Patent Publication Number: US-11379930-B1

Title: System and method for targeted data gathering for tax preparation

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a U.S. Continuation of U.S. application Ser. No. 14/673,646 filed Mar. 30, 2015. The entirety of all the above-listed applications are incorporated herein by reference. 
    
    
     SUMMARY 
     In one embodiment, tax preparation software that runs on a computing device operates on a construct in which tax rules and the calculations based thereon are established in declarative data-structures, namely, completeness graph(s) and tax calculation graph(s). Use of these declarative data-structures permits the user interface to be loosely connected or even divorced from the tax calculation engine and the data used in the tax calculations. For example, the user interface can be designed to minimize the number interview questions that are asked to the user during the interview gathering process. The user interface can also be designed to be tailored to the particular taxpayer utilizing the tax preparation software. For example, past or prior year tax data may be used by the tax preparation software to selectively present certain tax topics that are relevant to the tax payer. In a related aspect, the tax preparation software may also make educated guesses or conjecture about relevant tax topics based on one or more characteristics that pertain to the taxpayer. These may include, by way of example, occupation, marital status, geographic location, filing status, and the like. 
     In one embodiment, at least some of the tax data pertaining to a user of the tax preparation software is automatically acquired by a data capture utility executed by the computing device. The data capture utility connects to one or more remotely located data sources and captures and transfers at least some tax data to a data store configured to store user-specific tax data. The data capture utility is able to automatically gather and collect tax related information that is needed by the tax preparation software to compute a tax liability or refund and ultimately complete a fileable tax return. The data capture utility thus significantly reduces the amount of manually entered tax data that is required to be input into the tax preparation software. The data capture utility speeds the process of preparing a tax return by automatically collecting and then storing in a data store or repository tax data that is used by the tax preparation software. 
     In one aspect of the invention, the data capture utility selectively targets a subset of the total universe of remotely located data sources. For example, the data capture utility may select the top several remotely located data sources that are statistically relevant to one or more characteristics of the taxpayer. For example, statistical data of other taxpayers sharing a common characteristic may indicate that certain remotely located data sources are likely to contain relevant information. For example, if a taxpayer lists his or her occupation as a truck driver, there is a statistically significant likelihood that the taxpayer may operate the truck as an owner operator. The tax preparation software is able to then automatically select, for example, Department of Motor Vehicle database records (or other equivalent agency) to obtain information on the make/model, and purchase date of the vehicle. 
     For tax data that is not automatically collected by the data capture utility, a user interface manager associated with the tax preparation software prompts the user for entry of additional tax data. The additional tax data may include any remaining tax data items that were not already stored in the data store by the data capture utility. The additional tax data may also be other tax data that is associated with or typically entered by other different taxpayers sharing one or more characteristics with the user of the tax preparation software program. In another aspect, tax data may be estimated by the use of an estimation module that is executed by the tax preparation software. Estimated values may be arrived at using as inputs data obtained by the data capture utility. 
     Tax calculations can be dynamically calculated based on tax-related data that is collected from the data capture utility, input from a user, or estimated by the tax preparation software. A smart tax logic agent running on a set of rules can review current run time data and evaluate missing tax data necessary to prepare and complete a tax return. The tax logic agent proposes suggested questions (or declarative statements) to be asked to a user to fill in missing blanks. This process can be continued until completeness of all tax topics has occurred. A completed tax return (e.g., a printed tax return or an electronic tax return) can then be prepared and filed with respect to the relevant taxing jurisdictions. 
     In another embodiment, a computer-implemented method for capturing tax-related data for use with tax preparation software is described. The method includes a computing device connecting to one or more remotely located data sources and executing a data capture utility, the data capture utility capturing and transferring at least some tax data pertaining to the user to a data store associated with the tax preparation software, the data store configured to store user-specific tax data therein. The computing device executes a user interface manager associated with the tax preparation software to prompt the user for entry of user-specific tax data not already stored in the data store by the data capture utility. The computing device executes a tax calculation engine of the tax preparation software configured to read the user-specific tax data obtained from the one or more remotely located data sources and the interface manager and compute an intermediate or final tax liability or refund amount. 
     In one embodiment, the one or more remotely located data sources may comprise a data source associated with a financial management program. Examples of financial management software includes, for example products or systems that are available from Intuit Inc. (e.g., MINT, QUICKEN, QUICKBOOKS and FINANCEWORKS). Financial management software allow consumers or business owners to combine, process, and categorize financial data and present numerical data in various forms or summaries such as balances or values, charts, graphs and reports. MINT, QUICKEN, QUICKBOOKS and FINANCEWORKS are registered trademarks of Intuit Inc., Mountain View, Calif. 
     In another embodiment, the one or more remotely located data sources may include user accounts for online financial service account(s) or online accessible social media accounts. The one or more remotely located data sources may also include employers, credit reporting bureaus, government agencies (e.g., court records, real property databases, motor vehicle databases, medical databases, or taxing authorities). 
     In one aspect, the data capture utility may include a screen scraping utility or a web crawler program. The data capture utility may ask the user for his or her credentials to a financial management software program that is, in turn, liked to various financial services accounts. The data capture utility may also ask the user for his or her credentials to financial management software accounts, online financial service accounts, online accessible social media accounts, credit bureaus, government agencies, or third party data provider (e.g., LEXIS/NEXIS). Alternatively or in addition to, the data capture utility may attempt to contact one or more remotely data sources without using any user provided credentials. For example, there may be some publicly available information in various online resources that may contain personal or tax data that can be captured and transferred by the data capture utility to the data store. The data capture utility may also connect to one or more locally located data sources. Such locally located data sources may include files or data associated with financial management software that is running locally on a computing device. Locally locate data sources may also include locally stored documents or even images. For example, PDF or image files of tax forms W-2s, 1099s, prior year tax returns, and the like may be interrogated by the data capture utility with relevant personal financial information extracted therefrom. The data capture utility may perform optical character recognition (OCR) on the data prior to transfer to the data store. 
     In one aspect of the invention, the computer-implemented method first utilizes tax preparation software to identify one or more tax topics that may be relevant to the taxpayer utilizing the tax preparation software. The tax topic may be identified based on prior tax return history (e.g., frequently used tax topics) that is particularized for the taxpayer. Alternatively, the one or more tax topics may be identified from other tax return data from different taxpayers. These topics may be extracted based on one or more shared characteristics (or combination of characteristics) between the taxpayer utilizing the tax preparation software and characteristics of taxpayers having stored tax return data. Tax topics may also be identified based on transactions that are entered in a financial management program (running either locally or remotely) or transactions contained in financial related online resources (e.g., bank or brokerage accounts). 
     Once the need information is contained within the data store, the computing device may execute a services engine that is configured to prepare a tax return for filing with relevant tax authorities. The tax return may be an electronic tax return or it may be a conventional paper tax return that is printed out by the user. 
     In another embodiment, a computer-implemented system for capturing user-related tax data for use with tax preparation software is described. The system includes a computing device operably coupled to a data store configured to store user-specific tax data therein. In one aspect, the tax preparation software may first look for any prior tax return data. This prior return data may be stored locally or remotely. By analyzing prior tax return data, common or recurring tax topics can be identified and flagged by the tax preparation software. For example, the software may flag that the taxpayer files Schedule D (capital gains and losses) every year. This means that the taxpayer will either be asked questions (or presented with a declarative statement) on this topic or, alternatively, one or more remote databases will automatically be searched to obtain information needed for Schedule D. The user interface manager is executed and may identify, for example, this flag and present to the user on or more questions regarding investments. 
     Alternatively, or in addition to, a data capture utility is executed by the computing device and configured to connect to one or more remotely located data sources, the data capture utility capturing and transferring at least some tax data pertaining to the user to the data store (e.g., in this case investment information regarding, for example, the sale of securities). The user interface manager is executed by a computing device that prompts the user for entry of user-specific tax data not already stored in the data store by the data capture utility or prompts the user to confirm certain data that was found automatically. 
     The system includes a tax calculation engine executed by the computing device and configured to read the user-specific tax data obtained from the one or more remotely located data sources and the interface manager and compute an intermediate or final tax liability or refund amount. The computing device may include a local device that has its own user interface or the computing device may be a separate, remotely located computing device. In the later example, the tax preparation software may be run, at least partially, in a cloud environment or software as service model where the user interface is local but the computational and data capture and gathering functionality is run on another computing device. 
     In another embodiment, a computer-implemented method for gathering user-related tax data for use with tax preparation software includes a computing device executing a data capture utility configured to connect to one or more remotely located data sources that relate to a particular topic that has been identified by the tax preparation software, wherein the data capture utility captures user-specific tax data from the one or more remotely located data sources and stores the captured data in a data store. The one or more remotely located data sources may optionally include a mix of paid and non-paid data sources. Alternatively, the one or more remotely located data sources may include non-paid data sources, e.g., financial and social media accounts liked to a specific user. A user interface manager may be executed by the tax preparation software that presents to the user a user interface to confirm data that has been obtained from the one or more remotely located data sources. Alternatively, the user interface manager may display one or more interview screens to the user to supplement or fill in the blanks that was not already obtained by the data capture utility. 
     The computing device executes a tax calculation engine of the tax preparation software configured to read the user-specific tax data obtained from the one or more remotely located data sources and the interface manager and compute an intermediate or final tax liability or refund amount. 
     In another embodiment, a computer-implemented method for gathering user-related tax data for use with tax preparation software includes a computing device executing a user interface manager that asks the user of the tax preparation software one or more preliminary questions that establish a tax characteristic profile. This tax characteristic profile of the user is then compared with a database containing tax characteristic profiles of other users. The tax characteristic profile correlates particular tax topics for a particular user. For example, tax characteristic profile “A” may indicate that the user typically is self-employed and files a Schedule C and deducts an automobile and a home office. Based on this profile, the user interface manager may select interview topics from these high probability topics. In this case, the user interface manager may ask the user whether he or she is self-employed and additional details regarding their business. Likewise the user interface manager may further ask about the possible home office deduction and automobile deduction. 
     In one aspect, the computer executes a data capture utility configured to connect to one or more remotely located data sources that relate to the particular topic that has been identified tax characteristic profile, wherein the data capture utility captures user-specific tax data from the one or more remotely located data sources and stores the captured data in a data store. This data may be obtained instead of manually entered data or this data may be obtained to confirm and/or verify data. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  schematically illustrates according to one embodiment how tax legislation/tax rules is parsed and represented by a completeness graph and a tax calculation graph. 
         FIG. 2  illustrates an example of a simplified version of a completeness graph according to one embodiment related to a qualifying child for purposes of determining deductions for federal income tax purposes. 
         FIG. 3  illustrates another illustration of a completeness graph according to one embodiment. 
         FIG. 4  illustrates a decision table based on or derived from the completeness graph of  FIG. 3 . 
         FIG. 5  illustrates another embodiment of a decision table that incorporates statistical data. 
         FIG. 6  illustrates an example of a calculation graph according to one embodiment. 
         FIG. 7  schematically illustrates a system according to one embodiment for calculating taxes using rules and calculations based on a declarative data structures. 
         FIG. 8A  schematically illustrates a system according to another embodiment for calculating taxes using rules and calculations based on a declarative data structures. 
         FIG. 8B  schematically illustrates a system according to another embodiment for calculating taxes using rules and calculations based on a declarative data structures. In this embodiment, a topic analysis engine is used to aid in generating interview questions for highly relevant tax topics or automatically obtaining highly relevant information from one or more online resources. 
         FIG. 9  illustrates a table of estimated tax values and associate attributes according to one embodiment. 
         FIG. 10  illustrates a computing device with an illustrative user interface presentation that incorporates the attribute rules to arrive a confidence level for tax calculations according to one embodiment. 
         FIG. 11  illustrates a computing device with another illustrative user interface presentation that incorporates the attribute rules to arrive a confidence level for tax calculations according to another embodiment. 
         FIG. 12  illustrates a flowchart of operations used in connection with a method of calculating tax liability according to one embodiment. 
         FIG. 13  illustrates the implementation of tax preparation software on various computing devices according to one embodiment. 
         FIG. 14  schematically illustrates a process whereby a combination of user inputs, sourced data, and estimates are used in connection with a tax calculation according to one embodiment. 
         FIG. 15  schematically illustrates a system for capturing user-related tax data for use with tax preparation software according to one embodiment. 
         FIG. 16  schematically illustrates a system for capturing user-related tax data for use with tax preparation software according to another embodiment. 
         FIG. 17  illustrates the operations performed using a secondary data capture utility according to one alternative embodiment. 
         FIG. 18  schematically illustrates a system for capturing user-related tax data for use with tax preparation software according to another embodiment. 
         FIG. 19  schematically illustrates a system for capturing user-related tax data for use with tax preparation software according to another embodiment. 
         FIG. 20  illustrates a flowchart of operations performed by software running on a computing device to identify tax topics with high relevant to a user based on user-specific tax history files. 
         FIG. 21  illustrates how tax completeness graphs are simplified by identifying topics with high or higher relevance and eliminating nodes of low or lower relevance from the completion graphs. 
         FIG. 22  illustrates a flowchart of operations performed by software running on a computing device to identify tax highly relevant topics using a user profile. 
         FIG. 23  illustrates a process of utilizing a user profile to generate interview questions on one or more highly relevant topics or automatically obtaining the same from one or more remote data sources. 
         FIG. 24  illustrates how real time statistical data can be used to generate custom tailored interviews for different users with interviews focused on highly relevant tax topics. 
