OPTIMIZATION OF OUTPUT DATA ASSOCIATED WITH A POPULATION

• US 20120226690A1
• Filed: 03/01/2011
• Published: 09/06/2012
• Est. Priority Date: 03/01/2011
• Status: Active Grant

• Current Assignee: Bank of America Corp.
• Original Assignee: Bank of America Corp.
• Inventors: Kheyfets, Boris L.
• Granted Patent: US 8,620,887 B2
• Time in Patent Office: Days
• US Class Current: 707/737
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 40/12 Accounting

Abstract

Embodiments of the present invention relate to systems, methods and computer program products for allowing a user to define output data to be optimized for a population, define one or more input constraints based on which the output data is to be optimized, and optimizing the output data for the population by selecting an object based on a selection routine. Embodiments of the present invention allow fast and accurate optimization of output data associated with large populations.

Claims

1. A method for optimizing output data for a population, the method comprising:

allowing a user to define, via a computing device processor, the output data to be optimized;

allowing a user to define, via a computing device processor, one or more input constraints based on which the output data is to be optimized;

optimizing, via a computing device processor, subject to the input constraints, the output data for the population by;

partitioning, via a computing device processor, the population into a first number of partitions;

optimizing, via a computing device processor, output data for each partition by;

selecting, via a computing device processor, an object from each partition based on a first selection routine; and

generating, via a computing device processor, optimized output data for the population.

2. The method of claim 1, wherein the first selection routine comprises sorting, via a computing device processor, each partition based at least in part on one or more parameters associated with objects in each partition.

3. The method of claim 1, wherein objects in each partition are similar.

4. The method of claim 1, further comprising:

partitioning, via a computing device processor, the population into a second number of partitions; and

optimizing, via a computing device processor, output data for each partition by;

selecting, via a computing device processor, an object from each partition based on a second selection routine;

wherein the second number of partitions is greater than the first number of partitions.

5. The method of claim 4, wherein the second selection routine comprises sorting, via a computing device processor, each partition based at least in part on one or more parameters associated with objects in each partition, and based at least in part on the optimized output data for the population.

6. The method of claim 1, wherein the output data for the population is defined as a function that comprises at least one variable.

7. The method of claim 6, wherein the value of the variable is ‘

1’

for a selected object, and wherein the value of the variable is ‘

0’

for an unselected object.

8. The method of claim 1, wherein the output data for a partition is defined as a function that comprises at least one variable.

9. The method of claim 8, wherein the value of the variable is greater than ‘

0’

but less than or equal to ‘

1’

for a selected partition, and wherein the value of the variable is ‘

0’

for an unselected partition.

10. The method of claim 1, wherein the output data for the population is income collected from accounts opened by one or more selected objects.

11. The method of claim 1, wherein the population comprises at least 50,000 objects.

12. The method of claim 1, wherein the population comprises at least 10,000,000 objects.

13. The method of claim 4, further comprising:

producing, via a computing device processor, a communication file comprising communication details for selected objects.

14. A computer program product for optimizing output data for a population, the computer program product comprising:

a non-transitory computer-readable medium comprising a set of codes for causing a computer to;

allow a user to define, via a computing device processor, one or more output data for optimization;

allow a user to define, via a computing device processor, one or more input constraints based on which the output data for the population is to be optimized;

optimize, via a computing device processor, subject to the input constraints, the output data for the population by;

partitioning, via a computing device processor, the population into a first number of partitions;

optimizing, via a computing device processor, output data for each partition by;

selecting, via a computing device processor, an object from each partition based on a first selection routine; and

generate, via a computing device processor, optimized output data for the population.

15. The computer program product of claim 14, wherein the first selection routine comprises sorting, via a computing device processor, each partition based at least in part on one or more parameters associated with objects in each partition.

16. The computer program product of claim 14, wherein objects in each partition are similar.

17. The computer program product of claim 14, wherein the set of codes further causes a computer to:

partition, via a computing device processor, the population into a second number of partitions; and

optimize, via a computing device processor, output data for each partition by;

selecting, via a computing device processor, an object from each partition based on a second selection routine;

wherein the second number of partitions is greater than the first number of partitions.

18. The computer program product of claim 17, wherein the second selection routine comprises sorting, via a computing device processor, each partition based at least in part on one or more parameters associated with objects in each partition, and based at least in part on the optimized output data for the population.

