the comparison yields a tie result, randomly selecting one of the at least two bids to yield a result.

25. The method of claim 1, wherein at least one of the decision processes of the series comprises a propagation of information in a direction from the sink nodes to the source node, the information being related to a node that is a child of the node at which the decision process is being performed.

26. The method of claim 25, wherein the information comprises a bid price.

27. The method of claim 1, wherein the business entity in the set identified to execute the transaction is associated with a bid price that yields a highest revenue for the business entity represented by the source node.

28. The method of claim 1, wherein the performing comprises: determining a payment amount to be paid by the business entity in the set identified to execute the transaction with the business entity represented by the source node.

29. The method of claim 28, wherein the payment amount to be paid is all or a portion of a bid price associated with the business entity in the set identified to execute the transaction.

30. The method of claim 1, wherein the performing comprises: identifying a first one of the business entities of the set associated with a first bid price that yields a highest revenue for the business entity represented by the source node;identifying a second one of the business entities of the set associated with a second bid price that yields a second highest revenue for the business entity represented by the source node; andapplying a dynamic pricing reduction rule to the first bid price based on the second bid price to determine a payment amount to be paid by the first one of the business entities of the set, the first one of the business entities being the business entity in the set identified to execute the transaction.

31. The method of claim 30, wherein the payment amount to be paid is the second bid price incremented by a predetermined margin.

32. The method of claim 31, wherein the predetermined margin is expressed as a percentage.

33. The method of claim 30, wherein the payment amount to be paid is greater than a bid price associated with any node within a subtree of the graph in which the first one of the business entities is located.

34. The method of claim 1, wherein each of the business entities in the set satisfies a set of constraints associated with the transaction involving the online advertisement space.

35. The method of claim 34, wherein the set of constraints comprises constraints related to one or more of the following: price, geography, time of delivery, location of delivery, quantity, and language.

36. The method of claim 1, further comprising: receiving information defining a relationship between a first business entity and a second business entity; andstoring the received information.

37. The method of claim 36, wherein the received information comprises information related to a revenue sharing agreement.

38. The method of claim 1, further comprising: executing the transaction between the identified one of the business entities in the set represented by a sink node with the business entity represented by the source node.

39. The method of claim 38, wherein the executing comprises one or more of the following: providing sufficient first information to each business entity represented by a node in a path between the source node and the sink node representing the identified one of the business entities in the set to effect the execution of the transaction;logging sufficient second information to document the execution of the transaction; andenabling an advertisement creative associated with the business entity representing the identified one of the business entities in the set to be delivered directly or indirectly to the business entity representing the source node.

40. A machine-readable medium that stores executable instructions to cause a machine to: identify a set of business entities eligible to participate in a transaction involving an online advertisement space;generate a graph of the set of business entities using pre-stored information defining relationships between the business entities in the set, each business entity in the set being represented by a node of the graph, each relationship between a pair of identified business entities being represented by an edge of the graph, one of the nodes of the graph being designated as a source node, and one or more of the nodes of the graph being designated as sink nodes; andperform a series of decision processes to identify one of the business entities in the set represented by a sink node to execute the transaction with the business entity represented by the source node.

41. The machine-readable medium of claim 40, wherein the instructions to cause the machine to perform a series of decision processes comprises instructions to perform the series of decision processes recursively at the non-sink nodes of the graph, the non-sink nodes comprising the source node and one or more interior nodes.

42. The machine-readable medium of claim 41, wherein the instructions to cause the machine to perform a decision process of the series comprises instructions to compare bid prices associated with nodes directly coupled to the non-sink node at which the decision process is being performed.

43. The machine-readable medium of claim 40, wherein the instructions to cause the machine to perform a decision process of the series comprises instructions to compare at least two bid prices associated with nodes of the graph.

44. The machine-readable medium of claim 43, wherein the instructions to cause the machine to perform a decision process of the series comprises instructions to normalize the at least two bid prices in accordance with one or more pricing models prior to the comparison.