         FIG. 25  illustrates a flowchart of operations performed by software running on a computing device to identify highly relevant tax topics by obtaining data from a financial management program or financial data from an online resource. 
         FIG. 26  illustrates three different users having three different profiles. The different user profiles are associated with different tax completion graphs that are used to generate topic interview questions or statements or automatically obtain tax data. 
         FIG. 27  illustrates generally the components of a computing device that may be utilized to execute the software for automatically calculating or determining tax liability or refund amount and preparing a tax return based thereon according to one embodiment. 
     
    
    
     DETAILED DESCRIPTION OF ILLUSTRATED EMBODIMENTS 
     Tax preparation is a time-consuming and laborious process. It is estimated that individuals and businesses spend around 7 billion hours per year complying with the filing requirements promulgated by the Internal Revenue Service in the United States. Tax preparation software has been commercially available to assist taxpayers in preparing their tax returns. Tax preparation software is typically run on a computing device such as a computer, laptop, tablet, mobile computing device such as a Smartphone, or remotely on another computer and accessed via a network. 
     Traditionally, a user has walked through a set of rigidly defined user interface interview screens that selectively ask questions that are relevant to a particular tax topic or data field needed to calculate a taxpayer&#39;s tax liability. 
     In contrast to the rigidly defined user interface screens used in prior iterations of tax preparation software, the current methods and systems provide tax preparation software  100  that runs on computing devices  102  that operates on a construct or platform in which tax rules and the calculations based thereon are established in declarative data-structures, namely, one or more completeness graph(s)  12  and one or more tax calculation graph(s)  14 . 
     Completeness graphs  12  and tax calculation graphs  14  are data structures in the form of graphs having nodes and interconnecting arcs in which arcs are directed from one node to another. Completion graphs  12  identify when all conditions have been satisfied to complete a particular tax topic or, collectively, produce a fileable tax return. The tax calculation graph  14  semantically describes data depending tax operations that perform a tax calculation or operation in accordance with tax code or tax rules. Examples of these data structures may be found in U.S. patent application Ser. Nos. 14/097,057 and 14/448,886, both of which are incorporated by reference as if set forth fully herein. 
     Use of these data-structures permits the user experience to be loosely connected or even divorced from the tax calculation engine and the data used in the tax calculations. Tax calculations are dynamically calculated based in tax data derived from sourced data that is obtained from a data capture utility as described herein, estimates, user input, or a combination of the above. A smart tax logic agent running on a set of rules can review current run time data and evaluate missing data fields and propose suggested questions to be asked to a user to fill in missing blanks. This process can be continued until completeness of all tax topics has occurred. An electronic return can then be prepared and filed with respect to the relevant taxing jurisdictions. 
     According to one aspect of the invention, a computer-implemented for capturing user-related tax data for use with tax preparation software is provided. The computing device connects to one or more remotely located data sources and executes a data capture utility that captures and transfers at least some tax data to a data store configured to store user-specific tax data. The data capture utility is able to automatically gather and collect tax-related information that is needed by the tax preparation software to compute a tax liability or refund and ultimately complete a fileable tax return. The data capture utility thus significantly reduces the amount of manually entered tax data that is required to be input into the tax preparation software. The data capture utility speeds the process of preparing a tax return by automatically collecting and then storing in a data store or repository tax data that is used by the tax preparation software. 
       FIG. 1  illustrates graphically how tax legislation/tax rules  10  are broken down into a completeness graph  12  and a tax calculation graph  14 . In one aspect of the invention, tax legislation or rules  10  are parsed or broken into various topics. For example, there may be nearly one hundred topics that need to be covered for completing a federal tax return. When one considers both federal and state tax returns, there can be well over one hundred tax topics that need to be covered. When tax legislation or tax rules  10  are broken into various topics or sub-topics, in one embodiment of the invention, each particular topic (e.g., topics A, B) may each have their own dedicated completeness graph  12 A,  12 B and tax calculation graph  14 A,  14 B as seen in  FIG. 1 . 
     Note that in  FIG. 1 , the completeness graph  12  and the tax calculation graph  14  are interdependent as illustrated by dashed line  16 . That is to say, some elements contained within the completeness graph  12  are needed to perform actual tax calculations using the tax calculation graph  14 . Likewise, aspects within the tax calculation graph  14  may be needed as part of the completion graph  12 . Taken collectively, the completeness graph  12  and the tax calculation graph  14  represent data structures that capture all the conditions necessary to complete the computations that are required to complete a tax return that can be filed. The completeness graph  12 , for example, determines when all conditions have been satisfied such that a “fileable” tax return can be prepared with the existing data. The completeness graph  12  is used to determine, for example, that no additional data input is needed to prepare and ultimately file a tax return. The completeness graph  12  is used to determine when a particular schema contains sufficient information such a tax return can be prepared and filed. Individual combinations of completeness graphs  12  and tax calculation graphs  14  that relate to one or more topics can be used to complete the computations required for some sub-calculation. In the context of a tax return, for example, a sub-selection of topical completeness graphs  12 A and tax calculation graphs  14 A can be used for intermediate tax results such as Adjusted Gross Income (AGI) or Taxable Income (TI). 
     The completeness graph  12  and the tax calculation graph  14  represent data structures that can be constructed in the form of tree.  FIG. 2  illustrates a completeness graph  12  in the form of a tree with nodes  20  and arcs  22  representing a basic or general version of a completeness graph  12  for the topic of determining, for example, whether a child qualifies as a dependent for federal income tax purposes. A more complete flow chart-based representation of questions related to determining a “qualified child” may be found in U.S. patent application Ser. No. 14/097,057, which is incorporated by reference herein. Each node  20  contains a condition that in this example is expressed as a Boolean expression that can be answered in the affirmative or negative. The arcs  22  that connect each node  20  illustrate the dependencies between nodes  20 . The combination of arcs  22  in the completeness graph  12  illustrates the various pathways to completion. A single arc  22  or combination of arcs  22  that result in a determination of “Done” represent a pathway to completion. As seen in  FIG. 2 , there are several pathways to completion. For example, one pathway to completion is where an affirmative (True) answer is given to the question of whether you or a spouse can be claimed on someone else&#39;s tax return. If such a condition is true, your child is not a qualifying dependent because under IRS rules you cannot claim any dependents if someone else can claim you as a dependent. In another example, if you had a child and that child did not live with you for more than six months of the year, then your child is not a qualifying dependent. Again, this is a separate IRS requirement for a qualified dependent. 
     As one can imagine given the complexities and nuances of the tax code, many tax topics may contain completeness graphs  12  that have many nodes with a large number of pathways to completion. However, by many branches or lines within the completeness graph  12  can be ignored, for example, when certain questions internal to the completeness graph  12  are answered that logically eliminate other nodes  20  and arcs  22  within the completeness graph  12 . The dependent logic expressed by the completeness graph  12  allows one to minimize subsequent questions based on answers given to prior questions. This allows a minimum question set that can be generated that can be presented to a user as explained herein. 
       FIG. 3  illustrates another example of a completeness graph  12  that includes a beginning node  20   a  (Node A), intermediate nodes  20   b - g  (Nodes B-G) and a termination node  20   y  (Node “Yes” or “Done”). Each of the beginning node  20   a  and intermediate nodes  20   a - g  represents a question. Inter-node connections or arcs  22  represent response options. In the illustrated embodiment, each inter-node connection  22  represents an answer or response option in binary form (Y/N), for instance, a response to a Boolean expression. It will be understood, however, that embodiments are not so limited, and that a binary response form is provided as a non-limiting example. In the illustrated example, certain nodes, such as nodes A, B and E, have two response options  22 , whereas other nodes, such as nodes D, G and F, have one response option  22 . 
     As explained herein, the directed graph or completion graph  12  that is illustrated in  FIG. 3  can be traversed through all possible paths from the start node  20   a  to the termination node  20   y . By navigating various paths through the completion graph  12  in a recursive manner can determine each path from the beginning node  20   a  to the termination node  20   y . The completion graph  12  along with the pathways to completion through the graph can be converted into a different data structure or format. In the illustrated embodiment shown in  FIG. 4 , this different data structure or format is in the form of a decision table  30 . In the illustrated example, the decision table  30  includes rows  32  (five rows  32   a - e  are illustrated) based on the paths through the completion graph  12 . In the illustrated embodiment, the columns  34   a - g  of the completion graph represent expressions for each of the questions (represented as nodes A-G in  FIG. 3 ) and answers derived from completion paths through the completion graph  12  and column  34   h  indicates a conclusion, determination, result or goal  34   h  concerning a tax topic or situation, e.g., “Yes—your child is a qualifying child” or “No—your child is not a qualifying child.” 
     Referring to  FIG. 4 , each row  32  of the decision table  30  represents a tax rule. The decision table  30 , for example, may be associated with a federal tax rule, state tax rule, or local tax rule. In some instances, for example, a state tax rule may include the same decision table  30  as the federal tax rule. The decision table  30  can be used, as explained herein, to drive a personalized interview process for the user of tax preparation software  100 . In particular, the decision table  30  is used to select a question or questions to present to a user during an interview process. In this particular example, in the context of the completion graph from  FIG. 3  converted into the decision table  30  of  FIG. 4 , if the first question presented to the user during an interview process is question “A” and the user answers “Yes” rows  32   c - e  may be eliminated from consideration given that no pathway to completion is possible. The tax rule associated with these columns cannot be satisfied given the input of “Yes” in question “A.” Note that those cell entries denoted by “?” represent those answers to a particular question in a node that is irrelevant to the particular pathway to completion. Thus, for example, referring to row  34   a , when an answer to Q A  is “Y” and a path is completed through the completion graph  12  by answering Question C as “N” then answers to the other questions in Nodes B and D-F are “?” since they are not needed to be answered given that particular path. 
     After in initial question has been presented and rows are eliminated as a result of the selection, next, a collection of candidate questions from the remaining available rows  32   a  and  32   b  is determined. From this universe of candidate questions from the remaining rows, a candidate question is selected. In this case, the candidate questions are questions Q C  and Q G  in columns  34   c ,  34   g , respectively. One of these questions is selected and the process repeats until either the goal  34   h  is reached or there is an empty candidate list. 
       FIG. 5  illustrates another embodiment of a decision table  30 . In this embodiment, the decision table  30  includes additional statistical data  36  associated with each rule (e.g., rules R 1 -R 6 ). For example, the statistical data  36  may represent a percentage or the like in which a particular demographic or category of user(s) satisfies this particular path to completion. The statistical data  36  may be mined from existing or current year tax filings. The statistical data  36  may be obtained from a proprietary source of data such as tax filing data owned by Intuit, Inc. The statistical data  36  may be third party data that can be purchased or leased for use. For example, the statistical data  36  may be obtained from a government taxing authority or the like (e.g., IRS). In one aspect, the statistical data  36  does not necessarily relate specifically to the individual or individuals preparing the particular tax return. For example, the statistical data  36  may be obtained based on a number of tax filers which is then classified one or more classifications. For example, statistical data  36  can be organized with respect to age, type of tax filing (e.g., joint, separate, married filing separately), income range (gross, AGI, or TI), deduction type, geographic location, and the like). 
       FIG. 5  illustrates two such columns  38   a ,  38   b  in the decision table  30  that contain statistical data  36  in the form of percentages. For example, column  38   a  (STAT1) may contain a percentage value that indicates taxpayers under the age of thirty-five where Rule 1  is satisfied. Column  38   b  (STAT2) may contain a percentage value that indicates taxpayers over the age of thirty-five where Rule 1  is satisfied. Any number of additional columns  38  could be added to the decision table  30  and the statistics do not have to relate to an age threshold or grouping. The statistical data  36  may be used, as explained in more detail below, by the tax preparation software  100  to determine which of the candidate questions (Q A -Q G ) should be asked to a taxpayer. The statistical data  36  may be compared to one or more known taxpayer data fields (e.g., age, income level, tax filing status, geographic location, or the like) such that the question that is presented to the user is most likely to lead to a path to completion. Candidate questions may also be excluded or grouped together and then presented to the user to efficiently minimize tax interview questions during the data acquisition process. For example, questions that are likely to be answered in the negative can be grouped together and presented to the user in a grouping and asked in the negative—for example, “we think these question do not apply to you, please confirm that this is correct.” This enables the elimination of many pathways to completion that can optimize additional data requests of the taxpayer. Predictive models based on the statistical data  36  may be used to determine candidate questions. 
       FIG. 6  illustrates an example of a tax calculation graph  14 . The tax calculation graph semantically describes the tax legislation/tax rules  10 . In  FIG. 6 , various nodes  24  are leaf or input nodes. Examples of leaf nodes  24  in this particular example include data obtained from W-2 forms, data obtained from 1099-INT forms, data obtained from other investment income, filing status, and number of dependents. In other embodiments, however, the leaf nodes  24  may be populated with information that is automatically obtained by the tax preparation software  100 . As described herein, for example, a data capture utility  43  may be executed by the tax preparation software  100  to capture and transfer tax data from one or more remotely located data sources to a data store that works in connection with the tax preparation software  100  to store user-specific tax data. In one embodiment of the invention, leaf nodes  24  may be automatically populated with data obtained from the data capture utility  43 . For example, in some embodiments, the data capture utility  43  captures tax data from online resources such as financial services websites or other user-specific websites and transfers this data back to a data store. For instance, a user can provide his or her user credentials (e.g., user identification and password) for one or more online resources and the data capture utility  43  can then automatically capture information from these properties and transfer the same back to the data store for use by the tax preparation software  100 . The online resources can be crawled and scanned to scrape or otherwise download tax related information that can be automatically populated into leaf nodes  24 . Online social media and professional sites such as FACEBOOK, LINKED-IN and the like can also be accessed in a similar manner by the data capture utility  43  whereby personal or financial information may be captured and transferred to a data store that is used to populate the leaf nodes  24 . 
