Patent Grant
9916628
Embodiments are directed to changing how questions are presented to a user of a tax return preparation application.
Certain embodiments are directed to modifying a question that is selected by a rule engine that operates independently of a user interface (UI) management module or UI controller such that tax logic and tax rule determinations are separate or disconnected from interview screens (in contrast to known tax return preparation applications that have tax logic integrated or programmed within interview screens), and providing the modified question to a UI controller, which presents the modified question to the user through an interview screen generated by the UI controller.
Certain embodiments are directed to modifying or twisting how a question is presented and the meaning of an answer to a twisted or modified question relative to an answer of an original or unmodified question. Question modification or twisting may involve restructuring, rephrasing or rewording questions such that answering an original question with a binary answer (e.g., “yes”) has a different meaning relative to answering the modified question with the same binary answer (“yes”). For example, an original question may be flipped or twisted to be phrased in an “opposite” manner such that a “yes” answer to the original question has the same or substantially similar meaning as a “no” answer to the modified question. Embodiments may involve modifying or twisting a single original question into a single modified question and other modifications involving combining multiple questions or sub-questions including a sub-question directed to a topic of an original question, into a single question such that a single response to the modified question applies to each sub-question.
Certain embodiments are directed to modifying or twisting how a question is presented to a user of a tax return preparation application in order to convey to the user that the tax return preparation application already knows certain data about the user and to provide a more positive tax return preparation experience to the user.
Certain embodiments are directed to modifying or twisting how a question is presented to a user of a tax return preparation application in order to encourage positive responses such that the user will be more likely to respond in a positive manner (e.g., by answering “yes”) to the modified questions that would otherwise be answered “no” if an original question were asked instead.
Certain embodiments are directed to modifying or twisting an original question that is consistent with or based on a data model or schema of the tax return preparation application into a rephrased or reworded question that may not be consistent with or based on the data model or schema. An answer to the rephrased or reworded question is converted, mapped or “untwisted” so that the resulting answer determined to correspond to the first question is consistent with or specified by the model or schema for subsequent processing.
Certain embodiments are directed computer-implemented methods, computerized systems and articles of manufacture or computer program products that twist, change, rephrase or modify a question that is to be presented to a user of a tax preparation application in real time during preparation of the electronic tax return. Thus, a user is presented with and answers a modified question, and then the answer to the modified question is converted or mapped to an answer of the original question.
For example, one embodiment is directed to a computer-implemented method for presenting a personalized interview question to a user of a tax return preparation application during preparation of an electronic tax return and comprises a computer executing the tax return preparation application selecting a first or original question to be presented to a user of the tax preparation application during preparation of the electronic tax return. According to one embodiment, the first question may be selected as a result of a rule engine determining how rules apply to current runtime data of the electronic tax return to select one or more questions that are the subject of a non-binding suggestion that is provided to a UI management module or controller. The method further comprises the computer determining data of a pre-determined attribute of the user based at least in part upon the current runtime data of the electronic tax return and determining a second question that is modified relative to the first question based at least in part upon the pre-determined attribute data. For example, the second question may be restructured, rephrased or reworded based at least in part upon the user's demographic data such as age, residence location. As another example, the second question may be restructured, rephrased or reworded based at least in part upon a type of computing device that is executing the tax return preparation application. The method further comprises the computer presenting the second question to the user through an interview screen generated by the tax return preparation application and receiving an answer to the second question (rather than the first, original question) through the interview screen.
Another embodiment is directed to a computer-implemented method for presenting a personalized interview question to a user of a tax return preparation application during preparation of an electronic tax return and comprises a computer executing the tax return preparation application that includes a rule engine, a question modification module and a UI management module or controller. The rule engine selects a first or original question to be presented to the user of the tax preparation application during preparation of the electronic tax return, and the modification module, which is in communication with the rule engine, determines data of a pre-determined attribute of the user based at least in part upon current runtime data of the electronic tax return and determines a second question based at least in part upon the pre-determined attribute data. The second question is a modified version of the first question in that the second question restructures, rewords or rephrases how the original question is asked. The second question is provided by the modification module to the UI management module or controller, which determines whether and when to present the second question to the user through an interview screen. When the second question is presented to the user, the UI management module or controller receives an answer to the second question through the interview screen.
