METHOD AND SYSTEM FOR EVALUATING INTERCHANGEABLE ANALYTICS MODULES USED TO PROVIDE CUSTOMIZED TAX RETURN PREPARATION INTERVIEWS

• US 20160180470A1
• Filed: 12/23/2014
• Published: 06/23/2016
• Est. Priority Date: 12/23/2014
• Status: Abandoned Application

• Current Assignee: Intuit, Inc.
• Original Assignee: Intuit, Inc.
• Inventors: Mascaro, Massimo, Goldman, Jonathan R., Cabrera, Luis Felipe, Laaser, William T.
• Application Number:
  US14/580,416
• Publication Number:
  US 20160180470A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 40/123 Tax preparation or submission

Abstract

A method and system evaluates analytics modules to improve a personalization of tax questions delivered to a user in a tax return preparation system, according to one embodiment. The method and system retrieves historical tax return data and selects one or more interchangeable analytics modules for evaluation with the historical tax return data, according to one embodiment. The method and system applies the historical tax return data to the one or more analytics modules that are selected for evaluation, according to one embodiment. The method and system receives analytics outputs from the one or more analytics modules, in response to applying the historical tax return data, according to one embodiment. The method and system determines an effectiveness of each of the one or more analytics modules by correlating the analytics outputs with at least part of the historical tax return data, according to one embodiment.

Claims

1. A computing system implemented method for evaluating analytics modules to improve a personalization of tax questions delivered to a user in a tax return preparation system, comprising:

retrieving, with a computing system, historical tax return data;

selecting one or more analytics modules for evaluation with the historical tax return data,

wherein each of the one or more analytics modules are interchangeably pluggable into the tax return preparation system;

applying the historical tax return data to the one or more analytics modules that are selected for evaluation;

receiving analytics outputs from the one or more analytics modules, in response to applying the historical tax return data; and

determining an effectiveness of each of the one or more analytics modules by correlating the analytics outputs with at least part of the historical tax return data.

2. The method of claim 1, further comprising:

sorting the one or more analytics modules based on the effectiveness of each of the one or more analytics modules; and

providing, for use within the tax return preparation system, one of the one or more analytics modules having a highest effectiveness.

3. The method of claim 1, wherein the effectiveness of each of the one or more analytics modules is associated with a numerical effectiveness score.

4. The method of claim 1, wherein the historical tax return data includes one or more of:

data indicating the user'"'"'s name;

data indicating the user'"'"'s Social Security Number;

data indicating the user'"'"'s government identification;

data indicating the user'"'"'s a driver'"'"'s license number;

data indicating the user'"'"'s date of birth;

data indicating the user'"'"'s address;

data indicating the user'"'"'s zip code;

data indicating the user'"'"'s home ownership status;

data indicating the user'"'"'s marital status;

data indicating the user'"'"'s annual income;

data indicating the user'"'"'s job title;

data indicating the user'"'"'s employer'"'"'s address;

data indicating the user'"'"'s spousal information;

data indicating the user'"'"'s children'"'"'s information;

data indicating the user'"'"'s assets;

data indicating the user'"'"'s medical history;

data indicating the user'"'"'s occupation;

data indicating the user'"'"'s website browsing preferences;

data indicating the user'"'"'s typical lingering duration on a website;

data indicating the user'"'"'s dependents;

data indicating the user'"'"'s salary and wages;

data indicating the user'"'"'s interest income;

data indicating the user'"'"'s dividend income;

data indicating the user'"'"'s business income;

data indicating the user'"'"'s farm income;

data indicating the user'"'"'s capital gain income;

data indicating the user'"'"'s pension income;

data indicating the user'"'"'s IRA distributions;

data indicating the user'"'"'s unemployment compensation;

data indicating the user'"'"'s educator expenses;

data indicating the user'"'"'s health savings account deductions;

data indicating the user'"'"'s moving expenses;

data indicating the user'"'"'s IRA deductions;

data indicating the user'"'"'s student loan interest deductions;

data indicating the user'"'"'s tuition and fees;

data indicating the user'"'"'s medical and dental expenses;

data indicating the user'"'"'s state and local taxes;

data indicating the user'"'"'s real estate taxes;

data indicating the user'"'"'s personal property tax;

data indicating the user'"'"'s mortgage interest;

data indicating the user'"'"'s charitable contributions;

data indicating the user'"'"'s casualty and theft losses;

data indicating the user'"'"'s unreimbursed employee expenses;

data indicating the user'"'"'s alternative minimum tax;

data indicating the user'"'"'s foreign tax credit;

data indicating the user'"'"'s education tax credits;

data indicating the user'"'"'s retirement savings contribution;

data indicating the user'"'"'s child tax credits;

data indicating the user'"'"'s residential energy credits;

data from the user'"'"'s 1099 form; and

data from the user'"'"'s K-1 form.