     Additional third party information sources such as credit bureaus, government databases, and the like can also be used by the tax preparation software  100  to obtain information that can then be populated in to respective leaf nodes  24 . In still other embodiments, values for leaf nodes  24  may be derived or otherwise calculated. For example, while the number of dependents may be manually entered by a taxpayer, those dependent may not all be “qualifying” dependents for tax purposes. In such instances, the actual number of “qualified” dependents may be derived or calculated by the tax preparation software  100 . In some instances, the data capture utility  43  uses object character recognition (OCR) techniques or the like to extract useful information from files or documents. For example, tax documents (e.g., W-2s or 1099s) may be imaged or scanned with relevant data being automatically extracted. In other embodiments, prior tax returns may be used by the tax preparation software  100  to extract information (e.g., name, potential dependents, address, and social security number) which can then be used to populate the leaf nodes  24 . In still other embodiments, values for leaf nodes  24  may be estimated as described herein. Leaf nodes  24  may also be populated with user inputs. That is to say the user taxpayer will enter this information from a user interface (described in more detail below). Of course, a main goal of the invention described herein is to minimize the amount of manual entry required on behalf of a user. Instead, it is preferable to automatically capture this relevant information by using the data capture utility  43  and manual input of information 
     Still other internal nodes  26  semantically represent a tax concept and may be calculated using a function  28 . Some or all of these internal nodes  26  may be labelled as “tax concepts.” Interconnected nodes  26  containing tax concepts may be connected via “gist” functions that can be tagged and later be used or called upon to explain to the user the reasoning behind why a particular result was calculated or determined by the tax preparation software  100  program as explained in more detail below. For example, the internal nodes  26  and associated functions  28  may be used to generate explanations to the user regarding the reasons behind certain tax calculations. U.S. patent application Ser. No. 14/530,159, which is incorporated by reference herein, explains how such explanations are automatically generated. Gists are well-defined functions to capture domain specific patterns and semantic abstractions used in tax calculations. Gists can be de-coupled from a specific narrow definition and instead be associated with one or more explanation. Examples of common “gists” found in tax legislation/rules include the concepts of “caps” or “exceptions” that are found in various portions of the tax code. The function  28  may include any number of mathematical or other operations. Examples of functions  28  include summation, subtraction, multiplication, division, and look-ups of tables or values from a database  30  or library as is illustrated in  FIG. 6 . It should be understood that nodes within completion graph  12  and the tax calculation graph  14  may be shared in some instances. For example, AGI is a reoccurring tax concept that occurs in many places in the tax code. AGI is used not only for the mathematical computation of taxes is also used, for example, to determine eligibility of certain tax deductions and credits. Thus, the AGI node is common to both the completion graph  12  and the tax calculation graph  14 . 
       FIG. 7  schematically illustrates a system  40  for calculating taxes using rules and calculations based on a declarative data structures according to one embodiment. The system  40  include a data store  42  that contains therein a schema  44  or canonical model representative to the data fields utilized or otherwise required to complete a tax return. The data store  42  may be a repository, file, or database that is used to contain the tax-related data fields. The data store  42  is accessible by a computing device  102 ,  103  as described herein. The data store  42  may be located locally with the computing device  102 ,  103  running the tax preparation software  100  or it may be located remotely, for example, in cloud environment on another, remotely located computer. The schema  44  may include, for example, a schema based on the Modernized e-File (MeF) system developed by the Internal Revenue Service. The MeF is a web-based system that allows electronic filing of tax returns through the Internet. MeF uses extensible markup language (XML) format that is used when identifying, storing, and transmitting data. For example, each line or data element on a tax return is given an XML name tag as well as every instance of supporting data. Tax preparation software  100  uses XML schemas and business rules to electronically prepare and transmit tax returns to tax reporting agencies. Transmitters use the Internet to transmit electronic tax return data to the IRS MeF system. The IRS validates the transmitted files against the XML schemas and Business Rules in the MeF schema  44 . 
     The schema  44  may be a modified version of the MeF schema used by the IRS. For example, the schema  44  may be an extended or expanded version of the MeF model established by government authorities. The schema  44  may have extra fields available for flags (explained herein) that can be utilized to optimize suggested questions that can be presented to user as part of an interview process. While the particular MeF schema  44  is discussed herein the invention is not so limited. There may be many different schemas  44  depending on the different tax jurisdiction. For example, Country A may have a tax schema  44  that varies from Country B. Different regions or states within a single country may even have different schemas  44 . The systems and methods described herein are not limited to a particular schema  44  implementation. The schema  44  may contain all the data fields required to prepare and file a tax return with a government taxing authority. This may include, for example, all fields required for any tax forms, schedules, and the like. Data may include text, numbers, a response to a Boolean expression (e.g., True/False or Yes/No), and flags. As explained in more detail, the data store  42  may, at any one time, have a particular instance  46  of the MeF schema  44  stored therein at any particular time. For example,  FIG. 7  illustrates several instances  46  of the MeF schema  44  (labeled as MeF 1 , MeF 2 , MeF N ). These instances  46  may be updated as additional data is input or otherwise transferred into the data store  42 . 
     As seen in  FIG. 7 , a data capture utility  43  is provided that is executed by or as part of the tax preparation software  100 . The data capture utility  43  connects the computing device  102 ,  103  to one or more remotely located data sources  48   a - 48   g . The data capture utility  43  captures tax data pertaining to the user and then transfers this information to the data store  42 . Tax data includes any data that may be required or used by the MeF schema  44 . Tax data does not necessarily have to include a monetary (e.g., income) component. For example, tax data could include data related to the number and identify of dependents within a household. It also includes other tax-related information such as income amounts and expenses related to tax deductions or credits. The data capture utility  43  may include a number of different forms. For example, the data capture utility  43  may include a screen scraping utility, web crawler, or program that is able to log into a website or other online interface (e.g., using an Application Program Interface or API) and copy all or selected portions of the data would normally appear on a display or screen to a user. The data capture utility  43  in the form of an API may allow for direct computer-to-computer transfer of data. The API may be provided, for example, by the host computer that is hosting the remote data source  48 . This data can then be parsed, filtered, and organized such that relevant tax data is then transferred into the data store  42 . The data capture utility  43  may also use a web crawler utility or program that is crawls over websites or other online content and copies all or selected portions of tax data and transfers the same to the data store  42 . 
     The data capture utility  43  may be executed contemporaneously with the execution and use of the tax preparation software  100 . Alternatively, the data capture utility  43  may be run prior to actual use of the tax preparation software  100  for tax return preparation. In the later approach, relevant data may be captured and stored in the data store  42  for later use. For example, a user may invoke or execute the data capture utility  43  on February 15 th  of a tax year and then later work on preparing his or her taxes closer to the deadline of April 15 th . The data capture utility  43  may be run again to capture any relevant data that was not already captured when run earlier (e.g., a tax form that was not available is now available for use). 
     The data capture utility  43  may operate in a number of different modes to gain access to different user accounts. In one mode, a user will provide his or her authentication information (e.g., login ID and password) to the data capture utility  43  for each different online account as seen in  FIG. 15 . For example, a user may have one or more banking accounts, one or more investing accounts, an online account associated with a mortgage loan, and various social media accounts (e.g., FACEBOOK account, LINKED-IN account). In such instances, the user will identify each such account and provide the proper login credentials. The data capture utility  43  will then use this information to scrape or crawl each designated account and capture and transfer the tax data to the data store  42 . In another mode, as illustrated in  FIG. 16 , the user does not have to provide his or her credentials for each account. Instead, the user may provide his or her credentials (e.g., password or user ID and password) to a financial management software program that itself is already linked to the various online financial services accounts. By gaining access to the financial management program this data capture utility  43  may be able to capture data from existing account histories already scrapped or crawled by the financial management program. The data capture utility  43  may also initiate the financial management program to synchronize or update all linked accounts so that the most recent information can be obtained. Of course, in this embodiment, the user may be requested to provide the authentication information for other accounts that are otherwise not linked or part of the financial management program. This would include, for example, social media accounts that are typically not contained in financial management programs. Thus, in some embodiments, a hybrid version incorporating aspects of the  FIG. 15  and  FIG. 16  may be used. 
     There are a number of different types of data sources  48   a - 48   g  that may be accessed by the data capture utility  43 . For example, a financial data source  48   a  may include an account held with a financial service provider such as a bank, credit union, brokerage, investment advisor, or the like may be accessed by the data capture utility  43 . A financial data source  48   a  may also include a financial management software program, application, or service such as MINT, QUICKEN, QUICKBOOKS, and the like. Such accounts typically provide online access to account information via a website though the invention is not limited to website access. The data capture utility  43  may access data via the website or through another Application Programming Interface (API). Most financial service providers such as banks, credit unions, brokerages, investment advisors typically provide online access for their customers to view holdings, balances, transactions. Financial service providers also typically provide year-end tax documents to their customers such as, for instance, 1099-INT (interest income), 1099-DIV (dividend income), 1099-B (brokerage proceeds), 1098 (mortgage interest) forms. The data contained on these tax forms may be captured and transferred electronically to the data store  42  by the data capture utility. 
     Another example of an online resource that can be accessed by the data capture utility  43  includes social media data sources  48   b . For example, many taxpayers may have social media or similar accounts. These include, by way of illustration and not limitation, FACEBOOK, LINKED-IN, TWITTER, and the like. User&#39;s may post or store personal information on these properties that may have tax implications. For example, a user&#39;s LINKED-IN account may indicate that he or she is married and has three children. The data capture utility  43  may capture this information and write to the data store  42  information regarding the family status of the particular user. For example, the data store  42  may be written with married, three dependents based on the information gathered by the data capture utility  43 . 
     In still another example, a search of a user&#39;s social medial accounts may indicate that a person changed jobs during a tax year. Likewise, a posting on FACEBOOK about a new home may suggest that a person has purchased a home, moved to a new location, changed jobs; all of which may have possible tax ramifications. The identification of multiple jobs or residences during a tax year may be flagged by the data capture utility  43  or by the data store  42  after the information is transferred therein. This flagging may be used to drive or shape the interview process described herein. For instance, using the example above, a person may be asked a question whether or not she changed jobs during the year (e.g., “It looks like you changed jobs during the past year, is this correct?”). Additional follow-up questions can then be presented to the user such as asking for multiple W-2 forms for each job held during the year. Likewise, for the information that indicates that a person purchased a new home may prompt questions by the interface manager  82  that are directed to the user. For example, if the new residence was purchased, there may be tax deductible expenses such as loan origination points, moving expenses, and the like that may be deductible. By finding this personal financial information, the data capture utility  43  is able to more efficiently drive the remaining interview process to focus on relevant tax topics that are germane to events that occurred during the particular tax year of interest. 
     Still referring to  FIG. 7 , another data source includes credit data source  48   c  (e.g. a data source at a credit bureau) that may be accessed by the data capture utility  43 . For example, credit data sources  48   c  contain a rich source of data that may implicate one or more tax items. For example, credit data sources  48   c  may show that a taxpayer has taken out a student loan or home mortgage loan that may be the source of possible tax deductions for the taxpayer. A credit data source  48   c  may indicate unpaid medical bills which may indicate the presence of large medical bills that could potentially be a deductible expense. Another example of a data source includes records from a database or other repository of a government agency data source  48   d . For example, the state department of motor vehicles may contain information relevant to tax portion of vehicle registration fees which can be deductible in some instances. Other government agency data sources  48   d  that may be accessed include the IRS (e.g., IRS tax return transcripts), and state taxing authorities. 
     Another example of a data source  48  includes may include an employer data source  48   e  as illustrated in  FIG. 7 . For example, an employer may provide electronic or online access to payroll and/or tax reporting systems such that employees can access information such as tax reporting forms (e.g., W-2 forms). An employer data source  48   e  may also include a payroll processing company that provides payroll services for that particular employer (e.g., INTUIT Payroll, ADP, Paychex, and the like). 
     Another example of a data source  48  may include third party data sources  48   f  that collect, aggregate or store data that pertain to a user (e.g., LEXIS/NEXIS). For example, people searching services are currently available to find the location or residence address of persons. Likewise, commercial databases are available for asset searches for particular individuals. These third party data sources  48   f  typically require payment and may not need permission from the user as the information is obtained from various publicly available sources. In one aspect of the invention, the data capture utility  43  may submit a payment in order to access and collect this information. Payment may be made by the user of the tax preparation software  100 . Alternatively, payment may be made by another party such as the provider or seller of the tax preparation software  100 . For example, some users may be particularly valued users of the products and services of the provider of the tax preparation software  100  (e.g., these users utilize a variety of products and services or may be likely to do so in the future). For such users, payment may be made to the providers of the third party data sources  48   f  on behalf of the user so that the user may not incur these expenses directly. Of course, this is just one example and various payment arrangements could be made to facilitate the capture and transfer of relevant data to the data store  42  so that manual entry of data is minimized and/or avoided entirely. 