Yet other embodiments are directed to non-transitory computer-readable medium or computer program product comprising instructions, which when executed by a processor of a computing device, cause the computing device to execute a process for presenting a personalized interview question to a user of a tax return preparation application during preparation of an electronic tax return or determining and presenting a different, modified or twisted question to the user and processing the answer to the modified question to determine a corresponding answer to an original question.
Yet other embodiments are directed to computerized systems for presenting a personalized interview question to a user of a tax return preparation application during preparation of an electronic tax return or determining and presenting a modified or twisted question to the user and processing the answer to the modified question to determine a corresponding answer to an original question. One embodiment of a computerized system comprises a rule engine or logic agent, a question modification module and a UI management module or controller. According to one embodiment, the modification module is in communication with the rule engine and the UI management module or controller, and may be in communication with a data store shared among these components to read electronic tax return data there from. The rule engine or logic agent is configured or operable to read stored electronic tax return data and use rules defined by a decision table to identify a candidate question to be presented to the user. The candidate question may be part of or the subject of a non-binding suggestion that is generated by the rule engine for the UI management module or controller. The modification module receives data of the candidate question, converts the candidate question into a modified question or determines a modified question that rephrases or rewords candidate question, and provides the modified question to the UI management module or controller, which processes the suggestion/modified question. When the modified question is processed, it is presented to the user through an interview screen generated by the UI management module or controller, which receives an answer to the modified question from the user through the interview screen.
In a single or multiple embodiments, a second or modified question is automatically determined, e.g., in response to the modification module receiving certain electronic tax return or instance data from the data store or determining that the user is utilizing a certain type of computing device. According to another embodiment, the user requests a more personalized experience through the tax return preparation application, and modified questions are determined in response to the user request and based on the electronic tax return or instance data.
In a single or multiple embodiments, after an answer to a second or modified question has been converted or mapped to a corresponding answer to a first or original question, the rule engine may read or request data from the data store. In response, the data store may respond with a corresponding answer, which may be the result of conversion, mapping or “untwisting” of the answer to the second or modified question, or conversion, mapping or untwisting (if needed) may be performed in response to the request. In both cases, the corresponding or converted, mapped or untwisted answer is provided or served to the rule engine in response to the request rather than the answer to the second or modified question. In this manner, the rule engine can operate in a manner that is consistent with the data model or schema to execute rules derived from a decision table (which may not specify the manner in which a modified question is phrased), while the modified question determinations and conversions or mapping that may not be consistent with the data model or schema can be performed independently of the rule engine by the modification module.
In a single or multiple embodiments, the first or original question is in a format of a question concerning user data and requests a binary or “yes/no” answer such as “Are you older than 65?” whereas the second or modified question is in a different question format. For example, the second or modified question may be phrased to include an assumption, determination or assessment about the user such as “We believe you are younger than 65, correct?”
In a single or multiple embodiments, the first or original question that is modified or twisted is not presented to the user, and an answer to the first or original question is derived from the answer to the second or modified question as a result of converting, mapping or untwisting the received answer into an answer to the first or original question. Thus, while the user does not answer the first or original question directly, and may not even be presented with the first question at all, embodiments determine or derive an answer to the first question based on an answer to a different question.
In a single or multiple embodiments, the second or modified question is twisted relative to the first or original question such that the same answer to both questions conveys different meanings, whereas different answers to the first or original question and to the second or modified question convey the same or substantially similar meanings.
In a single or multiple embodiments, pre-determined attribute data used to determine or select a second or modified question is demographic data of the user. For example, a second or modified question may be used instead of a first or original question that is consistent with the data model or schema in cases in which the user is younger than a pre-determined age or the user utilizes a certain type of computing device such as a mobile communication device (which may also be indicative of age). Once the pre-determined attribute data has been received or determined, the second or modified question may be determined or selected by, for example, searching a table that associates pre-determined attributes, attribute data and a second or modified question to be used instead of a first or original question based on that attribute data, or by transforming a first or original question into a second or modified question by, for example, expressing one or more portions of the original question in a different way, e.g., opposite of what is recited in the first or original question.
In a single or multiple embodiments, whether question modification is performed is based at least in part upon probabilities of a positive user answers or responses. In one embodiment, a first probability of a positive response to the first or original question and a second probability of a positive response to the second or modified question are determined (e.g., based on statistical data or data of other tax users with similar demographic data), and a second or modified question is selected for presentation to the user when the second probability is greater than the first probability, else the original question can be presented.
