Systems, methods and articles of manufacture for determining relevancy of tax topics in a tax preparation system

• US 10,614,529 B1
• Filed: 04/12/2019
• Issued: 04/07/2020
• Est. Priority Date: 11/26/2014
• Status: Active Grant

• Current Assignee: Intuit, Inc.
• Original Assignee: Intuit, Inc.
• Inventors: Wang, Gang, McCluskey, Kevin M., Keenan, Jennifer, Tritt, Suzette
• Primary Examiner(s): Niquette, Robert R
• Assistant Examiner(s): Nguyen, Liz P
• Application Number:
  US16/382,855
• Time in Patent Office: 361 Days
• Field of Search: 705 31
• CPC Class Codes: G06Q 40/123 Tax preparation or submission

Abstract

Systems, methods and articles of manufacture for determining the relevancy of tax matters to a particular taxpayer and prioritizing the tax matters in a tax preparation system for preparing an electronic tax return. A computerized tax preparation system accesses taxpayer data and generates a taxpayer data profile using the taxpayer data. The system executes at tax logic agent which evaluates missing tax data required to prepare the tax return for the taxpayer and outputs a plurality of suggested tax matters for obtaining the missing tax data to a user interface manager. The tax logic agent utilizes the taxpayer data profile and a tax matter relevancy module having a plurality of tax data profiles to determine a relevancy ranking for each suggested tax matter. A user interface manager receives the suggested tax matters and the corresponding relevancy rankings and determines one or more tax questions to present to the user.

Claims

1. A computing system, comprising:

a shared data store; and

at least one processor in communication with the shared data store, the at least one processor being configured to;

access, by a logic agent of the at least one processor, the shared data store to determine user data for a user that is stored within the shared data store;

generate, by the logic agent, a user data profile using the user data;

evaluate, by the logic agent, missing data needed to prepare a form being completed by the user;

determine, by the logic agent, a plurality of suggested options for obtaining the missing data;

receive, by the logic agent, a plurality of data profiles and a plurality of logic based correlations from a relevancy module of the at least one processor, the data profiles including at least one of individual profiles and pre-defined profiles, wherein each individual profile includes a plurality of individual attributes and related data retrieved from the shared data store for a particular individual, each pre-defined profile includes a plurality of class attributes and related data retrieved from the shared data store for a class of individuals, and each logic based correlation defines a logical correlation between one or more class attributes and a related aspect;

analyze, by the logic agent, the user data profile in relation to the data profiles in the relevancy module to determine a relevancy ranking for each of the suggested options, wherein each relevancy ranking is related to a likelihood that the suggested option is relevant to the form being completed by the user;

compare, by a user interface controller of the at least one processor, the relevancy rankings corresponding to each of the plurality of suggested options;

select, by the user interface controller, a suggested option corresponding to the highest relevancy ranking from the comparison;

determine, by the user interface controller, one or more questions related to the selected suggested option to present to the user;

generate, by the user interface controller, an interactive interview screen comprising at least one of the one or more determined questions, the interactive interview screen being presentable through a display in communication with the at least one processor;

receive, by the user interface controller, user input through the interactive interview screen in response to the at least one determined question for obtaining the missing data; and

update, by the user interface controller, the shared data store with the received user input.

2. The computing system of claim 1, wherein the at least one processor is configured to analyze, by the logic agent, the user data profile in relation to the data profiles in the relevancy module to determine the relevancy ranking for each of the suggested options by a process including:

identifying a population set of data profiles that are similar to the user data profile;

computing a population probability of relevancy of each suggested option based on the population set; and

utilizing the population probability of each suggested option to compute a relevancy ranking of the respective option.

3. The computing system of claim 1, wherein the at least one processor is configured to analyze, by the logic agent, the user data profile in relation to the data profiles in the relevancy module to determine the relevancy ranking for each of the suggested options by a process including:

analyzing a population set of data profiles to identify at least one option presented to members of the class of individuals; and

determining an effectiveness of the at least one option presented to the members of the class of individuals.

4. The computing system of claim 1, wherein the at least one processor is further configured to:

update, by the logic agent, the user data profile and generate an updated user data profile based at least in part upon the updated data of the shared data store; and

analyze, by the logic agent, the updated user data profile in relation to the data profiles in the relevancy module to determine a second relevancy ranking for each of a second plurality of suggested options, wherein the second relevancy ranking is related to a likelihood that a suggested option of the second plurality of options is relevant to the form being completed by the user.

5. The computing system of claim 1, wherein each relevancy ranking is one of an estimate of the probability of relevance, a numerical ranking among the suggested options, or a ranking index.

