In one embodiment, a computer-implemented method for generating explanations for a tax calculation or tax operation is performed by tax preparation software. The method includes a computing device executing a tax calculation engine located or contained in connection with the tax preparation software, the tax calculation engine operating on a tax calculation graph to perform a tax calculation, the tax calculation graph semantically describing data dependent tax operations comprising functional nodes connected to input nodes by one of a plurality of functions, wherein each tax operation is associated with one or more explanations. The computing device executes an explanation engine in connection with the tax preparation software to generate a narrative explanation from the one or more explanations associated with one of the tax operations and the computing device presents the narrative explanation to the user on a computing device.
In another aspect of the invention, a user may be able to drill down to find even more detailed explanations or answers to a tax topic or the reasoning or rationale behind a particular tax computation or process. The computing device executes the explanation engine to generate narrative explanations from one or more predecessor tax operations in response to a user request or automatically as a user advances through the tax preparation software. The predecessor tax operations are found further upstream within the tax calculation graph.
In some embodiments, the level of detail visible to a user may be tied to a particular product type (e.g., SKU code or the like). In still another alternative, the level of detail visible to a user may be tied to a particular user type. For example, a more basic user may be shown a bare-bones or summary explanation. A more skilled user may be shown more detailed explanations or may be offered additional level of detail that is otherwise unavailable to other users. In still another embodiment, a user may be able to upgrade or purchase additional explanations.
In one aspect of the invention, the tax calculation graph semantically describes data dependent tax operations comprising functional nodes connected to input nodes by one of a plurality of functions. The functions that are used to generate explanations may be found in a common data store and library and are used throughout the tax calculation graphs. Certain functions may be more common than others and shared in various nodes of the tax calculation graph. This sharing of functions aids in programming and creating of tax calculation graphs that are utilized as part of the tax preparation software.
The computing device may automatically generate a narrative explanation that is displayed to a user or the narrative may be generated in response to a user request. In one aspect of the invention, the narrative explanation that is presented to the user is a natural language expression that is generated by an explanation engine that is executed as part of the tax preparation software.
In another embodiment, a computer-implemented system is provided for generating explanations for a tax calculation or tax operation performed by tax preparation software. The system includes a computing device executing a tax calculation engine in connection with the tax preparation software, the tax calculation engine operating on a tax calculation graph to perform a tax calculation, the tax calculation graph semantically describing data dependent tax operations comprising functional nodes connected to input nodes by one of a plurality of functions, wherein each tax operation is associated with one or more explanations. The system includes a data store operatively coupled to the computing device and containing the one or more explanations. The computing device executes an explanation engine in connection with the tax preparation software to generate a narrative explanation from the one or more explanations associated with one of the tax operations. A display operatively connected to the computing device is provided for displaying the narrative explanation to the user.
Tax preparation is a time-consuming and laborious process. It is estimated that individuals and businesses spend around 6.1 billion hours per year complying with the filing requirements of the Internal Revenue Code. 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's tax liability.
In contrast to the rigidly defined user interface screens used in prior iterations of tax preparation software, more recent iterations provide tax preparation software 100 that runs on computing devices 102, 103 (as seen in
According to one aspect of the invention, a computer-implemented method is provided that generates explanations for tax calculations or tax operations performed by tax preparation software. The explanations that are generated by the computer may be automatically generated as data is input or otherwise loaded into the tax preparation software. Explanations may also be generated or presented after a request by a user. The explanations are in easy to understand and may be presented in a narrative form. The explanations may be generated with varying levels of complexity depending on the user type, product type, or user credentials. The explanations are generated based on explanations associated with tax operations contained in the tax calculation graphs.
Note that in
The completeness graph 12 and the tax calculation graph 14 represent data structures that can be constructed in the form of tree.
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 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.
As explained herein, the directed graph or completion graph 12 that is illustrated in
Referring to
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 32a and 32b 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 QC and QG in columns 34c, 34g, respectively. One of these questions is selected and the process repeats until either the goal 34h is reached or there is an empty candidate list.
In
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 still other embodiments, values for leaf nodes 24 may be estimated as described herein.