In a single or multiple embodiments, question modification may involve aggregating multiple questions into a second or modified question such that the second or modified question includes multiple question elements or sub-questions to which a single response applies. These question elements or sub-questions may all be based on a data model or schema of the tax return preparation application such that when an answer is received to the modified question, that same answer can be applied to all question elements or sub-questions. This may involve parsing the second or modified question into respective individual sub-questions and storing the received answer as the answer to each of the individual sub-questions in a data store. In another embodiment, at least one of the question elements or sub-questions is not consistent with or based on the data model or schema of the tax return preparation application. Thus, for certain sub-questions that are consistent with the data model or schema, the received answer can be applied to each of those consistent sub-questions, whereas for the others sub-questions that are not consistent with the data model or schema, the answers thereto can be converted, mapped or untwisted into a corresponding answer to a question that is supported by the data model or schema. The converted, mapped or untwisted answer is then stored to the data store and in a format consistent with or specified by the data model or schema.
Embodiments are directed to computer-implemented methods, computerized systems and articles of manufacture or computer program products for modifying interview questions during preparation of an electronic tax return with a tax return preparation application.
In contrast to known tax preparation applications that utilize pre-determined questions and pre-determined question sequences that are programmed within and presented as part of an interview screen, and that involve manual interview screen coding and binding of an interview screens and associated tax logic, embodiments involve a modular interview engine that employs a rule engine or logic agent and UI management module or controller that are loosely coupled to each other such that the rule engine is dedicated to using tax or tax return related rules to generate results in the form of non-binding suggestions or recommendations for the UI controller. The UI controller then decides whether and when to generate or select an interview screen including the subject matter of the non-binding suggestion. A non-binding suggestions may refer to or include a candidate or potential question identified by the rule engine based on the rule engine's analysis of runtime data and rules specified by a decision table derived from a completeness graph representing tax law or tax return requirements. With embodiments, a modification module receives a first question or non-binding suggestion including a first question and generates or selects a modified or twisted question that involves the topic of the first question but restructures or rewords or rephrases the first question. For example, the first or original question may as “Are you older than 65?” whereas the second or modified question may be “We believe you are older than 65, right?” Thus, the meaning conveyed by the question may be twisted, and in this example, a positive response to the first question has a different meaning or result compared to a positive response of the second question.
Question or semantic twisting achieved with embodiments can be utilized to provide a more personalized tax return preparation experience, e.g., by crafting or modifying questions in view of a user's age or other demographic data, a type of computing device utilized by the user, and promoting positive responses or responses confirming assumptions made by the tax return preparation application were correct in order to provide a more positive impression of the tax return preparation application and its capabilities to the user, in contrast to the user answer “no” or “that is not correct” which may leave the user with a less positive or negative impression of the tax return preparation application. Further details regarding embodiments are described with reference to
Referring to
Thus, embodiments derive an answer 142 to the first question 112 without presenting the first question 112 to the user, through use of an intermediate, modified question 122 and processing of the answer 132 thereto. Further aspects of embodiments described above and system components of other embodiments are described with reference to
Referring to
The rule engine 412, one example of which is a drools expert engine, receives runtime or instance data 442 that is based on a “dictionary” of terms of a data model or schema 446 (generally, schema 446). For example, the schema 446 may specify, define or list tax-related concepts or terms, e.g., by names, type or category and hierarchy such as “name,” “social security number,” “citizenship,” “address,” “employer,” “interest,” “dividends,” “mortgage,” “deduction,” “tax credit,” “capital gain,” etc. An instance 447 is instantiated or created for the collection of data received and for each term or topic of the schema 446. The schema 446 may also specify a certain format of questions and answers (e.g., answer is binary (Y/N), a number of value). It will be understood that the schema 446 may define hundreds or thousands of such concepts or terms and may be defined in various ways, one example is based on an Extensible Markup Language (XML) schema. Non-limiting examples of schemas 446 that may be utilized in embodiments include Modernized E-File (MeF) and MeF++ schemas. It will be understood that embodiments may utilize various other schemas, and that a schema such as MeF is provided as a non-limiting example of a schema 446 that may be utilized in embodiments.
The runtime or instance data (generally, runtime data 442) stored in the shared data store 440 is used to populate corresponding fields of an electronic tax return or electronic tax form used to prepare an electronic tax return and may be received from various data sources 450a-d (generally, source) including user input or a user answer 436/450a to a question presented in an interview screen, data imported from a prior electronic tax return, online resources (such as online social networks) and third parties databases or resources. The rule engine 412 reads the runtime or instance data 442 from the shared data store 440 and utilizes or executes rules 461 using that data in order to determine which questions 462 still need to be presented to the user.