19. The computer program product of claim 14, wherein the set of codes further causes a computer to define the output data for the population as a function that comprises at least one variable.

20. The computer program product of claim 19, wherein the value of the variable is ‘

1’

for a selected object, and wherein the value of the variable is ‘

0’

for an unselected object.

21. The computer program product of claim 14, wherein the set of codes further causes a computer to define the output data for a partition as a function that comprises at least one variable.

22. The computer program product of claim 21, wherein the value of the variable is greater than ‘

0’

but less than or equal to ‘

1’

for a selected partition, and wherein the value of the variable is ‘

0’

for an unselected partition.

23. The computer program product of claim 14, wherein the output data for the population is income collected from accounts opened by one or more selected objects.

24. The computer program product of claim 14, wherein the population comprises at least 50,000 objects.

25. The computer program product of claim 14, wherein the population comprises at least 10,000,000 objects.

26. The computer program product of claim 17, wherein the set of codes further causes a computer to:

produce, via a computing device processor, a communication file comprising communication details for selected objects.

27. An apparatus for optimizing output data for a population, the apparatus comprising:

a computing platform including at least one computing device processor and a memory;

a module stored in the memory, executable by a computing device processor, and configured to;

allow a user to define, via a computing device processor, one or more output data for optimization;

allow a user to define, via a computing device processor, one or more input constraints based on which the output data for the population is to be optimized;

optimize, via a computing device processor, subject to the input constraints, the output data for the population by;

partitioning, via a computing device processor, the population into a first number of partitions;

optimizing, via a computing device processor, output data for each partition by;

selecting, via a computing device processor, an object from each partition based on a first selection routine; and

generate, via a computing device processor, optimized output data for the population.

28. The apparatus of claim 27, wherein the first selection routine comprises sorting, via a computing device processor, each partition based at least in part on one or more parameters associated with objects in each partition.

29. The apparatus of claim 27, wherein objects in each partition are similar.

30. The apparatus of claim 27, wherein the module is further configured to:

partition, via a computing device processor, the population into a second number of partitions; and

optimize, via a computing device processor, output data for each partition by;

selecting, via a computing device processor, an object from each partition based on a second selection routine;

wherein the second number of partitions is greater than the first number of partitions.

31. The apparatus of claim 30, wherein the second selection routine comprises sorting, via a computing device processor, each partition based at least in part on one or more parameters associated with objects in each partition, and based at least in part on the optimized output data for the population.

32. The apparatus of claim 27, wherein the module is configured to define the output data for the population as a function that comprises at least one variable.

33. The apparatus of claim 32, wherein the value of the variable is ‘

1’

for a selected object, and wherein the value of the variable is ‘

0’

for an unselected object.

34. The apparatus of claim 27, wherein the module is configured to define the output data for a partition as a function that comprises at least one variable.

35. The apparatus of claim 34, wherein the value of the variable is greater than ‘

0’

but less than or equal to ‘

1’

for a selected partition, and wherein the value of the variable is ‘

0’

for an unselected partition.

36. The apparatus of claim 27, wherein the output data for the population is income collected from accounts opened by one or more selected objects.

37. The apparatus of claim 27, wherein the population comprises at least 50,000 objects.

38. The apparatus of claim 27, wherein the population comprises at least 10,000,000 objects.

39. The apparatus of claim 30, wherein the module is further configured to:

produce, via a computing device processor, a communication file comprising communication details for selected objects.

40. A method for optimizing output data for a population, the method comprising:

allowing a user to define, via a computing device processor, one or more output data for optimization;

allowing a user to define, via a computing device processor, one or more input constraints based on which the output data is to be optimized; and

optimizing, via a computing device processor, subject to the input constraints, the output data for the population by;

selecting an object of the population based on a selection routine;

wherein the population comprises at least 10,000,000 objects.

41. A method for optimizing output data for a population, the method comprising:

allowing a user to define, via a computing device processor, one or more output data for optimization;

allowing a user to define, via a computing device processor, one or more input constraints based on which the output data is to be optimized; and

optimizing, via a computing device processor, subject to the input constraints, the output data for the population by;

selecting an object of the population based on a selection routine;

wherein the input constraints comprise;

a maximum risk factor associated with an object of the population;

a minimum probability factor associated with an object of the population opening an account; and

a maximum cost factor associated with communicating to an object of the population an offer to open an account.