Still other internal nodes referred to as functional nodes 26 semantically represent a tax concept and may be calculated or otherwise determined using a function 28. The functional node 26 and the associated function 28 define a particular tax operation 29. For example, as seen in
Interconnected function nodes 26 containing data dependent tax concepts or topics are associated with a discrete set of functions 28 that are used to capture domain specific patterns and semantic abstractions used in the tax calculation. The discrete set of functions 28 that are associated with any particular function node 26 are commonly reoccurring operations for functions that are used throughout the process of calculating tax liability. For example, examples of such commonly reoccurring functions 28 include copy, capping, thresholding (e.g., above or below a fixed amount), accumulation or adding, look-up operations (e.g., look-up tax tables), percentage of calculation, phase out calculations, comparison calculations, exemptions, exclusions, and the like.
In one embodiment, the entire set of functions 28 that is used to compute or calculate a tax liability is stored within a data store 30 which in some instances may be a database. The various functions 28 that are used to semantically describe data connections between function nodes 26 can be called upon by the tax preparation software 100 for performing tax calculations. Utilizing these common functions 28 greatly improves the efficiency of the tax preparation software 100 can be used by programmer to more easily track and follow the complex nature of the ever-evolving tax code. The common functions 28 also enables easier updating of the tax preparation software 100 because as tax laws and regulations change, fewer changes need to be made to the software code as compared to prior hard-wired approaches.
Importantly, the tax calculation graph 14 and the associated function nodes 26 and functions 28 can be tagged and later be used or called upon to intelligently 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. The functions 28 can be de-coupled from a specific narrow definition and instead be associated with one or more explanations. Examples of common functions 28 found in tax legislation and tax rules include the concepts of “caps” or “exemptions” that are found in various portions of the tax code. One example of a “cap” is the portion of the U.S. tax code that limits the ability of a joint filer to deduct more than $3,000 of net capital losses in any single tax year. There are many other instances of such caps. An example of an “exemption” is one that relates to early distributions from retirement plants. For most retirement plans, early distributions from qualified retirement plans prior to reaching the age of fifty nine and one-half (59½) require a 10% penalty. This penalty can be avoided, however, if an exemption applies such as the total and permanent disability of the participant. Other exemptions also apply. Such exemptions are found throughout various aspects of the tax code and tax regulations.
In some embodiments, the function 28 may also include any number of mathematical or other operations. Examples of functions 28 include summation, subtraction, multiplication, division, and comparisons, greater of, lesser of, at least one of, calling of look-ups of tables or values from a database 30 or library as is illustrated in
Still referring to
As seen in
In the event there is a penalty, the ACA requires that the penalty be the greater of a percentage of income, net of specified deductions, or a specified penalty that is applied per individual or family. For example, for the 2015 year, the percentage is 2.0 percent and increases to 2.5 percent in subsequent years.
In order to determine the non-income or “fixed” penalty, an accumulator function 28i is used to determine the penalty. In this example, the calculation pertains to a family wherein the penalty includes a fixed amount for a child ($162.50 per child in 2015) and a fixed amount per adult ($325.00 per adult). Under the ACA, there is a maximum cap of this fixed penalty. For example, in 2015, the maximum family penalty is $975. As seen in
As seen in
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 that utilizes additional fields. 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, and a response to a Boolean expression (e.g., True/False or Yes/No). As explained in more detail, the shared 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,
As seen in
User input 48a is also one type of data source 48. User input 48a may take a number of different forms. For example, user input 48a 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), photograph or image, or the like to enter information manually into the tax preparation software 100. For example, as illustrated in
User input 48a 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 shared data store 42.
Another example of a data source 48 is a prior year tax return 48b. A prior year tax return 48b that is stored electronically can be searched and data is copied and transferred to the shared data store 42. The prior year tax return 48b may be in a proprietary format (e.g., .txf, .pdf) or an open source format. The prior year tax return 48b may also be in a paper or hardcopy format that can be scanned or imaged whereby data is extracted and transferred to the shared data store 42. In another embodiment, a prior year tax return 48b may be obtained by accessing a government database (e.g., IRS records).
An additional example of a data source 48 is an online resource 48c. An online resource 48c may include, for example, websites for the taxpayer(s) that contain tax-related information. For example, 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 shared data store 42.
Of course, there are additional examples of online resources 48c beyond financial service providers. 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's may post or store personal information on these properties that may have tax implications. For example, a user's Linked-In account 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. This information is then acquired and transferred to the shared data store 42, which can 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.
Still referring to
Referring briefly to
Still referring to
As seen in
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;
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
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
Still referring to
The TLA 60 also outputs a tax data that is used to generate the actual tax return (either electronic return or paper return). The return itself can be prepared by the TLA 60 or at the direction of the TLA 60 using, for example, the services engine 60 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). Whether a paper or electronic return is filed, data from the shared 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.