The rule engine 412 utilizes rules 461 expressed in a decision table 460 and the runtime data 442. Various types of rules 461 may be utilized by embodiments. For example, “tax” rules 461 that are utilized by the rule engine 412 may specify which types of data or tax documents are required, or which fields or forms of the electronic tax return should be completed. One example is if a taxpayer is married, then the electronic tax return is required to include information about a spouse. A tax rule 461 may involve if a certain box on a form (e.g., Box 1 of Form W2) is greater than a pre-determined amount, then certain fields of the electronic tax return (e.g., withholding fields) cannot be left empty and must be completed. Thus, tax rules 461 may reflect various tax requirements and are expressed using the concepts or terms of the data model or schema 446. As another example, other rules 461 may specify tax consequences or calculations and for this purpose, the logic agent 410 may be in communication with other modules or services 470a-d (generally, “Additional Services” such as printing, e-filing, tax recommendations, calculation).
As yet another example, rules 461 also be used for the purpose of identifying or narrowing which questions 462 are identified as potential questions to be presented to the user. This may involve utilizing rules 461 based on one or more associated data structures such as a decision table 460, which is based on a completion graph 465, which recites, for example, the requirements of a tax authority or a tax authority rule or law. The decision table 460 may be used for invalidation of potential questions 461 or topics and input or runtime data 442 requirements.
For example, referring to
In one embodiment, statistical data 463 (which may be appended as columns to the rule-question decision table 460, and received from or based on data collected by statistical/life knowledge module 490 described in further detail below) may indicate how likely a question 462 is to be relevant to a user given a set of runtime data 442 and may be utilized by the rule engine 442 when determining which question or topic 462 to select.
The logic agent 410 may also receive or otherwise incorporate information from a statistical/life knowledge module 490. The statistical/life knowledge module 490 contains statistical or probabilistic data related to the current or other users of the tax return preparation application and/or other taxpayers. For example, statistical/life knowledge module 490 may indicate that taxpayers residing within a particular zip code are more likely to be homeowners than renters. The logic agent 410 may use this knowledge to weight particular topics or questions related to these topics when processing rules 461 and questions 462 and generating non-binding suggestions 411. For example, questions 461 about home mortgage interest may be promoted or otherwise given a higher weight for users in particular zip codes or income levels. Statistical knowledge may apply in other ways as well. For example, tax forms often require a user 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 490 may contain data that shows that a large percentage of teachers have retirement accounts such as 403(b) retirement accounts. This information may then be used by the logic agent 410 when generating its suggestions 411. For example, rather than asking generically about retirement accounts, the suggestion 411 can be tailored directly to a question about 403(b) retirement accounts.
Data that is contained within the statistic/life knowledge module 490 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 490. 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 490 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.
In one embodiment, the rule engine 412 uses the decision table 460 to eliminate rules 461 that may apply which, is used to eliminate candidate questions 462 from consideration rather than requiring the user to step through each question of a pre-determined sequence of questions in order to conclude that a particular tax situation or topic applies to the user.
More specifically, continuing with the example shown in
The logic agent 410 provides a non-binding suggestion 411 comprising a selected question 461 or topic to be addressed to the UI controller 430, which includes a UI or user experience manager 431 that determines how to process selected questions 461 or topics and generates an interview screen 432 for the UI or selects an interview screen 432 of the UI based on the question 461 or topic of the non-binding suggestion 411. For ease of explanation, reference is made generally to a UI controller 430. For this purpose, the UI management module may include a suggestion resolution element, a generator element, and an interview screen management element or flow/view management” module as described in U.S. application Ser. No. 14/206,834, previously incorporated herein by reference, the suggestion resolution element is responsible for resolving the strategy of how to respond to incoming non-binding suggestions 441 provided by the logic agent, and for this purpose, the suggestion resolution element 341 may be programmed or configured or controlled by configuration files 433 that specify whether, when and/or how non-binding suggestions 411 are processed (e.g., priority, sequence, timing, in a current, next or subsequent interview screen, random, never or ignore, not until additional data received. For example, a configuration file 433 may specify one or more or all of how to process the non-binding suggestion 411 based on whether to consider or ignore the non-binding suggestion 411, when the non-binding suggestion 411 should be processed, content of an interview screen 432 based on the non-binding suggestion 411, how to present content or interview screens 432 based on the non-binding suggestion 411 in view of a form factor or type of a computing device utilized by the user of the tax preparation application or that executes the tax return preparation application embodying system components described above with reference to
The UI controller 430 generates the resulting user interaction or experience or creates or prepares an interview screen 432 or content thereof based on a library of visual assets such as pre-programmed interview screens or interview screens that are templates and that can be populated by the UI controller with a question 461 or topic of a non-binding suggestion 411.