     In another aspect of the invention, the third party data source  48   f  may include a free source of information. For example, many webpages or other Internet accessible content is indexed by various search engines such as GOOGLE, YAHOO, and BING. User-related tax data may be obtained from the data capture utility  43  searching one or more of these third party data sources  48   f.    
     Another example of a data source  48  is a prior year tax return  48   g . A prior year tax return that is stored electronically as prior year tax return data  48   g  can be searched and data is copied and transferred to the data store  42 . The prior year tax return may be in a proprietary format (e.g., .txf, .pdf) or an open source format. The prior year tax return may also be in a paper or hardcopy format that can be scanned or imaged whereby data is extracted using, for example, OCR and transferred to the data store  42 . In this sense the tax return may be considered user input  48   h  as described below. In another embodiment, prior year tax return data  48   g  may be obtained by accessing a government database (e.g., IRS records). For example, the IRS allows one to obtain transcripts of past tax returns in either an online or paper format. The data capture utility  43  can, for example, be provided with the user ID whereby this information is captured automatically. Alternatively, a paper copy may be sent to the taxpayer and an image is taken of the same, subject to OCR, with the relevant data fields captured and transferred to the data store  42 . 
     While a main priority or goal of the invention is to avoid manual entry of tax related data into the tax preparation software  100 , there will likely be some gapes or holes that will need to be filled by the manual entry of data. As seen in  FIG. 7 , user input  48   h  is one type of data source  48 . User input  48   h  may take a number of different forms. For example, user input  48   h  may be generated by a user using, for example, a input device such as keyboard, mouse, touchscreen display, voice input (e.g., voice to text feature) or the like to enter information manually into the tax preparation software  100 . 
     User input  48   h  may also include some form of automatic data gathering. For example, a user may scan or take a photographic image of a tax document (e.g., W-2 or 1099) that is then processed by the tax preparation software  100  to extract relevant data fields that are then automatically transferred and stored within the data store  42 . OCR techniques along with pre-stored templates of tax reporting forms may be called upon to extract relevant data from the scanned or photographic images whereupon the data is then transferred to the data store  42 . 
     Still referring to  FIG. 7 , the tax preparation software  100  executed by the computing device  102 ,  103  includes a tax calculation engine  50  that computes one or more tax calculations based on the available data at any given instance within the schema  44  in the data store  42 . The tax calculation engine  50  may calculate a final tax due amount, a final refund amount, or one or more intermediary calculations (e.g., taxable income, AGI, earned income, un-earned income, total deductions, total credits, alternative minimum tax (AMT) and the like). The tax calculation engine  50  utilizes the one or more calculation graphs  14  as described previously in the context of  FIGS. 1 and 6 . In one embodiment, a series of different calculation graphs  14  are used for respective tax topics. These different calculation graphs  14  may be glued together or otherwise compiled as a composite calculation graph  14  to obtain an amount of taxes due or a refund amount based on the information contained in the data store  42 . The tax calculation engine  50  reads the most current or up to date information contained within the data store  42  and then performs tax calculations. Updated tax calculation values are then written back to the data store  42 . As the updated tax calculation values are written back, new instances  46  of the canonical model  46  are created. The tax calculations performed by the tax calculation engine  50  may include the calculation of an overall tax liability or refund due. The tax calculations may also include intermediate calculations used to determine an overall tax liability or refund due (e.g., AGI calculation). The tax calculation engine  50  may be embodied as a programmed subroutine as part of the tax preparation software  100  that is executed by a processor or controller as described herein. 
     Still referring to  FIG. 7 , the system  40  includes a tax logic agent (TLA)  60 . The TLA  60  operates in conjunction with the data store  42  whereby updated tax data represented by instances  46  are read to the TLA  60 . The TLA  60  contains run time data  62  that is read from the data store  42 . The run time data  62  represents the instantiated representation of the canonical tax schema  44  at runtime. The TLA  60  may contain therein a rule engine  64  that utilizes a fact cache to generate either non-binding suggestions  66  for additional question(s) to present to a user or “Done” instructions  68  which indicate that completeness has occurred and additional input is not needed. The rule engine  64  may operate in the form a Drools expert engine. Other declarative rules engines  64  may be utilized and a Drools expert rule engine  64  is provided as one example of how embodiments may be implemented. The TLA  60  may be implemented as a dedicated module or engine that is executed by or as part of the tax preparation software  100  and may be embodied as a programmed subroutine that is executed by a processor or controller as described herein. 
     As seen in  FIG. 7 , The TLA  60  uses the decision tables  30  to analyze the run time data  62  and determine whether a tax return is complete. Each decision table  30  created for each topic or sub-topic is scanned or otherwise analyzed to determine completeness for each particular topic or sub-topic. In the event that completeness has been determined with respect to each decision table  30 , then the rule engine  64  outputs a “done” instruction  68  to the UI control  80 . If the rule engine  64  does not output a “done” instruction  68  that means there are one or more topics or sub-topics that are not complete, which, as explained in more detail below presents interview questions to a user for answer. The TLA  60  identifies a decision table  30  corresponding to one of the non-complete topics or sub-topics and, using the rule engine  64 , identifies one or more non-binding suggestions  66  to present to the UI control  80 . The non-binding suggestions  66  may include a listing of compilation of one or more questions (e.g., Q 1 -Q 5  as seen in  FIG. 7 ) from the decision table  30 . In some instances, the listing or compilation of questions may be ranked in order by rank. The ranking or listing may be weighted in order of importance, relevancy, confidence level, or the like. For example, a top ranked question may be a question that, based on the remaining rows (e.g., R 1 -R 5 ) in a decision will most likely lead to a path to completion. As part of this ranking process, statistical information such as the STAT1, STAT2 percentages as illustrated in  FIG. 5  may be used to augment or aid this ranking process. Questions may also be presented that are most likely to increase the confidence level of the calculated tax liability or refund amount. In this regard, for example, those questions that resolve data fields associated with low confidence values may, in some embodiments, be ranked higher. Predictive modeling based on statistical data  36  using any suitable data such as previously filed tax returns, demographic information, financial information and the like may be used as part of this ranking process. 
     The following pseudo code generally expresses how a rule engine  64  functions utilizing a fact cache based on the runtime canonical data  62  or the instantiated representation of the canonical tax schema  46  at runtime and generating non-binding suggestions  66  provided as an input a UI control  80 . As described in U.S. application Ser. No. 14/097,057 previously incorporated herein by reference, data such as required inputs can be stored to a fact cache so that the needed inputs can be recalled at a later time, and to determine what is already known about variables, factors or requirements of various rules: 
     Rule engine ( 64 )/Tax Logic Agent (TLA) ( 60 ) 
     // initialization process 
     Load_Tax_Knowledge_Base; 
     Create_Fact_Cache; While (new_data_from_application)
         Insert_data_into_fact_cache;   collection=Execute_Tax_Rules; // collection is all the fired rules and       

     corresponding conditions
         suggestions=Generate_suggestions (collection);   send_to_application(suggestions);       

     The TLA  60  may also receive or otherwise incorporate information from a statistical/life knowledge module  70 . The statistical/life knowledge module  70  contains statistical or probabilistic data related to the taxpayer. For example, statistical/life knowledge module  70  may indicate that taxpayers residing within a particular zip code are more likely to be homeowners than renters. The TLA  60  may use this knowledge to weight particular topics or questions related to these topics. For example, in the example given above, questions about home mortgage interest may be promoted or otherwise given a higher weight. The statistical knowledge may apply in other ways as well. For example, tax forms often require a taxpayer to list his or her profession. These professions may be associated with transactions that may affect tax liability. For instance, a taxpayer may list his or her occupation as “teacher.” The statistic/life knowledge module  70  may contain data that shows that a large percentage of teachers have retirement accounts and in particular 403(b) retirement accounts. This information may then be used by the TLA  60  when generating its suggestions  66 . For example, rather than asking generically about retirement accounts, the suggestion  66  can be tailored directly to a question about 403(b) retirement accounts. 
     The data that is contained within the statistic/life knowledge module  70  may be obtained by analyzing aggregate tax data of a large body of taxpayers. For example, entities having access to tax filings may be able to mine their own proprietary data to establish connections and links between various taxpayer characteristics and tax topics. This information may be contained in a database or other repository that is accessed by the statistic/life knowledge module  70 . This information may be periodically refreshed or updated to reflect the most up-to-date relationships. Generally, the data contained in the statistic/life knowledge module  70  is not specific to a particular tax payer but is rather generalized to characteristics shared across a number of tax payers although in other embodiments, the data may be more specific to an individual taxpayer. 
     Still referring to  FIG. 7 , the UI control  80  encompasses a user interface manager  82  and a user interface presentation or user interface  84 . The user interface presentation  84  is controlled by the interface manager  82  and may manifest itself, typically, on a visual screen or display  104  that is presented on a computing device  102  (seen, for example, in  FIG. 13 ). The computing device  102  may include the display of a computer, laptop, tablet, mobile phone (e.g., Smartphone), or the like. Different user interface presentations  84  may be invoked using a UI generator  85  depending, for example, on the type of display or screen  104  that is utilized by the computing device. For example, an interview screen with many questions or a significant amount of text may be appropriate for a computer, laptop, or tablet screen but such as presentation may be inappropriate for a mobile computing device such as a mobile phone or Smartphone. In this regard, different interface presentations  84  may be prepared for different types of computing devices  102 . The nature of the interface presentation  84  may not only be tied to a particular computing device  102  but different users may be given different interface presentations  84 . For example, a taxpayer that is over the age of 60 may be presented with an interview screen that has larger text or different visual cues than a younger user. 
     The user interface manager  82 , as explained previously, receives non-binding suggestions from the TLA  60 . The non-binding suggestions may include a single question or multiple questions that are suggested to be displayed to the taxpayer via the user interface presentation  84 . The user interface manager  82 , in one aspect of the invention, contains a suggestion resolution element  88 , is responsible for resolving of how to respond to the incoming non-binding suggestions  66 . For this purpose, the suggestion resolution element  88  may be programmed or configured internally. Alternatively, the suggestion resolution element  88  may access external interaction configuration files. Additional details regarding configuration files and their use may be found in U.S. patent application Ser. No. 14/206,834, which is incorporated by reference herein. 
     Configuration files specify whether, when and/or how non-binding suggestions are processed. For example, a configuration file may specify a particular priority or sequence of processing non-binding suggestions  66  such as now or immediate, in the current user interface presentation  84  (e.g., interview screen), in the next user interface presentation  84 , in a subsequent user interface presentation  84 , in a random sequence (e.g., as determined by a random number or sequence generator). As another example, this may involve classifying non-binding suggestions as being ignored. A configuration file may also specify content (e.g., text) of the user interface presentation  84  that is to be generated based at least in part upon a non-binding suggestion  66 . 
     A user interface presentation  84  may be pre-programmed interview screens that can be selected and provided to the generator element  85  for providing the resulting user interface presentation  84  or content or sequence of user interface presentations  84  to the user. User interface presentations  84  may also include interview screen templates, which are blank or partially completed interview screens that can be utilized by the generation element  85  to construct a final user interface presentation  84  on-the-fly during runtime. 
     As seen in  FIG. 7 , the UI control  80  interfaces with the data store  42  such that data that is entered by a user (i.e., user input  48   h ) in response to the user interface presentation  84  can then be transferred or copied to the data store  42 . The new or updated data is then reflected in the updated instantiated representation of the schema  44 . Typically, although not exclusively, in response to a user interface presentation  84  that is generated (e.g., interview screen), a user inputs data to the tax preparation software  100  using an input device that is associated with the computing device. For example, a taxpayer may use a mouse, finger tap, keyboard, stylus, voice entry, or the like to respond to questions. The taxpayer may also be asked not only to respond to questions but also to include dollar amounts, check or un-check boxes, select one or more options from a pull down menu, select radio buttons, or the like. Free form text entry may also be request of the taxpayer. For example, with regard to donated goods, the taxpayer may be prompted to explain what the donated good are and describe the same in sufficient detail to satisfy requirements set by a particular taxing authority. 
     Still referring to  FIG. 7 , a tax return (either electronic return or paper return) is prepared by the TLA  60  or at the direction of the TLA  60 . The TLA  60  is operatively coupled to a services engine  90  that is configured to perform a number of tasks or services for the taxpayer. For example, the services engine  90  can include a printing option  92 . The printing option  92  may be used to print a copy of a tax return, tax return data, summaries of tax data, reports, tax forms and schedules, and the like. The services engine  90  may also electronically file  94  or e-file a tax return with a tax authority (e.g., federal or state tax authority). The services engine  90  may be used in connection with the TLA  60  to properly format the required data into proper format for electronic filing. Whether a paper or electronic return is filed, data from the data store  42  required for particular tax forms, schedules, and the like is transferred over into the desired format. With respect to e-filed tax returns, the tax return may be filed using the MeF web-based system that allows electronic filing of tax returns through the Internet. Of course, other e-filing systems may also be used other than those that rely on the MeF standard. The services engine  90  may also make one or more recommendations  96  based on the run-time data  62  contained in the TLA  60 . For instance, the services engine  90  may identify that a taxpayer has incurred penalties for underpayment of estimates taxes and may recommend to the taxpayer to increase his or her withholdings or estimated tax payments for the following tax year. As another example, the services engine  90  may find that a person did not contribute to a retirement plan and may recommend  96  that a taxpayer open an Individual Retirement Account (IRA) or look into contributions in an employer-sponsored retirement plan. The services engine  90  may also include a calculator  98  that can be used to calculate various intermediate calculations used as part of the overall tax calculation algorithm. For example, the calculator  98  can isolate earned income, investment income, deductions, credits, and the like. The calculator  98  can also be used to estimate tax liability based on certain changed assumptions (e.g., how would my taxes change if I was married and filed a joint return?). The calculator  98  may also be used to compare analyze differences between tax years. The services engine  90  may be embodied as a programmed subroutine as part of the tax preparation software  100  that is executed by a processor or controller as described herein. 