5. The method of claim 1, further comprising:

receiving, with the computing system, user data from the user through a user interface; and

applying a selected one of the one or more analytics modules to the user data to determine a relevance of tax questions to the user,

wherein the selected one of the one or more analytics modules includes a higher effectiveness than another of the one or more analytics modules.

6. The method of claim 1, wherein selecting one or more analytics modules for evaluation includes selecting two analytics modules that are configured to perform a particular function, with two different techniques, to determine which of the two analytics modules more accurately prioritizes tax questions for the user.

7. The method of claim 1, wherein the historical tax return data includes tax return data for other users from a present tax year.

8. The method of claim 1, wherein the historical tax return data includes tax return data for other users from one or more previous tax years.

9. The method of claim 1, wherein the historical tax return data includes synthetic data that has been prepared for the evaluation of the one or more analytics modules.

10. The method of claim 1, wherein the one or more analytics modules are configured to prioritize tax questions and tax topics based on user data.

11. The method of claim 1, wherein the tax return preparation system applies at least some of the one or more analytics modules to user data received from the user during a tax return preparation interview.

12. The method of claim 1, wherein applying the historical tax return data includes applying a sample of the historical tax return data to the one or more analytics modules, to limit evaluation time for the one or more analytics modules.

13. The method of claim 1, wherein applying the historical tax return data to the one or more analytics modules includes applying one or more specific parameters of the historical tax return data to determine the analytics outputs for a particular tax question.

14. The method of claim 1, wherein the historical tax return data includes inflation adjustments so that the historical tax return data corresponds to present currency values.

15. The method of claim 1, wherein each of the one or more analytics modules includes at least one of an algorithm, a predictive model, and a statistical engine.

16. The method of claim 1, further comprising:

training one or more of the analytics modules based at least partially on the determined effectiveness of each of the analytics modules.

17. A computer-readable medium having a plurality of computer-executable instructions which, when executed by a processor, perform a method for evaluating interchangeable analytics modules to improve a personalization of tax questions delivered to a user in a tax return preparation system, the instructions comprising:

a data structure storing historical tax return data;

one or more interchangeable analytics modules,

wherein each of the one or more interchangeable analytics modules is configured to apply a data evaluation model to tax return data to generate an analytics output,

wherein the analytics output is associated with prioritizing tax questions for a tax return preparation interview; and

an analytics module evaluation engine configured to apply the one or more interchangeable analytics modules to the historical tax return data to generate analytics outputs,

wherein the analytics module evaluation engine compares the analytics outputs to the historical tax return data to determine a quantity of correlation between the analytics outputs and the historical tax return data,

wherein a higher correlation between one of the analytics outputs and the historical tax return data is associated with a higher predictive accuracy,

wherein the analytics module evaluation engine prioritizes the one or more interchangeable analytics modules based on the quantity of correlation between the analytics outputs and the historical tax return data.

18. The computer-readable medium of claim 17, wherein the instructions further comprise an analytics module configured to receive a recommendation for one of the interchangeable analytics modules from the analytics module evaluation engine, at least partially based on prioritizations of the one or more interchangeable analytics modules by the analytics module evaluation engine.

19. The computer-readable medium of claim 17, wherein the historical tax return data includes tax return data for other users from a present tax year.

20. The computer-readable medium of claim 17, wherein the historical tax return data includes tax return data for other users from one or more previous tax years.

21. The computer-readable medium of claim 17, wherein the analytics module evaluation engine is configured to apply the one or more interchangeable analytics modules to one or more specific parameters of the historical tax return data to determine the analytics outputs for a particular tax question.

22. The computer-readable medium of claim 17, wherein each of the one or more interchangeable analytics modules includes at least one of an algorithm, a predictive model, and a statistical engine.

23. A system for evaluating analytics modules to improve a personalization of tax questions delivered to a user in a tax return preparation system, the system comprising:

at least one processor; and

at least one memory coupled to the at least one processor, the at least one memory having stored therein instructions which, when executed by any set of the one or more processors, perform a process for evaluating analytics modules to improve a personalization of tax questions delivered to a user in a tax return preparation system, the process including;

retrieving, with a computing system, historical tax return data;

selecting one or more analytics modules for evaluation with the historical tax return data,

wherein each of the one or more analytics modules are interchangeably pluggable into the tax return preparation system;

applying the historical tax return data to the one or more analytics modules that are selected for evaluation;

receiving analytics outputs from the one or more analytics modules, in response to applying the historical tax return data; and

determining an effectiveness of each of the one or more analytics modules by correlating the analytics outputs with at least part of the historical tax return data.