The tax calculation engine 480 reads the current runtime or instance data 442 from the shared data store 440, and uses this data as inputs into respective nodes of one or more calculation graphs 482, and respective results or values are calculated with associated functions that are executed with the input data. New or resulting data is written back by the tax calculation engine 480 to the shared data store 440 for subsequent reading by the logic agent 410. For example, if the runtime or instance data 442 received thus far includes wages and interest earned from two savings accounts, a function for calculating Adjusted Gross Income (AGI) would sum this wage and interest data, and the resulting AGI value (based on the runtime data received thus far) is written back to the shared data store. As other types of AGI data are received or imported, the tax calculation engine 480 will run the calculation graphs 482 again to calculate a new AGI value, which would then be stored to the data store 482.
Additionally, according to embodiments, a question modification module or question personalization module 420 (generally, modification module) is a modular component that is positioned between the logic agent 410 and the UI controller 430 for purposes of modifying or twisting questions 461 that are the subject of a non-binding suggestion 411 directed to the UI controller 430. In the illustrated embodiment, the modification module 420 is in communication with the logic agent 410 and the UI controller 430, and may also be in communication with the shared data store 440. In certain embodiments, the modification module 420 receives data about the user from the logic agent 410, and in other embodiments, the modification module 420 reads the current runtime or instance data 442 to determine data about the user. Having described aspects of system components and how they work together, further details regarding the question modification module 420 are described with reference to
Referring to
For example, at 814, the question modification module 420 receives runtime or instance data 442 of a selected or pre-determined attribute of the user. This data 442 may be received from the logic agent 410 (as the logic agent 410 read current runtime data 442 from the data store 440 in connection with use of rules 461 to select a question 462), or received or read independently of the logic agent 410 by accessing the shared data store 440 directly. In either case, the question modification module 420 compares received data 442 of a selected or pre-determined attribute of the user and pre-determined criteria for determining whether to initiate question modification based on the question 462 or non-binding suggestion 411 received from the logic agent 410.
For example, the attribute of the user may be demographic data such as the user's age, state of residence, income level, occupation, etc. The attribute may also be not of the user but related to the user such as the type of computing device that executes the tax return preparation application including components of system embodiments and utilized by the user to prepare an electronic tax return—whether a laptop or desktop computer, a tablet computing device, or a mobile communication device such as a smartphone. At 816, the question modification module 420 determines whether the question 461 that was selected or identified by the logic agent 410 should be modified.
According to one embodiment, step 816 involves comparing the data of the pre-determined attribute and pre-determined criteria to determine whether to modify the question 416. According to one embodiment, the demographic data of the user is analyzed. For example, if the user is younger than a pre-determined age, then question modification is executed, but if not, then question modification is not executed. As another example, the pre-determined attribute involves a type of computing device utilized to prepare the electronic tax return. For example, question modification is executed if the user is using a smartphone or tablet, but not if the user is using a laptop or desktop computer.
For this purpose, referring to
According to another embodiment, a determination of whether to execute question modification involves a probability that a user's response to a first, original or unmodified question 461 will be a positive response and/or a probability that a response to a second or modified question 461m will be a positive response. For example, the table 900 or other data structure may include statistical data mined from other electronic tax returns such as prior year returns of the user and/or other taxpayers to determine that for certain attribute data of the user, the user is more likely to answer “yes” to certain questions, but more likely to answer “no” to other questions. Thus, questions to which the user is determined to more likely answer “no” may be questions that are modified according to embodiments in order to encourage positive responses and positive impressions and user feedback.
Continuing with the above example involving pre-determined attribute data is “occupation” and the user being a student, statistical data may show that the average age of “student” is 22, such that it is more likely (e.g., 85%) that the user is younger than 65, such that the table 900 would indicate a first or original question of “Are you older than 65” based on the decision table 460 and associated schema 446, whereas the second or modified question 461m of “You are younger than 65, right?” for a “student.”