By using calculation graphs 14 to drive tax calculations and tax operations, the year-over-year calculation graphs 14 can be used to readily identify differences and report the same to a user. Differences can be found using commonly used graph isomorphism algorithms over the two respective calculation graphs 14.
Still referring to
These stored entries 112 can be recalled or extracted by the explanation engine 110 and then displayed to a user on a display 104 of a computing device 102, 103. For example, explanation engine 110 may interface with the UI control 80 in two-way communication such that a user may be ask the tax preparation software 100 why a particular tax calculation, operation, or decision has been made by the system 40. For instance, the user may be presented with an on-screen link (
With reference to
In one aspect of the invention, the choice of what particular explanation will be displayed to a user may vary. For example, different explanations associated with the same function node 26 and function 28 pair may be selected by the explanation engine 110 for display to a user based on the user's experience level. A basic user may be given a general or summary explanation while a user with more sophistication may be given a more detailed explanation. A professional user such as a CPA or other tax specialist may be given even more detailed explanations.
In some embodiments, the different levels of explanation may be tied to product types or codes. These may be associated with, for example, SKU product codes. For example, a free edition of the tax preparation software 100 may little or no explanations. In a more advanced edition (e.g., “Deluxe edition”), additional explanation is provided. Still more explanation may be provided in the more advanced editions of the tax preparation software 100 (e.g., “Premier edition”). Version of the tax preparation software 100 that are developed for accountants and CPAs may provide even more explanation.
In still other embodiments a user may be able to “unlock” additional or more detailed explanations by upgrading to a higher edition of tax preparation software 100. Alternatively, a user may unlock additional or more detailed explanations in an a la carte manner for payment of an additional fee. Such a fee can be paid through the tax preparation software 100 itself using known methods of payment.
In one aspect of the invention, the natural language generator 114 may rely on artificial intelligence or machine learning such that results may be improved. For example, the explanation engine 110 may be triggered in response to a query that a user has typed into a free-form search box within the tax preparation software 100. The search that has been input within the search box can then be processed by the explanation engine 110 to determine what tax operation the user is inquiring about and then generate an explanatory response 115B.
As seen in
The narrative explanations 116 and their associated sub-explanations (e.g., 116′, 116a, 116b, 116d, 116e) are constructed as an explanation tree with the root of the tree representing a particular tax topic or tax operation. In the example of
By encapsulating the tax code and regulations within calculation graphs 14 results in much improved testability and maintainability of the tax preparation software 100. Software bugs can be identified more easily when the calculation graphs 14 are used because such bugs can be traced more easily. In addition, updates to the calculation graphs 14 can be readily performed when tax code or regulations change with less effort.
Further, the degree of granularity in the narrative explanations 116 that are presented to the user can be controlled. As explained in the context of
Note that one can traverse the calculation graph 14 in any topologically sorted order. This includes starting at a leaf or other input node and working forward through the calculation graph 14. Alternatively, one can start at the final or terminal node and work backwards. One can also start at in intermediate node and traverse through the directed graph in any order. By capturing the tax code and tax regulations in the calculation graph, targeted calculations can be done on various tax topics or sub-topics. For example,
A user initiates the tax preparation software 100 on a computing device 102, 103 as seen, for example, in
A user initiating the tax preparation software 100, as explained herein may import tax related information form one or more data sources 48. Tax data may also be input manually with user input 48a. The tax calculation engine 50 computes one or more tax calculations dynamically based on the then available data at any given instance within the schema 44 in the shared data store 42. In some instances, estimates or educated guesses may be made for missing data. Details regarding how such estimates or educated guesses are done maybe found in U.S. patent application Ser. No. 14/448,986 which is incorporated by reference as if set forth fully herein. As the tax preparation software 100 is calculating or otherwise performing tax operations, the explanation engine 110 is executing or made available to execute and provide to the user one or more narrative explanations regarding calculations or operations being performed as referenced by particular functional nodes 26 and functions 28 contained within the calculation graph 14. As noted herein, in some instances, the narrative explanations are provided automatically to the UI control 80. In other instances, explanations are provided by the explanation engine 110 upon request of a user. For example, a user may request explanations on an as-needed basis by interfacing with the tax preparation software 100.
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.
This application is a continuation of U.S. patent application Ser. No. 14/530,159 filed Oct. 31, 2014, now U.S. Pat. No. 10,169,826 entitled “System and Method for Generating Explanations for Tax Calculations”, the contents and disclosure of which are incorporated herein in their entirety.