       FIG. 8A  illustrates another schematic illustration of a system  40 ′ for calculating taxes using rules and calculations based on a declarative data structures. Those elements equivalent to the embodiment of  FIG. 7  are labelled with the same element numbers. In this alternative embodiment, the system  40 ′ includes an estimation module  110  that writes to the data store  42  with estimates  112  inferences, or guesses of one or more data fields contained within the data store  42 . The estimates  112  or guesses may pertain to any number of tax topics and may include alphanumeric characters, a response to a Boolean operation, text, and the like. In this particular embodiment, the estimate module  110  assigns an estimated value to one or more data fields of the schema  44  contained in the data store  42 . The estimated value may be obtained in a number of ways. In one aspect, user input  48   h  is used to generate the estimated value. For example, the user may be prompted by UI control  80  with a prompt  84  to enter a guess or estimate on a particular data field. In another aspect, a prior tax return or multiple tax returns can be used to generate an estimated value. For example, taxpayer A may have a history of the past three years of tax return data (e.g., stored as proprietary or standardized files) stored or otherwise made available to tax preparation software  100  that shows yearly dividend income of $1,200, $1,350, and $1,400. The estimation module  110  may generate an average of $1,317 to be used as an estimate for a current year return. Alternatively, the estimation module  110  may employ more robust analytics than merely computing an average or mean value. In the context of this example, the estimation module  110  seeing that dividends appear to be increasing in value each year may attempt to find a function (e.g., linear or non-linear function) that fits the observable data and can be used to better estimate current year tax data. For example, in the above example, a curve fitting function may estimate current year dividend at $1,525 rather than the average value of $1,317. 
     The estimation module  110  may use other techniques to generate estimates. For example, the estimate module  110  may use pro-ration to arrive at an estimated number. For example, assume that the data capture utility  43  captures nine months of interest income for a taxpayer. The estimation module  110  may pro-rate this over a twelve month period to estimate a full year&#39;s worth of interest income. A similar technique may be used to estimate wage income by the estimation module  110 . Execution of the estimation module  110 , which may be a programmed subroutine as part of the tax preparation software  100  that is executed by a processor or controller as described herein. 
     Various data sources  48  may also be used by the estimation module  110  to provide estimated values. Data sources  48  may be used as inputs for the estimation module  110 . Data that is obtained from the data sources  48  may be temporarily written or contained within the estimation module  110  or a storage area associated therewith prior to estimates being transferred to the data store  42 . Data sources  48  include, for example, a financial data source  48   a , social media data source  48   b , credit data source  48   c , governmental agency data source  48   d , employer data source  48   e , third party data source  48   f , and user input  48   h  as explained herein. The data capture utility  43  may be used to retrieve data that is provided to the estimation module  110 . 
     Estimates may include numerical data (e.g., dividend income) but it may also include non-numerical data. For example, the estimation module  110  may estimate a user&#39;s tax filing status (e.g., married filing jointly) based on data that is obtained from one or more remotely located data sources  48 . For example, the estimation module may make an inference on filing status based on postings made to an online social media account and entries made in a credit reporting database that are searched by the data capture utility  43 . 
     It should also be understood that the estimation module  110  may rely on one or more inputs to arrive at an estimated value. For example, the estimation module  110  may rely on a combination of prior tax return data  48   g  in addition to, for example, data obtained from a financial data source  48   a , social media data source  48   b , or the like to estimate a value or condition. This may result in more accurate estimations by relying on multiple, independent sources of information. The UI control  80  may be used in conjunction with the estimation module  110  to select those sources of data to be used by the estimation module  110 . For example, user input  48   h  will require input by the user of data using a user interface presentation  84 . The UI control  80  may also be used to identify and select prior tax returns from prior tax return data  48   g . Likewise, user IDs and passwords may be needed for various data sources  48  in which case UI control  80  can be used in conjunction with the data capture utility  43  needed to obtain this information from the user. 
     In one embodiment of the invention, the estimated values or other estimated data provided by the estimation module  110  may be associated with one or more attributes  122  as illustrated in  FIG. 9 . The attributes  122  may indicate a label such as a source  124  or provenance of the estimated value (e.g., user input  48   h , prior tax return data  48   g , financial data source  48   a , social media data source  48   b , and the like). In the example of  FIG. 9 , a source ID  124  indicates the particular source of the data that is used for the field. For example, source ID  01  may correspond to user input  48   h . Source ID  03  may correspond to a data  48   g  from a prior year tax return. Source ID  05  may correspond to an employer data source  48   e  while source ID  06  corresponds to a credit data source  48   c.    
     The attributes  122  may also include a confidence level  126  associated with each estimated field. The confidence level  126  is indicative of the level of trustworthiness of the estimated user-specific tax data and may be expressed in a number of different ways. For example, confidence level  126  may be broken down to intervals (e.g., low, medium, high) with each estimated value given an associated label (e.g., L—low, M—medium, H, high). Alternatively, confidence levels  126  may be described along a continuum without specific ranges (e.g., range from 0.0 to 1.0 with 0.0 being no confidence and 1.0 with 100% confidence). The confidence level  126  may be assigned based on the source of the estimated user-specific tax data (e.g., source #1 is nearly always correct so estimated data obtained from this source will be automatically assigned a high confidence level). 
     In some embodiments, the estimation module  110  may acquire a plurality of estimates from different sources (e.g., user input  48   h , prior year tax return data  48   g , financial data sources  48   a , social media data sources  48   b ) and only write the “best” estimate to the data store  42  (e.g., the source with the highest confidence level  126 ). Alternatively, the estimation module  110  may be configured to ignore data (e.g., sources) that have confidence levels  126  below a pre-determined threshold. For example, all “low” level data from a source may be ignored. Alternatively, all the data may be stored in the data store  42  including, for example, the attribute  122  of the confidence level  126  with each entry. The tax calculation engine  50  may ignore data entries having a confidence level below a pre-determined threshold. The estimation module  110  may generate a number of different estimates from a variety of different sources and then writes a composite estimate based on all the information from all the different sources. For example, sources having higher confidence levels  126  may be weighted more than other sources having lower confidence levels  126 . 
     Still referring to  FIG. 9 , another attribute  122  may include a confirmation flag  128  that indicates that a taxpayer or user of the tax preparation software  100  has confirmed a particular entry. For example, confirmed entries may be given an automatic “high” confidence value as these are finalized by the taxpayer. Another attribute  122  may include a range of values  130  that expresses a normal or expected range of values for the particular data field. The range of values  130  may be used to identify erroneous estimates or data entry that appear to be incorrect because they fall outside an intended range of expected values. Some estimates, such as responses to Boolean expressions, do not have a range of values  130 . In this example, for example, if the number of estimates dependents is more than five (5), the tax logic agent  60  may incorporate into the rules engine  64  attribute range information that can be used to provide non-binding suggestions to the UI control  80  recommending a question to ask the taxpayer about the high number of dependents (prompting user with “are you sure you have seven dependents”). Statistical data may also be used instead of specific value ranges to identify suspect data. For example, standard deviation may be used instead of a specific range. When a data field exhibits statistical deviation beyond a threshold level, the rules engine  64  may suggest a prompt or suggestion  66  to determine whether the entry is a legitimate or not. Additional details regarding methods and systems that are used to identify suspect electronic tax data may be found in U.S. Pat. No. 8,346,635 which is incorporated by reference herein. 
     In one embodiment, the data store  42  may receive data from multiple sources. For example, the data capture utility  43  may capture data that is transferred to the data store  42  while the estimation module  110  provides estimates  112  to the data store  42  for other portions of the schema  44 . User input  48   h  may also be transferred to the data store  42 . The estimation module  110  may work in conjunction with the data capture utility  43  to capture relevant needed to populate the data store  42 . For example, the data capture utility  43  may capture from a financial data source  48   a  a number of dividend transactions occurring in an investment account throughout all or a portion of the tax year. The completed 1099-DIV tax form may not be available yet from the particular financial service provider that has the financial data source  48   a  yet the estimation module  110  can be used to estimate the total dividend amount for the tax year and transfer the estimate  112  to the data store  42 . As another example, the estimation module  110  may use prior tax return data  48   g  to estimate values to be transferred to the data store  42 . 
     It should be understood that the estimation module  110  not only can estimate values (i.e., numerical amounts) but the estimation module  110  is also able to estimate various states or conditions that have tax implications. For example, the estimation module  110  may be able to estimate or predict your tax filing status based on data found, for example, in credit data obtained in the credit data source  48   c  or through analysis of a data contained within a social media data source  48   b  (e.g., online profile status indicates married and estimation module  110  indicates to data store  42  that tax payer is filing jointly with a spouse). 
     Referring back to  FIG. 8A , in this embodiment, the tax logic agent  64  includes within or as part of the rules engine  64  attribute rules  129  that are incorporated and used to generate the non-binding suggestion. For example, as explained above, when an estimated value is input or otherwise transferred to the shared data structure  42 , this estimated value may fall outside a generally accepted range of values. This may prompt the TLA  60  to suggest a confirmatory question to the UI control  80  to confirm the accuracy of the estimated value that has been obtained. Likewise, various data fields may be associated with a low level of confidence as seen in  FIG. 9 . Questions relating to tax topics that incorporate these low confidence fields may be promoted or otherwise ranked higher so that accurate values may be obtained from the taxpayer. Conversely, if a particular estimated tax field is associated with a high level of confidence, questions concerning this field may be demoted to a lower importance using the attribute rules  130 . For example, multiple fields with a high level of confidence could be presented to the user in a single interview screen to confirm the accuracy of this information without the need to walk through individual questions. 
     In some embodiments, each estimated value produced by the estimation module  110  will need to be confirmed by the user using the UI control  80 . For example, the user interface manager  82  may present estimated data fields to the user for confirmation or verification using a user interface presentation  84 . In other embodiments, however, the user may override data using the user interface presentation  84 . Some estimated data, for example, data having a high confidence level  126  may not need to be confirmed but can be assumed as accurate. 
       FIG. 10  illustrates an illustrative user interface presentation  84  on a computing device  102  that incorporates the attribute rules  130  to arrive a confidence level for tax calculations. The user interface presentation  84  appears on a screen  104  of the computing device  102 . As seen in  FIG. 10 , the dollar amount of the calculated federal refund in listed along with the refund amount of the calculated state refund. The user interface presentation  84  includes a confidence level indicator  132 . The confidence level indicator  132  indicates the overall or aggregate confidence level in the tax calculation. The tax calculation could include a refund amount as illustrated in  FIG. 10  but it may also include a taxes due amount. In the example given in  FIG. 10 , the confidence level indicator  132  is expressed as a bar  134  in a bar meter type implementation. 
     The confidence level indicator  132  may take a number of different forms, however. For example, the confidence level indicator  132  may be in the form of a gauge or the like that such as that illustrated in  FIG. 11 . In the example, of  FIG. 11 , the confidence level indicator  132  is indicated as being “low.” Of course, the confidence level indicator  132  may also appear as a percentage (e.g., 0% being low confidence, 100% being high confidence) or as a text response (e.g., “low,” “medium,” and “high” or the like). Other graphic indicia may also be used for the confidence level indicator  132 . For example, the color of a graphic may change or the size of the graphic may change as a function of level of confidence. Referring to  FIG. 11 , in this instance, the user interface presentation  84  may also include hyperlinked tax topics  136  that are the primary sources for the low confidence in the resulting tax calculation. For example, the reason that the low confidence is given is that there is low confidence in the amount listed on the taxpayer&#39;s W-2 form that has been automatically imported into the data store  42 . This is indicated by the “LOW” designation that is associated with the “earned income” tax topic. In addition, in this example, there is low confidence in the amount of itemized deductions being claimed by a taxpayer. This is seen with the “LOW” designation next to the “deductions” tax topic. Hyperlinks  136  are provided on the screen so that the user can quickly be taken to and address the key drivers in the uncertainty in the calculated tax liability. 
       FIG. 8B  illustrates another schematic illustration of a system  40 ″ for calculating taxes using rules and calculations based on a declarative data structures. Those elements equivalent to the embodiment of  FIG. 7  are labelled with the same element numbers. In this alternative embodiment, the system  40 ″ includes a topic analysis engine  140 . The topic analysis engine  140  is executed by the computing device  102 ,  103  and is configured to identify tax topics that are highly relevant to the user. The topic analysis engine  140  may identify tax topics that are highly relevant in response to user input  48   h . For example, preliminary questions regarding the user&#39;s occupation, residence, filing status, and other basic information may be used to generate a user profile as explained herein that can be used to flag specific tax topics of interest that are likely to apply to the taxpayer. To this end, a database  142  is linked to the topic analysis engine  140  and may contain information on past or current taxpayers that can be queried or otherwise utilized by the topic analysis engine to identify relevant topics (see, for example, database  502  in  FIG. 24 ). After relevant tax topics of interest have been identified, the user interface manager  82  may generate relevant interview questions or screens that are shown to the user to develop data for these topics. Alternatively, one or more remote data sources  48  can be accessed using the data capture utility  43  to obtain the highly relevant tax data. Rather than asking the user interview questions, the data capture utility  43  is able access the applicable remote data sources  48  for this information. If additional information is needed, interview questions can be generated by the user interface manager  82 . In another example, the data capture utility  43  may be used to analyze prior tax returns. The topic analysis engine  140  can examine the data from prior tax returns to identify those tax topics that are highly relevant. The topic analysis engine  140  may then communicate those identified topics to the user interface manager  82 . 