Referring to
Referring to
According to one embodiment, question modification involves utilizing a second question 416m that is different than the first or original question 416 in that the second question 416m rewords or rephrases the first question 416, or has a different structure than the first question 416, while still involving the same tax topic or subject matter of the first question 416. In one embodiment, with reference again to
For example, referring to
Thus, as shown in
Further, the embodiment illustrated in
Referring to
Referring to
For example, in the illustrated embodiment, the first or original question 461 addresses one tax-related topic (such as age, as in the example above), whereas the second or modified question 461m includes multiple sub-questions or question elements 1601a-d that addresses multiple tax-related topics (such as age, as well as marital status, sex), involve restructuring by being framed as an assumption requesting confirmation, and involves a twisting element 1601a/1630 compared to the original question since 1601a involves “younger” than 65 rather than “65 or older” as in the first or original question 461.
As described in further detail below, when the user responds with an answer or confirms an assumption 1602, that same answer or confirmation can be applied to each of the questions or sub-questions or elements that did not involve a twist 1630 such that when the sub-questions or elements are parsed 1604, the answer or confirmation 1602 is applied 1606 to that question (e.g., Q1), but for the other question Q2, an additional processing step to convert or map 1607 the answer or confirmation 1602 into an answer or confirmation corresponding to the original untwisted question or element, and then that corresponding answer is applied to that question or element 1608.
Referring again to
At 1016, and with further reference to
Referring to
For example, referring again to
Referring to
For example, referring again to
Referring to
Referring to
Thus, depending on the type of modification employed, the answer 435m to a modified question 461m can be processed and stored to the data store 440 in different ways such that “untwisting” or mapping the answer 435m to determine or derive a corresponding answer 435d may or may not be required, but eventually the answer 435d is determined or derived and stored to the shared data store 440 in a manner that is consistent with or specified by the data model or schema 446 even if the modified question 461m or answer 435m thereto is not consistent with or specified by the data model or schema 446.
Referring to
When the electronic tax return is populated and completed by the logic agent 410 or by the direction of the logic agent 410 or using one or more components or services 470 as applicable, the electronic tax return can be printed and/or filed with a tax authority such federal state or local tax authority or other tax collecting entity such as Internal Revenue Service and Franchise Tax Board of the State of California.
Method embodiments or certain steps thereof, some of which may be loaded on certain system components, computers or servers, and others of which may be loaded and executed on other system components, computers or servers, may also be embodied in, or readable from, a non-transitory, tangible medium or 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 2220 performs steps or executes program instructions 2212 within memory 2210 and/or embodied on the carrier to implement method embodiments.
Although particular embodiments have been shown and described, it should be understood that the above discussion is not intended to limit the scope of these embodiments. While embodiments and variations of the many aspects of the invention have been disclosed and described herein, such disclosure is provided for purposes of explanation and illustration only. Thus, various changes and modifications may be made without departing from the scope of the claims.
For example, while certain embodiments of question modification are described with reference to aggregating or combining sub-questions or question elements, embodiments may also modify a question by splitting a question into multiple sub-questions or elements. For example, the logic agent may generate a non-binding suggestion for the UI controller that includes a question concerning an address including “Address Line 1”, “Address Line 2”, “City”, “State”, “Zipcode” and etc. The modification module may split “Address Line 1” into multiple elements such as “Street Name” and “Apartment Number”, in which case a user would separately respond to these questions with different answers (e.g., “East Street” and “Apartment 10” such that the a joining operation may then be performed when storing these different answers back to the data store.
As another example, while certain embodiments are describe with reference to discrete boundaries (e.g., age 65 or older vs. younger than 65), the modification module may process multiple-to-one mapping. For example, the rules may require an answer to a question that involves a range or descriptive range (e.g., “Young,” “Middle Aged” or “Senior”) associated with ranges of numerical ages, in which case the modification module may modify the original question involving selection of “Young” “Middle Aged” and “Senior” options into a question specifying a range of ages, e.g., 1-18 years old, 19-61 years old, 62+ years old. Similarly, question modification may involve modifying a question asking for a range of ages into a question that asks the user to select a category, and then the answer comprising the selected category is subsequently untwisted into an associated age range. Thus, the “twist” and subsequent “untwisting” may involve conversion to or from descriptions to numerical data or ranges of numerical data.