Number | Name | Date | Kind |
---|---|---|---|
4213251 | Foundos | Jul 1980 | A |
4809219 | Ashford et al. | Feb 1989 | A |
5006998 | Yasunobu | Apr 1991 | A |
5495607 | Pisello et al. | Feb 1996 | A |
5557761 | Chan et al. | Sep 1996 | A |
5673369 | Kim | Sep 1997 | A |
5742836 | Turpin et al. | Apr 1998 | A |
5819249 | Dohanich | Oct 1998 | A |
6078898 | Davis | Jun 2000 | A |
6535883 | Lee et al. | Mar 2003 | B1 |
6601055 | Roberts | Jul 2003 | B1 |
6631361 | D'Flaherty et al. | Oct 2003 | B1 |
6670969 | Halstead et al. | Dec 2003 | B1 |
6690854 | Helbing | Feb 2004 | B2 |
6697787 | Miller | Feb 2004 | B1 |
6898573 | Piehl | May 2005 | B1 |
6910003 | Arnold | Jun 2005 | B1 |
6912508 | McCalden | Jun 2005 | B1 |
7062466 | Wagner | Jun 2006 | B2 |
7234103 | Regan | Jun 2007 | B1 |
7295998 | Kulkarni | Nov 2007 | B2 |
7331045 | Martin et al. | Feb 2008 | B2 |
7448022 | Ram et al. | Nov 2008 | B1 |
7539635 | Peak et al. | May 2009 | B1 |
7565312 | Shaw | Jul 2009 | B1 |
7603301 | Regan | Oct 2009 | B1 |
7668763 | Albrecht | Feb 2010 | B1 |
7680756 | Quinn | Mar 2010 | B2 |
7685082 | Coletta | Mar 2010 | B1 |
7693760 | Fiteni | Apr 2010 | B1 |
7693769 | Burlison et al. | Apr 2010 | B1 |
7716094 | Sutter | May 2010 | B1 |
7725408 | Lee | May 2010 | B2 |
7742958 | Leek | Jun 2010 | B1 |
7747484 | Stanley | Jun 2010 | B2 |
7761333 | Kapp | Jul 2010 | B2 |
7778895 | Baxter | Aug 2010 | B1 |
7809605 | Tonse | Oct 2010 | B2 |
7818222 | Allanson et al. | Oct 2010 | B2 |
7849405 | Coletta | Dec 2010 | B1 |
7860763 | Quinn et al. | Dec 2010 | B1 |
7865829 | Goldfield | Jan 2011 | B1 |
7895102 | Wilks et al. | Feb 2011 | B1 |
7899757 | Talan | Mar 2011 | B1 |
7900298 | Char et al. | Mar 2011 | B1 |
7908190 | Enenkiel | Mar 2011 | B2 |
7912767 | Cheatham | Mar 2011 | B1 |
7912768 | Abeles | Mar 2011 | B2 |
7925553 | Banks | Apr 2011 | B2 |
8001006 | Yu et al. | Aug 2011 | B1 |
8019664 | Tifford et al. | Sep 2011 | B1 |
8082144 | Brown et al. | Dec 2011 | B1 |
8086970 | Achtermann et al. | Dec 2011 | B2 |
8108258 | Slattery | Jan 2012 | B1 |
8126820 | Talan | Feb 2012 | B1 |
8156025 | Gymer | Apr 2012 | B1 |
8190499 | McVickar | May 2012 | B1 |
8191152 | Barker | May 2012 | B1 |
8204805 | Eftekhari | Jun 2012 | B2 |
8219409 | Vetterli | Jul 2012 | B2 |
8224726 | Murray | Jul 2012 | B2 |
8234562 | Evans | Jul 2012 | B1 |
8244607 | Quinn | Aug 2012 | B1 |
8321312 | Allanson | Nov 2012 | B2 |
8346635 | Ohm | Jan 2013 | B1 |
8346680 | Castleman | Jan 2013 | B2 |
8370795 | Sage | Feb 2013 | B1 |
8386344 | Christina | Feb 2013 | B2 |
8407113 | Eftekhari et al. | Mar 2013 | B1 |
8417596 | Dunbar et al. | Apr 2013 | B1 |
8417597 | McVickar | Apr 2013 | B1 |
8423433 | Miller | Apr 2013 | B1 |
8447667 | Dinamani et al. | May 2013 | B1 |
8452676 | Talan | May 2013 | B1 |
8463676 | Dinamani et al. | Jun 2013 | B1 |
8473880 | Bennett et al. | Jun 2013 | B1 |
8478671 | Tifford | Jul 2013 | B1 |
8510187 | Dinamani | Aug 2013 | B1 |
8521538 | Laughery | Aug 2013 | B2 |
8527375 | Ohm | Sep 2013 | B1 |