     Still referring to  FIG. 8B , for those tax topics that are deemed to have low relevance, the data store  42  may be informed of this decision by, for example, giving a particular data field corresponding to this information with a flag or giving the filed a zero or null value. This information can be read by the tax logic agent  60  so that completion graphs  12  can be minimized or otherwise reduced if this information is not needed. Of course, at some point the tax topics that are “ignored” will have to be presented to the user to verify or confirm that the tax topic does not apply. This can be done efficiently, however, because these topics can be summarized or aggregated (e.g., list) and presented to the user in a format that allows for easy confirmation/verification. 
       FIG. 12  illustrates the operations of one illustrative method for calculating tax liability according to an embodiment of the invention. In operation  1000 , a user initiates the tax preparation software  100  on a computing device  102 ,  103 , as seen, for example, in  FIG. 13 . The tax preparation software  100  may reside on the actual computing device  102  that the user interfaces with or, alternatively, the tax preparation software  100  may reside on a remote computing device  103  such as a server or the like as illustrated. In such an instances, the computing device  102  that is utilized by the user or tax payer communicates via the remote computing device  103  using an application  105  contained on the computing device  102 . The tax preparation software  100  may also be run using conventional Internet browser software. Communication between the computing device  102  and the remote computing device  103  may occur over a wide area network such as the Internet. Communication may also occur over a private communication network (e.g., mobile phone network). 
     Referring back to  FIG. 12 , after initiating the tax preparation software  100 , the tax preparation software  100 , in operation  1100 , executes the data capture utility  43 . The data capture utility  43  connects to the one or more data sources  48  as described herein and captures (e.g., through screen scraping techniques) tax data and transfers the same to the data store  42 . Note that the gathering of tax related data from the one or more data sources  48  may occur at the time the tax preparation software  100  is run. Alternatively, the gathering of tax related data from the one or more data sources  48  may occur over a period of time. For example, data sources  48  may be periodically queried over time (e.g., during a tax reporting year) whereby updated information is stored in a database (not shown) or the like that is then accessed by the tax preparation software  100 . This option may improve the efficiency and speed of tax return preparation as the information is already available. Thus, in some embodiments, the data capture utility  43  may have captured data even prior to the current execution of the tax preparation software  1000 . 
     In one embodiment, the data capture utility  43  provides the user via the user presentation  84  with a listing or grouping of different user accounts that can be searched by the data capture utility  43 . In one aspect of the invention, the user selects his or her accounts that are to be queried by the data capture utility  43 . For instance, a series of pull down menus can be provided on the user presentation for various categories of data sources  48 . For example, a user may be provided with a listing of financial service providers (e.g., financial data source  48   a ). These may include financial institutions where the user has a bank account, credit union account, investment account, and the like. The user can also select his or her social media accounts (e.g., social media data source  48   b ). Likewise, the user may be asked to provide or select from a list his or her employer. Once the relevant data sources  48  have been identified, the user will enter his or her authentication credentials. Typically this includes, for example, a user ID and password. In other instances, the user may be asked for other information such as name, employee number, or social security number.  FIG. 15  illustrates a view of a computer  102  and associated display that illustrates a number of different data sources  48  that have been selected to be interrogated by the data capture utility  43 . 
     A user may be given the opportunity to select which data sources  48  they want accessed and searched for relevant tax related data that will be imported into the data store  42 . A user may be asked to submit his or her account and password information for some data sources  48  using the UI control  80 . Other data sources  48  such as some third party data sources  48   f  or government agency data sources  48   d  may be accessed without such information. Prior tax return information (e.g., prior return data source  48   g ) may be found by the data capture utility  43  by, for example, searching a local storage device for designated file types. Of course, this search could also be performed on a remote or cloud based storage device that may have prior tax return data stored therein. 
     Still referring to  FIG. 12 , in one embodiment, the estimation module  110  is executed as seen in operation  1200  whereby one or more estimates are generated and transferred to the data store  43 . It should be understood, however, that the estimation module  110  is an optional feature and may not be utilized in some embodiments. After the schema  44  is populated with the various captured and transferred data fields from the data sources  48 , the tax calculation engine  50 , using the calculation graphs  14 , reads data from the data store  42 , performs tax calculations, and writes back data to the data store  42  (operation  1300 ). The schema  44  may also be populated with estimates or educated guesses as explained herein using the estimation module  110  as described in the context of the embodiment of  FIG. 8A . In operation  1400 , the tax logic agent  60  reads the run time data  62  which represents the instantiated representation of the canonical tax schema  44  at runtime. The tax logic agent  60  then utilizes the decision tables  30  to generate and send non-binding suggestions  66  to the UI control  80  as seen in operation  1500 . Alternatively, the tax logic agent  60  may determine that completeness has been achieved across the tax topics in which case a done instruction may be delivered to the UI control as seen in operation  1600 . The done instruction indicates that completeness has been achieved and a fileable tax return can be prepared based on the data contained within the data store  42 . In operation  1610 , a tax return is then generated by the tax preparation software  100  using, for example, the services engine  90 . In the event the tax return that is generated is an electronic tax return, the tax preparation software  100  can then file the tax return with the appropriate taxing authorities as illustrated in operation  1620 . 
     If there is not enough information in the data store  42  for fileable tax return, there is no “done” instruction and the process continues whereby the user interface manager  82  will then process the suggestion(s)  66  using the suggestion resolution element  88  for resolving of how to respond to the incoming non-binding suggestions  66  as seen in operation  1700 . The user interface manager  82  then generate a user interface presentation  84  to the user as seen in operation  1800  whereby the user is presented with one or more prompts. The prompts may include questions, affirmations, confirmations, declaratory statements, and the like. The prompts are displayed on a screen  104  of the computing device  102  whereby the user can then respond to the same by using one or more input devices associated with the computing device  102  (e.g., keyboard, mouse, finger, stylus, voice recognition, etc.). 
     Still referring to  FIG. 12 , as seen in operation  1900 , the response or responses (e.g., user inputs  18   h ) that are given by the user of the tax preparation software  100  are then written back to the data store  42  to thereby update all appropriate fields of the schema  44 . The process then continues with operation  1300  and proceeds as explained above until a completeness state has been reached and a done instruction is sent to the UI control  80 . 
       FIG. 14  illustrates a schematic representation of one preferred embodiment of the invention in which user input via the user interface presentation  84  is minimized. As seen in  FIG. 14 , tax calculations  2000  are performed based on a number of inputs including data that is captured from the data capture utility  43  and, optionally, the estimation module  110 . User inputs  48   h  are input using the user interface presentation  84  that appears on the computing device  102 ,  103 . It should be noted that tax calculations  2000  can be made even though there may be some missing data entry that is not incorporated into the tax calculation  2000 . While the tax return may not be in a condition to be filed, the tax liability or a sub-component thereof (e.g., total itemized deductions, or gross income) can often be calculated. The manually entered user inputs  48   h  are combined with the automatically gathered data from the data capture utility  43  and the optional estimation module  110 . In one aspect of the invention, a large portion of data needed for the calculation and preparation of taxes is obtained by the data capture utility  43 . For example, in one aspect of the invention, more than 20% of the user-specific data required to prepare a fileable tax return is obtained by the data capture utility  43 . In other embodiments, this number is even higher (e.g., higher than 40%). The higher the percentage of data that is obtained by the data capture utility  43  translates into less manual entry of data using user input  48   h.    
     The user input  48   h  aspect may be minimized by first populating relevant fields using data obtained from the data capture utility  43  or estimates  112  provided by the estimate module  110 . The user input  48   h  may be used to input missing data that was not otherwise obtained. User input  48   h , however, may also be used to verify estimates or verify sourced data. For example, prior to being incorporated into tax calculations (e.g., stored within the data store  42 ), the user may be prompted to accept, reject, or alter the values of data obtained from the data capture utility  43  or estimates  112  generated by the estimate module  110 . User  48   h  may also be used to resolve conflicts. For example, the data capture utility  43  may obtain sourced data  48  that may conflict with estimates  112  and user input  48   h  may be required to resolve the conflict. User input  48   h  may also be used to accept or reject sourced data from the data capture utility  43  or estimates  112 . For example, a user may know that a particular estimate  112  is incorrect and plans to input this particular value manually.  FIG. 14  illustrates that after sufficient data has been entered manually, captured using the data capture utility  43 , or estimated via estimation module  110 , no additional data is required to compute a user&#39;s tax liability as illustrated in operation  2100 . The tax preparation software  100  can then prepare and file (if e-file form) the tax return with the appropriate tax authorities as illustrated in operation  2200 . 
       FIG. 15  illustrates a computing device  102  that is used in one mode to select the various accounts that will be accessed by the data capture utility  43 . In this example, the computing device  102  has a display  104  whereby the user will identify, select, or input her account information for various accounts that will be accessed by the data capture utility  43 . In one optional embodiment, the user will be presented with listing or pull down menu of available sources that be accessed by the data capture utility  43 . The sources may be grouped or otherwise organized by type (e.g., banking accounts, investment accounts, social media accounts, etc.). In the example illustrated in  FIG. 15 , the user has entered her authentication information for a bank account  200 , an investment account  202 , two social media accounts  204 ,  206 , and information pertaining to her employer  208 . 
     As seen in  FIG. 15 , the computing device  102  connected to a remote computing device  103  that is running the tax preparation software  100  via a network connection via a wide area network  205  such as the Internet. In this configuration, the local computing device  102  may connect to the remote computing device  103  using an application or using, for example, a browser utility. In this embodiment, the remote computing device  103  executes the data capture utility  43  which accesses the various online accessible accounts that include data sources  48   a ,  48   b ,  48   c ,  48   d ,  48   e , and  48   f . For example, financial data source  48   a  is accessed to capture tax data obtained from the CHASE banking account  200 . Social media data sources  48   b  are accessed by the data capture utility  43  for the two social media accounts  204 ,  206 . Credit data source  48   c  may be searched without any user authentication provided by the user. The same applies to government agency data source  48   d  and third party data source  48   f . In this example, an employer data source  48   e  is searched by the data capture utility  43  because the user provided information on her employer  208 . 
     Still referring to  FIG. 15 , the data capture utility  43  accesses the data sources  48   a ,  48   b ,  48   c ,  48   d ,  48   e , and  48   f  and collects the relevant financial and/or personal data for the user and transfers this information to the data store  42 . As explained herein, this can be accomplished using screen scraping techniques or specialized bots or crawling programs, or an API and extract this information. In the example illustrated in  FIG. 15 , the data capture utility  43  captures and stores within the data store  42  interest income  210 , capital gains  212 , dividend income  214 , W-2 income  216 , filing status  218 , and also establishes a flag  220  for a possible mortgage deduction for the taxpayer. 
     As one can see from this example, a wide variety of tax-related data is obtained from a number of disparate sources using the data capture utility  43 . This data is now available within the data store  42  such that tax computations can be performed without need for the user to manually enter these entries into the tax software program  100 . In addition, as seen in the example of  FIG. 15 , a flag  220  has been established with respect to whether the user can take advantage of a mortgage deduction. This flag  220  may be set when the data capture utility  43  finds one or more indicia of home ownership. For example, the data capture utility  43  may search the social media data sources  48   b  and find a posting that includes the text “our new home.” 
     The data capture utility  43  may trigger the flag  220  which indicates that there may be one or more tax deductions or credits available to the user that relate to homeownership. In another example, the data capture utility  43  may search a credit data source  48   c  that indicates that the owner has a mortgage associate with her credit report. The flag  220  may be set because of this discovery by the data capture utility  43 . The data capture utility  43  may trigger flags based on text comparisons or searching performed on obtained data from data sources  48 . The data capture utility  43  may also trigger flags  218  based on numerical amounts, codes, and identifiers contained or associated with data in the data sources  48 . For example, a high loan balance may be indicative of a mortgage even though there may be no text associated within the balance in a data source  48 . 
     Still referring to  FIG. 15 , the TLA  60  is illustrated as posing a suggestion  66  to the user based on the flag  220 . In this example, the suggested question is “did you purchase a home this past year” which is presented to the user. This question was prompted by the discovery of data by the data capture utility  43  that would tend to indicate that the user moved into a new home within the current tax year. While the above-example has been given in the context of home ownership it is not so limited. For example, social media data sources  48   b  corresponding to social media accounts  204 ,  206  can be searched to determine whether the user changed jobs during a tax year. For example, the employment history of the user&#39;s LINKED-IN social media account  206  may be mined where dates of employment indicate that the user held multiple jobs during the tax year. The data capture utility  43  can then issue a flag  220  that would alert the software program  100  (e.g., TLA  60 ) that questions about multiple jobs held by the user should be asked to the user. 