Further, embodiments may involve storing only the post conversion or result of “untwisting” to the data store such that all of the data stored in the data store is consistent with or specified by the data model or schema, or other data such as the modified question and/or answer thereto can be stored to the data store and associated with the first question and corresponding answer, but the logic agent would only process the data that is consistent with or specified by the data model or schema when using the decision table rules to identify questions for the UI controller.
Moreover, while certain embodiments are described with reference to a modification module, or conversion module for “untwisting” answers as module components that operate independently of the logic agent, UI controller and data store, other embodiments may involve a modification module being a component of a logic agent or a component of a UI controller. Further, for purposes of “untwisting” or mapping an answer to a modified question to a corresponding answer to an original question, the modification module may embody these conversion capabilities, or a conversion module may be hosted by the data store to handle untwisting or mapping of answers to modified questions received from the UI controller.
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| Office Action dated Sep. 14, 2017 in U.S. Appl. No. 14/557,335, (57pgs.). |
| Amendment and Response dated Aug. 7, 2017 in U.S. Appl. No. 14/462,315, (10pgs.). |
| Advisory Action dated Aug. 24, 2017 in U.S. Appl. No. 14/462,315, (3pgs.). |
| Amendment and Response and Request for Continued Examination dated Sep. 6, 2017 in U.S. Appl. No. 14/462,315, (43pgs.). |
| Vanderbilt University, “Free tax prep help available for Vanderbilt employees”, Feb. 6, 2014, Vanderbilt University, p. 1-3. |
| Office Action dated Jun. 22, 2017 in U.S. Appl. No. 14/698,746, (50pgs.). |
| Amendment and Response dated Sep. 22, 2017 in U.S. Appl. No. 14/698,746, (26pgs.). |
| Office Action dated Oct. 13, 2017 in U.S. Appl. No. 14/462,397, (72pgs.). |
| Office Action dated Nov. 30, 2017 in U.S. Appl. No. 14/462,373, (72pgs.). |
| Office Action dated Jun. 27, 2017 in U.S. Appl. No. 14/755,859, (174pgs.). |
| Amendment and Response dated Nov. 27, 2017 in U.S. Appl. No. 14/755,859, (53pgs.). |
| Amendment and Response dated Jun. 20, 2017 in U.S. Appl. No. 14/448,886, (14pgs.). |
| Advisory Action dated Jul. 5, 2017 in U.S. Appl. No. 14/448,886, (4pgs.). |
| Amendment and Response dated Aug. 21, 2017 in U.S. Appl. No. 14/448,886, (37pgs.). |
| Office Action dated Nov. 28, 2017 in U.S. Appl. No. 14/448,886, (65pgs.). |
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| Office Action dated Oct. 16, 2017 in U.S. Appl. No. 14/448,986, (30pgs.). |
| OpenRules, Preparing a Tax Return Using OpenRules Dialog, Aug. 2011 (Year: 2011) (25pgs.). |
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| Amendment and Response dated Nov. 7, 2017 in U.S. Appl. No. 14/555,334, (26pgs.). |
| Advisory Action dated Nov. 22, 2017 in U.S. Appl. No. 14/555,334, (2pgs.). |
| Office Action dated Oct. 11, 2017 in U.S. Appl. No. 14/701,030, (53pgs.). |
| Office Action dated Aug. 25, 2017 in U.S. Appl. No. 14/673,646, (65pgs.). |
| Office Action dated Jul. 10, 2017 in U.S. Appl. No. 14/555,222, (63pgs.). |
| Amendment and Response dated Nov. 10, 2017 in U.S. Appl. No. 14/555,222, (25pgs.). |
| Office Action dated Nov. 3, 2017 in U.S. Appl. No. 14/701,087, (103pgs.). |
| Office Action dated Jun. 27, 2017 in U.S. Appl. No. 14/675,166, (46pgs.). |
| Amendment and Response dated Oct. 27, 2017 in U.S. Appl. No. 14/675,166, (25pgs.). |
| Response dated Jun. 23, 2017 in U.S. Appl. No. 14/555,296, (7pgs.). |
| Office Action dated Oct. 20, 2017 in U.S. Appl. No. 14/555,296, (50pgs.). |
| Office Action dated Aug. 18, 2017 in U.S. Appl. No. 14/555,543, (42pgs.). |
| Interview Summary dated Oct. 25, 2017 in U.S. Appl. No. 14/555,543, (3pgs.). |
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