8560409 | Abeles | Oct 2013 | B2 |
8577760 | Rutsch | Nov 2013 | B2 |
8583516 | Pitt et al. | Nov 2013 | B1 |
8589262 | Wang | Nov 2013 | B1 |
8607353 | Ripped et al. | Dec 2013 | B2 |
8612318 | Blowers | Dec 2013 | B1 |
8635127 | Shaw | Jan 2014 | B1 |
8639616 | Rolenaitis | Jan 2014 | B1 |
8682756 | Tifford | Mar 2014 | B1 |
8682829 | Barthel | Mar 2014 | B2 |
8694395 | Houseworth | Apr 2014 | B2 |
8706580 | Houseworth | Apr 2014 | B2 |
8788412 | Hamm | Jul 2014 | B1 |
8812380 | Murray | Aug 2014 | B2 |
8813178 | Khanna | Aug 2014 | B1 |
8838492 | Baker | Sep 2014 | B1 |
8892467 | Ball | Nov 2014 | B1 |
8924269 | Seubert et al. | Dec 2014 | B2 |
8949270 | Newton et al. | Feb 2015 | B2 |
9105262 | Shih | Aug 2015 | B2 |
9117118 | Lewis | Aug 2015 | B1 |
9372687 | Pai | Jun 2016 | B1 |
9406089 | Mori | Aug 2016 | B2 |
9454965 | Schubert | Sep 2016 | B2 |
9524525 | Manyam | Dec 2016 | B2 |
9690854 | Stent | Jun 2017 | B2 |
9760953 | Wang et al. | Sep 2017 | B1 |
9805723 | Roy | Oct 2017 | B1 |
9916627 | Huang | Mar 2018 | B1 |
9916628 | Wang et al. | Mar 2018 | B1 |
9922376 | Wang et al. | Mar 2018 | B1 |
9990678 | Cabrera et al. | Jun 2018 | B1 |
10096072 | Ali | Oct 2018 | B1 |
10140666 | Wang | Nov 2018 | B1 |
10157426 | Wang | Dec 2018 | B1 |
10169826 | Wang | Jan 2019 | B1 |
10176534 | Mascaro | Jan 2019 | B1 |
10204382 | Morin | Feb 2019 | B2 |
20020022986 | Coker | Feb 2002 | A1 |
20020023064 | Grimse | Feb 2002 | A1 |
20020065831 | DePaolo | May 2002 | A1 |
20020107698 | Brown et al. | Aug 2002 | A1 |
20020111888 | Stanley et al. | Aug 2002 | A1 |
20020133410 | Hermreck et al. | Sep 2002 | A1 |
20020174017 | Singh | Nov 2002 | A1 |
20020198832 | Agee | Dec 2002 | A1 |
20030101070 | Mahosky et al. | May 2003 | A1 |
20030126054 | Purcell | Jul 2003 | A1 |
20030139827 | Phelps | Jul 2003 | A1 |
20030174157 | Hellman | Sep 2003 | A1 |
20030182102 | Corston-Oliver et al. | Sep 2003 | A1 |
20040002906 | Von Drehnen et al. | Jan 2004 | A1 |
20040019540 | William | Jan 2004 | A1 |
20040019541 | William | Jan 2004 | A1 |
20040021678 | Ullah et al. | Feb 2004 | A1 |
20040078271 | Morano | Apr 2004 | A1 |
20040083164 | Schwartz et al. | Apr 2004 | A1 |
20040088233 | Brady | May 2004 | A1 |
20040117395 | Gong | Jun 2004 | A1 |
20040172347 | Barthel | Sep 2004 | A1 |
20040181543 | Wu et al. | Sep 2004 | A1 |
20040205008 | Haynie et al. | Oct 2004 | A1 |
20040230397 | Chadwick | Nov 2004 | A1 |
20050171822 | Cagan | Aug 2005 | A1 |
20050216379 | Ozaki | Sep 2005 | A1 |
20050228685 | Schuster | Oct 2005 | A1 |
20050262191 | Mamou et al. | Nov 2005 | A1 |
20060041428 | Fritsch | Feb 2006 | A1 |
20060112114 | Yu | May 2006 | A1 |
20060155618 | Wyle | Jul 2006 | A1 |
20060155632 | Cherkas et al. | Jul 2006 | A1 |
20060178961 | Stanley et al. | Aug 2006 | A1 |
20060282354 | Varghese | Dec 2006 | A1 |
20060293990 | Schaub | Dec 2006 | A1 |
20070033116 | Murray | Feb 2007 | A1 |
20070033117 | Murray | Feb 2007 | A1 |
20070033130 | Murray | Feb 2007 | A1 |
20070055571 | Fox et al. | Mar 2007 | A1 |