       FIG. 16  illustrates another mode of the invention. In this embodiment, the user has an account with personal finance software  250  such as MINT. MINT is an online-based personal finance software program  250  that runs using a remotely accessible computer  252  via a browser or application. In this example, the user is prompted via a user interface  254  as illustrated in  FIG. 16  for the tax preparation software  100  to enter his or her MINT ID and password. Personal financial software such as MINT is already linked to various personal accounts such as banking, investment, property, debt, and the like. In this embodiment, the data capture utility  43  executed by the remote computing device  103  can capture personal financial information from data that is already collected (e.g., scrapped) by the personal finance software  250 . Typically, personal finance software  250  such as MINT will periodically access the designated online accounts to pull account balance information as well as recent transactions which can then be viewed by the user. 
     In this example, the process is streamlined in that the user does not have to enter the ID and password for each account as was done in the mode of  FIG. 15 . Instead, the user enters a login ID and password for the personal finance software program  250  and the data capture utility  43  is able to capture and transfer the relevant data to the data store  42 . Of course, there may be still other data sources  48  that have relevant information that is not already linked or captured by the personal finance software program  250 . In such situations, the user can identify those accounts as was described in the context of  FIG. 15  whereby the data capture utility  43  can then capture and transfer relevant tax data to the data store  42 . In the embodiment of  FIG. 16 , the data capture utility  43  has captured several relevant fields used in computing a tax liability. These include long term capital gains  260 , short term capital gains  262 , mortgage interest  264 , and property taxes  266 . A flag  268  has been set about moving expenses. For example, in this particular example, the data capture utility  43  may have found that during the tax year the user moved residence locations. This may be discovered through data obtained in a credit data source  48   c  or social media data source  48   b . If the change in residence was caused due to job change and otherwise met the required IRS rules, some of these expenses could be deductible. As shown in this example, a proposed question  66  is generated by the TLA  60  to ask the user about whether he or she moved due to a new job. 
       FIGS. 17 and 18  illustrate another embodiment of the invention. In this embodiment, user-related information is collected by a secondary data capture utility  270  (seen in  FIG. 18 ) run on computer  272  that is independent from the data capture utility  43  associated with the tax preparation software program  100 . Other elements similar to that illustrated in  FIG. 16  retain the same element numbers in  FIG. 17 . The secondary data capture utility  270  does not require any input from a user to run or operate and in this regard is independent of any user input. As seen in operation  2500  of  FIG. 17  and illustrated in the system of  FIG. 18 , the secondary data capture utility  270  captures user-related tax data and stores the same in a storage area  274 . As seen in  FIG. 18 , storage area  274  may be accessible by the remote computer  103  such that information that is stored in the storage area  274  may be transferred or copied to the data store  42 . The user-related tax data may be stored, for example, in a database or other storage area known to those skilled in the art. The user-related tax data may be obtained from publicly available data sources  48  or paid data sources such as a third party data source  48   f  or a credit data source  48   c . Publicly available data sources  48  may include, by way of illustration and not limitation, social media data sources  48   b , government agency data sources  48   d , blog posts, web postings, web clippings, and the like. 
     In this embodiment, a tax logic agent  280  operates on the stored data contained in the storage area  274  to determine a degree of completeness for a particular tax return as seen by operation  2600  in  FIG. 17 . The degree of completeness refers to how much data has been gathered by the secondary data capture utility  270  as compared to the total data required to file a complete tax return. For example, in one particular embodiment, the secondary data capture utility  270  may be able to acquire 20% of the data needed to file a completed tax return. This 20% may be based, for example, on the data required to complete a fileable tax return as determined by the completeness graphs  12  as explained herein. In this embodiment, it may be preferably to acquire as much data as possible about the user so as to limit any subsequent manual entry or necessity to enter user account and password information as described in prior embodiments. For example, while 20% was given in the example listed herein, it may be preferable to have even a higher percentage captured by the secondary data capture utility  270 . 
     Still referring to  FIG. 17 , in operation  2700 , the user or potential user is notified of the degree of completeness via a message  276 . For example, a user or potential user may receive an electronic notification such as an e-mail, text, pop-up message, voicemail message, SMS text, proprietary text (e.g., WhatsApp), Tweet using a service such as Twitter, or the like that indicates the degree of completeness that has been achieved by data obtained from the secondary data capture utility  270 . This notification may be communicated by either the remote computer  103  or the computer  272  running the secondary data capture utility  270 . The message  276  may be communicated by a message utility or program that is executed by the remote computer  103  or the computer  272 . The notification may arise as a pop-up message  276  that is seen in the screen of a user that is visiting a website for the tax preparation software  100 . Alternatively, the message  276  may be seen when a user is accessing his or her financial management software. For example, a user or potential user may receive a message  276  as illustrated in  FIG. 18  that says “we completed 28% of your tax return for you, would you like to continue working on your return.” The message  276  does not necessarily have to include a numerical indicator of completeness (e.g., a percentage or other numerical indicator). The message  276  may also just generally describe the degree of completion. For example, the message  276  may say that “some” or “most” of the data required to prepare and file a tax return has already been obtained. This message could be sent to pre-existing users of the tax preparation software  100 . Alternatively, this message could be sent to persons who do not already use tax preparation software  100  and may be a marketing tool to encourage user to purchase or obtain a subscription to use the tax preparation software  100 . For example, a potential user knowing that a portion of his or her tax return is already completed may find such an option particularly attractive. 
     As seen in  FIG. 17 , the process may continue with the operations outlines in  FIG. 12 . For example, the user may purchase or subscribe to the tax preparation software  100  and then use the primary data capture utility  43  to obtain user related tax data that was not already obtained by the secondary data capture utility  270 . Alternatively, the user may already have purchased or subscribed to the tax preparation software  100  and the user may just be notified that a portion of his or her tax return has already been completed as now available now for access. 
     Note that in an alternative embodiment, the data capture utility  43  of the remote computer  103  that contains the tax preparation software  100  may be used in lieu of the secondary data capture utility  270 . In such an alternative embodiment, the data obtained thereby may be stored in the data store  42 . In this alternative embodiment, the computer  272  may be omitted entirely with the remote computer  103  and software contained therein running all operations. In this embodiment, the TLA  60  may be used to determine degree of completeness. 
       FIG. 19  illustrates another embodiment of the system. In this embodiment, which shares certain aspects of the embodiment illustrated in  FIG. 16  and is numbered consistently employs estimation module  110  as described herein that is stored within the data store  42 . In this embodiment, the data capture utility  43  captures data from one or more data sources  48   a - 48   g . In this embodiment, the estimation module  110  is used as described in the  FIG. 8A  such that one or more of the entries in the data store  42  are populated with estimates  290 .  FIG. 19  illustrates an example selection of entries in the data store  42  whereby dividend income and interest income are estimated. The estimates  290  may be based on data obtained from one or more of the data sources  48   a - 48   g . For example, prior return data  48   g  may be analyzed to determine estimated values. Of course, other data sources  48  may also be used. As seen in  FIG. 19  estimates that populate the data store  42  may be used to calculate a degree of completeness as is disclosed in  FIG. 17 . For example, the data capture utility  43  may capture and transfer some data to the data store  42  while other data that populates the data store  42  may be estimated by the estimation module  110 . The user may be provided with a message  292  such as “we estimated or completed 50% of your federal tax return. Would you like to continue?” A user may be able to click on the hyperlinked “continue” that would then execute or open the tax preparation software  100 . 
       FIG. 20  illustrates a flowchart of operations performed by the tax preparation software  100  running on a computing device  102 ,  103  to identify tax topics with high relevance to a user based on user-specific tax history files. Tax topics with a high relevance to the user are those tax topics in which is more likely than not that the user&#39;s current tax return will touch upon that particular tax topic. Of course it should be understood that tax topics may have a range or degrees of relevance to a user. Thus, there may be spectrum of degrees of relevance that vary from low to high. In some embodiments described herein, the cutoff for highly relevant tax topics and less relevant tax topics could be adjusted either upward or downward. For example, a cutoff or threshold of a 75% chance that a particular tax topic applies to a user for a current tax year could be quantified as high relevance in the event the cutoff or threshold is more stringent than what was described previously (e.g., above 50%). 
     In this particular example, as seen in operation  3000 , the software  100  scans either a local resource or a remote resource for user-specific tax history files. For example, the software  100  can scan a local storage device such as a hard drive or flash drive for tax history files. The tax history files may include electronically saved copies of actual tax files or electronic copies of data used for preparation of actual tax returns. For example, the software  100  can scan resources for a .PDF copy or even an image file (.JPEG, etc.) of a prior year tax return. The software  100  can also scan various proprietary file formats (e.g., .tax, .txx (numbers for xx), .ta and the like). There may be multiple years of tax history files that are found or there may be a single year. The software  100  is also able to scan a remote computing device  102 ,  103  for user-specific tax history files. For example, it is increasingly common for software  100  to be run via a browser or application with tax data of a user being stored remotely, for example, in a cloud environment. In this example, the software  100  is able to scan, for example, for prior tax return data that is stored in a cloud environment. This may be accomplished, for example, by providing the user ID and password for a particular tax filing service that has been used in the past. This may be the same or different tax preparation software  100  that is being used for the current year. For example, the software  100  may be able to access tax data from another provider of tax preparation software. The software  100  can access the saved files and/or data that are contained in such files. In one aspect, the data capture utility  43  that was described herein functions as the utility that scans local or remote files for tax history data. 
     Next, as seen in operation  3050 , the software  100  identifies topics with high relevance for the user. The software  100  may identify topics with high relevance in a number of ways. For example, the software  100  may identify those areas or lines within tax forms where a user has entered text or non-zero numbers. For example, the software  100  may identify that capital gains/losses are a topic of high relevance for this user because in two of the past three years the user has filed a Schedule D and has had a capital gain that he includes on his 1040 tax form and/or line 13 of Form  1040  (for tax year 2014) includes a non-zero entry. Thus, relevancy can also take into account into the frequency in which a particular tax topic appears in prior tax reporting periods. The appearance of Schedule D losses in one out of the past seven years may mean that capital gains/losses may not be particularly relevant to this user because of the infrequency in which they appear on filed tax returns/forms. Conversely, if Schedule D income/losses appear with regularity then there is high probability that Schedule D will be needed in a current year tax return. 
     As an alternative to looking at data fields contained in actual tax forms or worksheets, the software  100  might also look to data contained within the electronically stored tax files. For example, proprietary tax files may contain a robust amount of information that includes identifying prior interview questions and responses beyond data that ultimately is presented in a filed tax return. The software  100  is able to extract this information to identify topics with high relevance to the user. For example, the tax file data may include question and answer responses about child care. This may indicate that the tax topics of tax credits or deductions pertaining to child care may be a particular priority to this user. 
     Highly relevant tax topics may also be identified by their impact on a particular tax return. Some tax topics may have large impact on the amount of any refund or tax liability of a taxpayer. For example, for some taxpayers the EITC may change what was otherwise a tax liability into a rather substantial tax refund. The EITC is thus highly relevant to this taxpayer and obtaining the tax data in order to determine whether this credit applies is a priority and interview questions or automatic downloading of data can be geared to quickly and efficiently determine whether the taxpayer is entitled to this credit. 
     Still referring to  FIG. 20 , in operation  3100 , the software  100  generates interview questions on topics with high relevance to the user that were previously identified. As described herein, in some embodiments, interview questions may be presented to the user only on high or highly relevant topics. Those topics that were not identified as being particularly relevant to the user can, optionally, be grouped together and presented to user later, for example, to verify that none of the topics are related to this year&#39;s particular tax filing. In this regard, the user is presented with interview questions that are relevant or germane to the user&#39;s own unique tax situation and is not bothered with having to wade through many other tax topics that may not apply. A common problem that many people have with preparing tax returns using software is that it can become a time consuming process to walk through all potential tax topics, many of which may not apply to the user. Some users may not finish preparing a return and may abandon the process out of frustration. This aspect of the invention produces targeted interview questions on topics that apply to a particular user&#39;s tax situation. Interview questions can be dynamically generated by the user interface manager  82  as described herein. These are typically displayed on a screen or display and require a response by the user to enter data within a field, respond with a yes/no, or select a dialog response (e.g., check a box). 
     Alternatively, or in addition to the interview questions described in operation  3100 , the software  100  may also automatically obtain tax data from one or more remote data sources  48  using the data capture utility  43  as described herein. This is illustrated in operation  3150  in  FIG. 20 . The tax data may be obtained and then automatically stored, for example, in the shared data store  42 . The tax data is obtained from one or more remote data sources  42  that are associated with tax topics with high relevance. For example, if capital gains/losses are a tax topic that has been identified by the software  100  as a topic with high relevance, then the software  100  may automatically try to download data from the user&#39;s brokerage or other financial institution. The data capture utility  43  may be programmed to automatically obtain tax data for data sources  42  linked to particular tax topics. One particular advantage of this method is that resources are not wasted on obtaining data that may be irrelevant to the user. For example, in some cases, obtaining access to a data source  42  may cost money. This money may be paid on behalf of the tax filer by the provider of the tax preparation software  100 . Savings can be achieved when payment is made only towards those data sources  42  that are likely to contain data to highly relevant tax topics. There is no need, for example, to waste money on providing access to a commercial database for tax information if that information has no relevancy to the user. 