24. The system of claim 23, wherein the process further comprises:

sorting the one or more analytics modules based on the effectiveness of each of the one or more analytics modules; and

providing, for use within the tax return preparation system, one of the one or more analytics modules having a highest effectiveness.

25. The system of claim 23, wherein the effectiveness of each of the one or more analytics modules is associated with a numerical effectiveness score.

26. The system of claim 23, wherein the historical tax return data includes one or more of:

data indicating the user'"'"'s name;

data indicating the user'"'"'s Social Security Number;

data indicating the user'"'"'s government identification;

data indicating the user'"'"'s a driver'"'"'s license number;

data indicating the user'"'"'s date of birth;

data indicating the user'"'"'s address;

data indicating the user'"'"'s zip code;

data indicating the user'"'"'s home ownership status;

data indicating the user'"'"'s marital status;

data indicating the user'"'"'s annual income;

data indicating the user'"'"'s job title;

data indicating the user'"'"'s employer'"'"'s address;

data indicating the user'"'"'s spousal information;

data indicating the user'"'"'s children'"'"'s information;

data indicating the user'"'"'s assets;

data indicating the user'"'"'s medical history;

data indicating the user'"'"'s occupation;

data indicating the user'"'"'s website browsing preferences;

data indicating the user'"'"'s typical lingering duration on a website;

data indicating the user'"'"'s dependents;

data indicating the user'"'"'s salary and wages;

data indicating the user'"'"'s interest income;

data indicating the user'"'"'s dividend income;

data indicating the user'"'"'s business income;

data indicating the user'"'"'s farm income;

data indicating the user'"'"'s capital gain income;

data indicating the user'"'"'s pension income;

data indicating the user'"'"'s IRA distributions;

data indicating the user'"'"'s unemployment compensation;

data indicating the user'"'"'s educator expenses;

data indicating the user'"'"'s health savings account deductions;

data indicating the user'"'"'s moving expenses;

data indicating the user'"'"'s IRA deductions;

data indicating the user'"'"'s student loan interest deductions;

data indicating the user'"'"'s tuition and fees;

data indicating the user'"'"'s medical and dental expenses;

data indicating the user'"'"'s state and local taxes;

data indicating the user'"'"'s real estate taxes;

data indicating the user'"'"'s personal property tax;

data indicating the user'"'"'s mortgage interest;

data indicating the user'"'"'s charitable contributions;

data indicating the user'"'"'s casualty and theft losses;

data indicating the user'"'"'s unreimbursed employee expenses;

data indicating the user'"'"'s alternative minimum tax;

data indicating the user'"'"'s foreign tax credit;

data indicating the user'"'"'s education tax credits;

data indicating the user'"'"'s retirement savings contribution;

data indicating the user'"'"'s child tax credits;

data indicating the user'"'"'s residential energy credits;

data from the user'"'"'s 1099 form; and

data from the user'"'"'s K-1 form.

27. The system of claim 23, wherein the process further comprises:

receiving, with the computing system, user data from the user through a user interface; and

applying a selected one of the one or more analytics modules to the user data to determine a relevance of tax questions to the user,

wherein the selected one of the one or more analytics modules includes a higher effectiveness than another of the one or more analytics modules.

28. The system of claim 23, wherein selecting one or more analytics modules for evaluation includes selecting two analytics modules that are configured to perform a particular function, with two different techniques, to determine which of the two analytics modules more accurately prioritizes tax questions for the user.

29. The system of claim 23, wherein the historical tax return data includes tax return data for other users from a present tax year.

30. The system of claim 23, wherein the historical tax return data includes tax return data for other users from one or more previous tax years.

31. The system of claim 23, wherein the historical tax return data includes synthetic data that has been prepared for the evaluation of the one or more analytics modules.

32. The system of claim 23, wherein the one or more analytics modules are configured to prioritize tax questions and tax topics based on user data.

33. The system of claim 23, wherein the tax return preparation system applies at least some of the one or more analytics modules to user data received from the user during a tax return preparation interview.

34. The system of claim 23, wherein applying the historical tax return data includes applying a sample of the historical tax return data to the one or more analytics modules, to limit evaluation time for the one or more analytics modules.

35. The system of claim 23, wherein applying the historical tax return data to the one or more analytics modules includes applying one or more specific parameters of the historical tax return data to determine the analytics outputs for a particular tax question.

36. The system of claim 23, wherein the historical tax return data includes inflation adjustments so that the historical tax return data corresponds to present currency values.

37. The system of claim 23, wherein each of the one or more analytics modules includes at least one of an algorithm, a predictive model, and a statistical engine.

38. The system of claim 23, wherein the process further comprises:

training one or more of the analytics modules based at least partially on the determined effectiveness of each of the analytics modules.