20070094207 | Yu et al. | Apr 2007 | A1 |
20070136157 | Neher, III | Jun 2007 | A1 |
20070150347 | Bhamidipati | Jun 2007 | A1 |
20070150387 | Seubert et al. | Jun 2007 | A1 |
20070156564 | Humphrey et al. | Jul 2007 | A1 |
20070179841 | Agassi | Aug 2007 | A1 |
20070192166 | Van Luchene | Aug 2007 | A1 |
20070250418 | Banks et al. | Oct 2007 | A1 |
20080017722 | Snyder | Jan 2008 | A1 |
20080059900 | Murray | Mar 2008 | A1 |
20080097878 | Abeles | Apr 2008 | A1 |
20080126170 | Leck et al. | May 2008 | A1 |
20080147494 | Larson | Jun 2008 | A1 |
20080154873 | Redlich | Jun 2008 | A1 |
20080162310 | Quinn | Jul 2008 | A1 |
20080177631 | William | Jul 2008 | A1 |
20080189197 | Allanson et al. | Aug 2008 | A1 |
20080215392 | Rajan | Sep 2008 | A1 |
20080243531 | Hyder et al. | Oct 2008 | A1 |
20090024694 | Fong | Jan 2009 | A1 |
20090037305 | Sander | Feb 2009 | A1 |
20090037847 | Achtermann et al. | Feb 2009 | A1 |
20090048957 | Celano | Feb 2009 | A1 |
20090064851 | Morris et al. | Mar 2009 | A1 |
20090117529 | Goldstein | May 2009 | A1 |
20090125618 | Huff | May 2009 | A1 |
20090138389 | Barthel | May 2009 | A1 |
20090150169 | Kirkwood | Jun 2009 | A1 |
20090157572 | Chidlovskii | Jun 2009 | A1 |
20090192827 | Andersen | Jul 2009 | A1 |
20090193389 | Miller | Jul 2009 | A1 |
20090204881 | Murthy | Aug 2009 | A1 |
20090239650 | Alderucci et al. | Sep 2009 | A1 |
20090248594 | Castleman | Oct 2009 | A1 |
20090248603 | Kiersky | Oct 2009 | A1 |
20100036760 | Abeles | Feb 2010 | A1 |
20100088124 | Diefendort et al. | Apr 2010 | A1 |
20100100561 | Cooper | Apr 2010 | A1 |
20100131394 | Rutsch et al. | May 2010 | A1 |
20100153114 | Shih | Jun 2010 | A1 |
20100153138 | Evans | Jun 2010 | A1 |
20100169309 | Barrett | Jul 2010 | A1 |
20100169359 | Barrett | Jul 2010 | A1 |
20110004537 | Allanson et al. | Jan 2011 | A1 |
20110071854 | Medeiros | Mar 2011 | A1 |
20110078062 | Kleyman | Mar 2011 | A1 |
20110087703 | Varadarajan | Apr 2011 | A1 |
20110145112 | Abeles | Jun 2011 | A1 |
20110173222 | Sayal et al. | Jul 2011 | A1 |
20110225220 | Huang et al. | Sep 2011 | A1 |
20110258195 | Welling | Oct 2011 | A1 |
20110258213 | Pollara | Oct 2011 | A1 |
20110258610 | Aaraj et al. | Oct 2011 | A1 |
20110264569 | Houseworth et al. | Oct 2011 | A1 |
20110284213 | Kowalewski | Nov 2011 | A1 |
20110285213 | Kowalewski | Nov 2011 | A1 |
20120016817 | Smith et al. | Jan 2012 | A1 |
20120027246 | Tifford | Feb 2012 | A1 |
20120030076 | Checco et al. | Feb 2012 | A1 |
20120030577 | Akolkar et al. | Feb 2012 | A1 |
20120053965 | Hellman | Mar 2012 | A1 |
20120072321 | Christian et al. | Mar 2012 | A1 |
20120109792 | Eftekhari et al. | May 2012 | A1 |
20120109793 | Abeles | May 2012 | A1 |
20120136764 | Miller | May 2012 | A1 |
20120278365 | Labat et al. | Nov 2012 | A1 |
20120304056 | Detlef | Nov 2012 | A1 |
20130030839 | Opfer | Jan 2013 | A1 |
20130036347 | Eftekhari | Feb 2013 | A1 |
20130080302 | Allanson et al. | Mar 2013 | A1 |
20130097262 | Dandison | Apr 2013 | A1 |
20130111032 | Alapati et al. | May 2013 | A1 |