6. The computing system of claim 1, wherein the at least one processor is configured to modify, by the user interface controller, at least one of a size of a text and an amount of the text within the interactive interview screen based on a form factor of the display.

7. The computing system of claim 1, wherein the at least one processor is configured to:

generate, by an estimation module of the at least one processor, an estimated value for an entry in the form being completed by the user; and

insert, by the estimation module, the estimated value into the form being completed by the user.

8. The computing system of claim 7, wherein the estimated value is generated based on the user data that is stored in the shared data store.

9. The computing system of claim 7, wherein the estimated value is generated based on external user data that is obtained from at least one data source separate from the computing system.

10. The computing system of claim 7, wherein:

the at least one processor is configured to determine, by the estimation module, a confidence level for the estimated value; and

the estimated value is inserted into the form being completed by the user in response to the confidence level being above a threshold level.

11. A computer-implemented method, comprising:

accessing, by a logic agent of at least one processor, a shared data store to determine user data for a user that is stored within the shared data store;

generating, by the logic agent, a user data profile using the user data;

evaluating, by the logic agent, missing data needed to prepare a form being completed by the user;

determining, by the logic agent, a plurality of suggested options for obtaining the missing data;

receiving, by the logic agent, a plurality of data profiles and a plurality of logic based correlations from a relevancy module of the at least one processor, the data profiles including at least one of individual profiles and pre-defined profiles, wherein each individual profile includes a plurality of individual attributes and related data retrieved from the shared data store for a particular individual, each pre-defined profile includes a plurality of class attributes and related data retrieved from the shared data store for a class of individuals, and each logic based correlation defines a logical correlation between one or more class attributes and a related aspect;

analyzing, by the logic agent, the user data profile in relation to the data profiles in the relevancy module to determine a relevancy ranking for each of the suggested options, wherein each relevancy ranking is related to a likelihood that the suggested option is relevant to the form being completed by the user;

comparing, by a user interface controller of the at least one processor, the relevancy rankings corresponding to each of the plurality of suggested options;

selecting, by the user interface controller, a suggested option corresponding to the highest relevancy ranking from the comparison;

determining, by the user interface controller, one or more questions related to the selected suggested option to present to the user;

generating, by the user interface controller, an interactive interview screen comprising at least one of the one or more determined questions, the interactive interview screen being presentable through a display in communication with the at least one processor;

receiving, by the user interface controller, user input through the interactive interview screen in response to the at least one determined question for obtaining the missing data; and

updating, by the user interface controller, the shared data store with the received user input.

12. The computer-implemented method of claim 11, further comprising analyzing, by the logic agent, the user data profile in relation to the data profiles in the relevancy module to determine the relevancy ranking for each of the suggested options by a process including:

identifying a population set of data profiles that are similar to the user data profile;

computing a population probability of relevancy of each suggested option based on the population set; and

utilizing the population probability of each suggested option to compute a relevancy ranking of the respective option.

13. The computer-implemented method of claim 11, further comprising analyzing, by the logic agent, the user data profile in relation to the data profiles in the relevancy module to determine the relevancy ranking for each of the suggested options by a process including:

analyzing a population set of data profiles to identify at least one option presented to members of the class of individuals; and

determining an effectiveness of the at least one option presented to the members of the class of individuals.

14. The computer-implemented method of claim 11, further comprising:

updating, by the logic agent, the user data profile and generate an updated user data profile based at least in part upon the updated data of the shared data store; and

analyzing, by the logic agent, the updated user data profile in relation to the data profiles in the relevancy module to determine a second relevancy ranking for each of a second plurality of suggested options, wherein the second relevancy ranking is related to a likelihood that a suggested option of the second plurality of options is relevant to the form being completed by the user.

15. The computer-implemented method of claim 11, wherein each relevancy ranking is one of an estimate of the probability of relevance, a numerical ranking among the suggested options, or a ranking index.

16. The computer-implemented method of claim 11, further comprising modifying, by the user interface controller, at least one of a size of a text and an amount of the text within the interactive interview screen based on a form factor of the display.

17. The computer-implemented method of claim 11, further comprising:

generating, by an estimation module of the at least one processor, an estimated value for an entry in the form being completed by the user; and

inserting, by the estimation module, the estimated value into the form being completed by the user.

18. The computer-implemented method of claim 17, wherein the estimated value is generated based on the user data that is stored in the shared data store.

19. The computer-implemented method of claim 17, wherein the estimated value is generated based on external user data that is obtained from at least one data source separate from the computing system.

20. The computer-implemented method of claim 17, further comprising determining, by the estimation module, a confidence level for the estimated value, wherein the estimated value is inserted into the form being completed by the user in response to the confidence level being above a threshold level.