45. The machine-readable medium of claim 43, wherein the instructions to cause the machine to perform a decision process of the series comprises instructions to normalize the at least two bid prices in accordance with one or more predictive metrics associated with respective advertisement creatives prior to the comparison.

46. The machine-readable medium of claim 43, wherein the at least two bid prices are associated with nodes of the graph that are children of the node at which the decision process is being performed.

47. The machine-readable medium of claim 40, wherein the instructions to cause the machine to perform the series of decision processes comprises instructions to compare bids associated with nodes of the graph.

48. The machine-readable medium of claim 47, wherein the instructions to cause the machine to compare bids comprises instructions to compare bids based on one or more of the following metrics: a priority metric, a normalized price metric, an advertiser value metric, and a percentage delivered metric.

49. The machine-readable medium of claim 40, wherein the instructions to cause the machine to perform the series of decision processes comprises instructions to: perform a comparison of at least two bids associated with nodes of the graph based on a normalized price metric; andif the comparison yields a tie result, perform one or more additional comparisons of the at least two bids associated with nodes of the graph based on one or more of the following metrics: a priority metric, an advertiser value metric, and a percentage delivered metric, until a tie-breaking result is yielded.

50. The machine-readable medium of claim 40, wherein the instructions to cause the machine to perform the series of decision processes comprises instructions to: perform a comparison of at least two bids associated with nodes of the graph based on a normalized price metric; andif the comparison yields a tie result, randomly select one of the at least two bids to yield a result.

51. The machine-readable medium of claim 40, wherein the instructions to cause the machine to perform a series of decision processes comprises instructions to propagate information in a direction from the sink nodes to the source node, the information being related to a node that is a child of the node at which the decision process is being performed.

52. The machine-readable medium of claim 51, wherein the information comprises a bid price.

53. The machine-readable medium of claim 40, wherein the instructions to cause the machine to perform a series of decision processes comprises instructions to determine a payment amount to be paid by the business entity in the set identified to execute the transaction with the business entity represented by the source node.

54. The machine-readable medium of claim 53, wherein the payment amount to be paid is all or a portion of a bid price associated with the business entity in the set identified to execute the transaction.

55. The machine-readable medium of claim 40, wherein the instructions to cause the machine to perform a series of decision processes comprises instructions to: identify a first one of the business entities of the set associated with a first bid price that yields a highest revenue for the business entity represented by the source node;identify a second one of the business entities of the set associated with a second bid price that yields a second highest revenue for the business entity represented by the source node; andapply a dynamic pricing reduction rule to the first bid price based on the second bid price to determine a payment amount to be paid by the first one of the business entities of the set, the first one of the business entities being the business entity in the set identified to execute the transaction.

56. The machine-readable medium of claim 55, wherein the payment amount to be paid is the second bid price incremented by a predetermined margin.

57. The machine-readable medium of claim 55, wherein the predetermined margin is expressed as a percentage.

58. The machine-readable medium of claim 55, wherein the payment amount to be paid is greater than a bid price associated with any node within a subtree of the graph in which the first one of the business entities is located.

59. The machine-readable medium of claim 40, further comprising instructions to cause the machine to: receive information defining a relationship between a first business entity and a second business entity; andstore the received information.

60. The machine-readable medium of claim 59, wherein the received information comprises information related to a revenue sharing agreement.

61. The machine-readable medium of claim 40, further comprising instructions to cause the machine to: execute the transaction between the identified one of the business entities in the set represented by a sink node with the business entity represented by the source node.

62. The machine-readable medium of claim 61, wherein the instructions to cause the machine to execute the transaction comprises one or more of the following instructions: instructions to cause the machine to provide sufficient first information to each business entity represented by a node in a path between the source node and the sink node representing the identified one of the business entities in the set to effect the execution of the transaction;instructions to cause the machine to log sufficient second information to document the execution of the transaction; andinstructions to cause the machine to enable an advertisement creative associated with the business entity representing the identified one of the business entities in the set to be delivered directly or indirectly to the business entity representing the source node.