20130138586 | Jung et al. | May 2013 | A1 |
20130185347 | Romano | Jul 2013 | A1 |
20130187926 | Silverstein et al. | Jul 2013 | A1 |
20130198047 | Houseworth | Aug 2013 | A1 |
20130218735 | Murray | Aug 2013 | A1 |
20130262279 | Finley et al. | Oct 2013 | A1 |
20130282539 | Murray | Oct 2013 | A1 |
20130290169 | Bathula | Oct 2013 | A1 |
20140058763 | Zizzamia | Feb 2014 | A1 |
20140067949 | Dearlove | Mar 2014 | A1 |
20140108213 | Houseworth | Apr 2014 | A1 |
20140149303 | Band | May 2014 | A1 |
20140172656 | Shaw | Jun 2014 | A1 |
20140201045 | Pat et al. | Jul 2014 | A1 |
20140207633 | Aldrich et al. | Jul 2014 | A1 |
20140241631 | Huang | Aug 2014 | A1 |
20140244455 | Huang | Aug 2014 | A1 |
20140244457 | Howell et al. | Aug 2014 | A1 |
20140337189 | Barsade et al. | Nov 2014 | A1 |
20150066715 | Carrier | Mar 2015 | A1 |
20150142703 | Rajesh | May 2015 | A1 |
20150237205 | Waller et al. | Aug 2015 | A1 |
20150254623 | Velez et al. | Sep 2015 | A1 |
20150269491 | Tripathi et al. | Sep 2015 | A1 |
20160027127 | Chavarria et al. | Jan 2016 | A1 |
20160063645 | Houseworth et al. | Mar 2016 | A1 |
20160071112 | Unser | Mar 2016 | A1 |
20160078567 | Goldman et al. | Mar 2016 | A1 |
20160092993 | Ciaramitaro | Mar 2016 | A1 |
20160092994 | Roebuck et al. | Mar 2016 | A1 |
20160098804 | Mascaro et al. | Apr 2016 | A1 |
20160148321 | Ciaramitaro et al. | May 2016 | A1 |
20160162456 | Munro | Jun 2016 | A1 |
20160162459 | Parker | Jun 2016 | A1 |
20160247239 | Houseworth | Aug 2016 | A1 |
20160275627 | Wang | Sep 2016 | A1 |
20160283353 | Owen | Sep 2016 | A1 |
20170004583 | Wang | Jan 2017 | A1 |
20170004584 | Wang | Jan 2017 | A1 |
20170032468 | Wang et al. | Feb 2017 | A1 |
20170046492 | Renner | Feb 2017 | A1 |
20180032855 | Wang et al. | Feb 2018 | A1 |
Number | Date | Country |
---|---|---|
2002-117121 | Apr 2002 | JP |
2005-190425 | Jul 2005 | JP |
2014-206960 | Oct 2014 | JP |
10-2012-0011987 | Feb 2012 | KR |
WO 2017004094 | Jan 2017 | WO |
WO 2017004095 | Jan 2017 | WO |
WO 2017019233 | Feb 2017 | WO |
WO 2017116496 | Jul 2017 | WO |
WO 2017116497 | Jul 2017 | WO |
WO 2018022023 | Feb 2018 | WO |
WO 2018022128 | Feb 2018 | WO |
WO 2018080562 | May 2018 | WO |
WO 2018080563 | May 2018 | WO |
Entry |
---|
“Ways to Increase your tax refund you never thought about”, Intuit Turboax (Year: 2017). |
U.S. Appl. No. 14/553,347, filed Nov. 25, 2014. |
U.S. Appl. No. 14/530,159, filed Oct. 31, 2014. |
English Language Abstract of KR 10-2012-0011987 published Feb. 9, 2012. |
International Search Report issued in PCT/US2016/067867 dated Jul. 26, 2017, 5 pages. |
Written Opinion issued in PCT/US2016/067867 dated Jul. 26, 2017, 9 pages. |
“Ways to Increase your Tax Refund you Never Thought About”, Intuit TurboTax (Year: 2017), 1 page. |
www.turbotax.com, printed Mar. 11, 2014, 7 pages. |
Cronin, Julie-Anne et al., Distributing the Corporate Income Tax: Revised U.S. Treasury Methodology, May 2012, Department of Treasury, web, 2-31 (2012), 34 pages. |