     Still referring to  FIG. 20 , as seen in operation  3200 , the software  100  confirms with the user that other topics that were either not automatically obtained or were not covered by interview questions do not apply to the user. These tax topics may be putatively classified as “low relevance” tax topics because the software  100  did not identify them as highly relevant tax topics. Even though these tax topics may be initially deemed to have low relevance the user may still select these topics during the confirmation process. For example, if these “low relevance” tax topics apply to the user then, as seen in operation  3250  the software will then generate interview questions on these topics or automatically obtain this data from one or more data sources  42 . Once all of these remaining topics have been addressed (or if they did not apply during the confirmation or verification process  3200 ), the tax preparation software  100  can then compute tax liability and generate a tax return as seen in operation  3300 . 
       FIG. 21  illustrates an exemplary portion of a tax completion graph  12  with internal nodes  20  with each node representative of a particular tax topic or related to data that is used as part of a tax topic.  FIG. 21  illustrates how identifying topics with high relevance to the user can be used to eliminate certain nodes  20  (i.e., indicated as being crossed out). Thus, once it is confirmed that these topics do not apply to the user, the tax completion graph  12  becomes much simpler from a topological perspective. Once the “low relevance” nodes  20  have been removed from the completion graph  12  the information pertaining to the remaining highly relevant topics as represented by the remaining nodes  20  can be automatically or manually obtained as described above. Notice how completion can be arrived at in a much quicker and efficient fashion by addressing relevant tax topics and then ignoring (after confirmation or verification) those topics that do not apply to the taxpayer. 
       FIG. 22  illustrates a flowchart of operations performed by tax preparation software  100  running on a computing device  102 ,  103  to identify tax topics with high relevant to a user based on a user profile. As illustrated in operation  4000 , the tax preparation software  100  first obtains a user profile of the user (see, e.g., user profile  500  in  FIG. 23 ). The user profile may be obtained by the tax preparation software  100  after a few preliminary questions posed to the user. For example, the user may be asked about his occupation, residence address, tax filing status. Alternatively, the software  100  may automatically assign the user a user profile based on prior year tax data. Based on this information, the tax preparation software  100  establishes a user profile for the user. As seen in operation  4050 , the software  100  then compares this generated user profile with the stored profiles of other tax payers. For example, the provider of tax payer software  100  may have large amounts of data on a large number of users. This data can be mined and analyzed to develop user profiles. The stored user profiles can be associated with highly relevant tax topics as described previously. For example, a user profile A can be established that identifies that a user fitting this profile will likely be asking about the Earned Income Tax Credit. In one embodiment, the user profile that has been established based on an interview process or analysis of prior year tax data is then compared with user profiles of other tax payers. This process is seen in operation  4050  of  FIG. 22 . The user profile is compared with other stored user profiles and identifies, for example, a stored user profile that most closely matches that of the user profile initially generated by the tax preparation software  100 . The stored user profile is associated with one or more topics that are likely to be highly relevant to the user. Based on the matching profiles, the software  100  is able to identify those topics with a high degree of relevance to the user as illustrated in operation  4100 . In one alternative, there is no need to find a matching profile, instead the user profile  500  that is created and the characteristics associated with the user profile  500  are then queried or analyzed by the software  100  to directly identify relevant topics for a particular user. 
     Knowing the topics with high relevance to the user, the software  100  can then generate interview questions as illustrated in operation  4150  or automatically obtain this data in operation  4200 . These operations are identical to those equivalent operations ( 3100 ,  3150 ) in  FIG. 20 . The software  100  will then confirm or verify with the user that the ostensible “low relevance” topics apply or do not apply to the tax payer in the current tax reporting year as illustrated in operation  4250 . If any low relevance topics do apply then the software  100  will present one or more interview questions on these topics or automatically obtain this information from one or more remote data sources  48  as seen in operation  4300  which operates as explained previously. 
     The user profile may act as a “fingerprint” that identifies a particular type of user. This fingerprint can then be used to identify those topics that are likely highly relevant to the user; either by way of a matching profile or directly analyzing the user profile and outputting tax topics that are relevant to the characteristics contained in the user profile.  FIG. 23  illustrates a user profile  500  that has been generated by the tax preparation software  100 . In this example, the user profile  500  has several characteristics associated with it (e.g., at least three). Of course, the more characteristics that are associated with a user profile  500  a higher level of confidence can be attributed to the topics that are deemed highly relevant to the user. The user profile  500  includes the filing status (e.g., single), dependent status as well as number of dependents (e.g., two dependents), employer identification (in this example Walmart), and zip code. The user profile  500  may have been generated by the tax preparation software  100  by asking the user a series of preliminary questions or it may have been automatically generated based on data from one or more prior tax returns. Of course, this user profile  500  is only illustrative and more or less characteristics may be included in the profile. Further, particular combinations of characteristics can be used as a fingerprint to identify relevant topics for a user. 
     Still referring to  FIG. 23 , the user profile  500  is compared with other profiles  504  stored within the database  502  and a matching or similar profile  506  is identified. This matching profile  506  has associated with it one or more topics that are highly correlated with a user having this particular profile  506 . In this example, the topics A12, B24, and D50 are associated with the profile  506 . As an alternative to finding a matching profile  506 , the characteristics contained within the user profile  500  may analyzed by the software program  100  to identify directly the relevant topics (e.g., topics A12, B24, and D50). Next, as seen in operations  508 ,  510  the software  100  generates one or more interview questions related to topics A12, B24, and D50.  FIG. 23  illustrates a computing device  102  where the user is presented with interview questions regarding topic A12 and topic B24. Another example is illustrated where the tax data related to topic A12 is automatically downloaded onto the computing device  102 . Note that the profiles  504  within the database  502  may be constantly updated. For example, while the description has been given that the profiles  504  contained in the database  502  are from prior year returns it is also possible that the database  502  contains tax data from current year tax returns. Thus, the database  502  can be periodically or constantly updated with new profile data so that the identified user profile can be properly associated with the most relevant tax topics. 
       FIG. 24  illustrates an embodiment of how the database  502  operating in conjunction with a computer  518  is updated in real time or near real time and can return to the user a customized interview or, alternatively, automatic downloading of relevant tax data. In this embodiment, a statistical fingerprint  520  is illustrated to for user #1.This statistical fingerprint  520  may be associated with a matched profile  506  or the fingerprint  520  is created by analyzing the characteristics of the user profile  500  as described previously. In this example, a histogram is illustrated to show the relevancy of various tax topics. In this particular example, the ACA and EITC are two highly relevant tax topics for user #1. In this particular embodiment, the software  100  would generate interview questions that are tailored to the ACA and EITC. User #1 would thus receive a custom delivered user interface that asks questions of the most relevant topics and demotes or even ignores tax topics that have no particular application to user #1. Likewise, with reference to  FIG. 24 , user #2 exhibits user profile characteristics that indicate a high degree of relevancy of the child tax credit and retirement contributions. User #2 has a different profile and will have interview questions tailored to include questions regarding any child tax credit or deduction as well as retirement contributions. User #3 has a different profile and for this user has characteristic profile that shows capital gains/losses and deductible contributions as highly relevant to this user. Accordingly, the interview process will be tailored for this user so that questions regarding these topics are presented to the user. Note that the database  502  may be updated in real time such that as more users complete and file taxes additional data is available to analyze to correlate user characteristics with particular tax topics. 
       FIG. 25  illustrates a flowchart of operations performed by tax preparation software  100  running on a computing device  102 ,  103  to identify tax topics with high relevant to a user based on data obtained from a financial management program/service or financial data from an online resource (e.g., financial institution, brokerage, bank, and the like). In this example, topics of relevance to a taxpayer are automatically generated based on information obtained from, for example, the user&#39;s financial management program and more specifically transactions contained or stored as part of the software, program, or application. For example, programs such as QUICKEN, QUICKBOOKS, or MINT track and record very detailed financial data related to owners or account holders. If the user of the software  100  also uses a financial management program, the software  100  may access this data and analyze the transactions as illustrated in operation  6000  of  FIG. 25 . For example, in one aspect of the invention, the data capture utility  43  may transfer data to a repository such as data store  42  although other storage areas may be used as well. Next, as seen in operation  6050 , the tax preparation software  100  identifies the tax topics that have a high degree of relevance to the user based on the transactions. The transactions that are analyzed and form the basis for identifying tax topics may be obtained from the tax year in which the return is being prepared or even prior year data. For example, the software  100  may find a series of monthly, recurring payments to a bank in an amount that is similar to mortgage payment. The software  100  may then make an assumption that the user is a homeowner with a mortgage and thus the mortgage interest deduction will be a relevant tax topic for this user. Of course, this example is only illustrative. The relevancy of particular transactions may be identified by one or more characteristics. The characteristics may include the amount of the transaction(s), the frequency of the transaction(s), the identity of the payee or payer, text that may be included in or associated with the transaction(s), flags paired with the transaction, category associated with the transaction (e.g., mortgage interest). 
     With reference to  FIG. 8B , the topic analysis engine  140  may be used to as part of the software  100  to identify the highly relevant tax topics. The topic analysis engine  140  may identify transactions stored within a data store such as data store  42 , although other data stores can be used. Alternatively, the topic analysis engine  140  may work in conjunction with the data capture utility  43  to directly analyze transactions contained within the various remotely located data sources  48 . In this sense, the data capture utility  43  may scan the transactions work in conjunction with the topic analysis engine  140  to perform on-the-fly analysis of the transactions to identify relevancy of tax topics. 
     In one aspect of the invention, the software  100  will then generate  6100  interview questions on the topics with high relevance based on the review of the transactions. These can then be presented to the user to obtain additional information. Alternatively, the software  100  may import tax data directly from the financial management program as illustrated in operation  6150 . Alternatively, for highly relevant topics, the software  100  may obtain tax data from one or more remote sources  48  as illustrated in operation  6200  and explained herein previously. For example, in the Example given herein where mortgage payments are made to “Bank A” the software  100  may obtain this data using a data capture utility  43 . 
     As with prior embodiments, after the highly relevant tax topics have been addressed either through interviews, downloads, or transfers from another application, the other non-addressed tax topics are confirmed with the user as either applying or not applying as illustrated in operation  6250 . If any of these topics apply, appropriate interview questions will be prepared or the data will be obtained automatically as illustrated in operation  6300 . After these topics are complete or none applied, the software  100  can calculate the tax liability and generate the tax return as seen in operation  6350 . 
       FIG. 26  illustrates how different users (users A, B, C) generate different user profiles  500   a ,  500   b ,  500   c . The different user profiles can be used to develop or generate different tax completion graphs  12   a ,  12   b ,  12   c . Each completion graph corresponds the tax topics applicable to the particular user. Note the illustrate completion graphs eliminate those nodes that pertain to irrelevant tax topics. In some instances, nodes may be added back to these graphs if, for example, a user determines during the verification or confirmation process that one or more previously “low relevance” tax topics actually do apply to the user. In such an instance, those nodes can be added back to the tax completion graphs illustrated in  FIG. 26 . By having tax completion graphs  12   a ,  12   b ,  12   c  already associated with particular user profiles the process of calculating and finalize a tax return becomes much more efficient as the user does not have to make through irrelevant tax topics and interview screens. As seen in  FIG. 26 , with the completion graphs already identified that correspond to a particular profile, the software  100  will generate interview questions or automatically obtain this tax data for the nodes within the corresponding completion graph (corresponding to highly relevant topics) as explained in the prior embodiments. Note how different profiles have different completion graphs  12   a ,  12   b ,  12   c . The software  100  will then calculate the tax liability and generate a tax return. 
       FIG. 27  generally illustrates components of a computing device  102 ,  103  that may be utilized to execute the software for automatically calculating or determining tax liability and preparing an electronic or paper return based thereon. The components of the computing device  102  include a memory  300 , program instructions  302 , a processor or controller  304  to execute program instructions  302 , a network or communications interface  306 , e.g., for communications with a network or interconnect  308  between such components. The computing device  102 ,  103  may include a server, a personal computer, laptop, tablet, mobile phone, or other portable electronic device. The memory  300  may be or include one or more of cache, RAM, ROM, SRAM, DRAM, RDRAM, EEPROM and other types of volatile or non-volatile memory capable of storing data. The processor unit  304  may be or include multiple processors, a single threaded processor, a multi-threaded processor, a multi-core processor, or other type of processor capable of processing data. Depending on the particular system component (e.g., whether the component is a computer or a hand held mobile communications device), the interconnect  308  may include a system bus, LDT, PCI, ISA, or other types of buses, and the communications or network interface may, for example, be an Ethernet interface, a Frame Relay interface, or other interface. The interface  306  may be configured to enable a system component to communicate with other system components across a network which may be a wireless or various other networks. It should be noted that one or more components of the computing device  102 ,  103  may be located remotely and accessed via a network. Accordingly, the system configuration illustrated in  FIG. 27  is provided to generally illustrate how embodiments may be configured and implemented. 
     Method embodiments may also be embodied in, or readable from, a computer-readable medium or carrier, e.g., one or more of the fixed and/or removable data storage data devices and/or data communications devices connected to a computer. Carriers may be, for example, magnetic storage medium, optical storage medium and magneto-optical storage medium. Examples of carriers include, but are not limited to, a floppy diskette, a memory stick or a flash drive, CD-R, CD-RW, CD-ROM, DVD-R, DVD-RW, or other carrier now known or later developed capable of storing data. The processor  304  performs steps or executes program instructions  302  within memory  300  and/or embodied on the carrier to implement method embodiments. 
     Embodiments, however, are not so limited and implementation of embodiments may vary depending on the platform utilized. Accordingly, embodiments are intended to exemplify alternatives, modifications, and equivalents that may fall within the scope of the claims.