H.R. Gregg; Decision Tables for Documentation and System Analysis; Oct. 3, 1967; Union Carbide Corporation, Nuclear Division, Computing Technology Center: (Year: 1967) 25 pages. |
hittp://en.wikipedia.org/wiki/Dependency_grammar#Semantic_dependencies, printed Mar. 11, 2014, 15 pages. |
hittp://en.wikipedia.org/wiki/Loose_coupling, printed Mar. 11, 2014, 2 pages. |
hittp://www.webopedia.corniTERM/Uloose_coupling.html, printed Mar. 11, 2014, 4 pages. |
http://doc,jboss.org/drools/release/5.3.0.Final/drools-expert-docs/html/ch01.html, printed March 11, 2014, 10 pages. |
http://en.wikipedia.org/wiki/Declarative_programming, printed Mar. 11, 2014, 4 pages. |
http://en.wikipedia.org/wiki/Drools, printed Mar. 11, 2014, 4 pages. |
http://quicken.intuit.com/support/help/income-and-expenses/how-to-assign-tax-form-line-items-to-a-category/GEN82142.html, updated Aug. 11, 2011, printed Jun. 24, 2014, 2 pages. |
http://quicken.intuit.com/support/help/reports--graphs-and-snapshots/track-the-earnings-taxes--deductions--or-deposits-from-paychecks/GEN82101.html, updated May 14, 2012, printed Jun. 24, 2014, 2 pages. |
http://quicken.intuit.com/support/help/tax-savings/simplify-tax-time/INF24047.html, updated Jul. 25, 2013, printed Jun. 24, 2014, 11 pages. |
http://www.wisegeek.com/what-is-declarative-programming.htm, printed Mar. 11, 2014, 2 pages. |
http://www.jboss.org/drools/drools-expert.html, printed Mar. 11, 2014, 5 pages. |
https://turbotax.intuit.com/snaptax/mobile/, printed Mar. 11, 2014, 2 pages. |
NY State Dep of Taxation, NY State Personal Income Tax MeF Guide for Software Developers, 2012, NY State, 30 pages. |
Openrules, Preparing a Tax Return Using OpenRules Dialog, Aug. 2011 (2011), 25 pages. |
Solomon L. Pollack; Analysis of the Decision Rules in Decision Tables, May 1963; The Rand Corporation; (1963), 78 Pages. |
Vanderbilt University, “Free tax prep help available for Vanderbilt employees”, Feb. 6, 2014, Vanderbilt University, 3 pages. |
Wikipedia, https://en.wikipedia.org/wikaree_(data_structure), “Tree (data structure)”, May 15, 2005, entire page, 2005), 1 page. |
Wikipedia, https://en.wikipedia.org/wiki/Data_structure, “Data Structures”, Jan. 12, 2012, entire page (2012), 1 page. |
Six, Janet M., “Going Mobile: Designing for Different Screen Sizes Promoting Your Mobile App” https://www.uxmatters.com/mt/archives/2010/10/going-mobile-designing-for-different-screen-sizes-promoting-your-mobile-app.php, 2010. |
U.S. Appl. No. 15/335,326, filed Oct. 26, 2016. |
U.S. Appl. No. 15/221,511, filed Jul. 27, 2016. |
U.S. Appl. No. 15/221,520, filed Jul. 27, 2016. |
U.S. Appl. No. 15/221,471, filed Jul. 27, 2016. |
U.S. Appl. No. 14/814,361, filed Jul. 30, 2014. |
U.S. Appl. No. 16/801,517, filed Feb. 26, 2020. |
U.S. Appl. No. 15/221,495, filed Jul. 27, 2016. |
U.S. Appl. No. 15/335,394, filed Oct. 26, 2016. |
U.S. Appl. No. 14/673,261, filed Mar. 30, 2015. |
U.S. Appl. No. 15/335,294, filed Oct. 26, 2016. |
Number | Date | Country | |
---|---|---|---|
Parent | 14530159 | Oct 2014 | US |
Child | 16226507 | US |