Patent Publication Number: US-10762472-B1

Title: Methods, systems and computer program products for generating notifications of benefit qualification change

Description:
This application includes subject matter similar to the subject matter described in the following co-owned applications: (1) U.S. application Ser. No. 15/221,471 filed Jul. 27, 2016, entitled “METHODS, SYSTEMS AND COMPUTER PROGRAM PRODUCTS FOR ESTIMATING LIKELIHOOD OF QUALIFYING FOR BENEFIT”; (2) U.S. application Ser. No. 15/221,495 filed Jul. 27, 2016, entitled “METHODS, SYSTEMS AND COMPUTER PROGRAM PRODUCTS FOR PRIORITIZATION OF BENEFIT QUALIFICATION QUESTIONS”; and (3) U.S. application Ser. No. 15/221,520 filed Jul. 27, 2016, entitled “METHODS, SYSTEMS AND COMPUTER PROGRAM PRODUCTS FOR GENERATING EXPLANATIONS FOR CHANGES IN BENEFIT QUALIFICATION STATUS”. The contents of the above-mentioned applications are fully incorporated herein by reference as though set forth in full. 
     SUMMARY 
     Embodiments are directed to systems, computer-implemented methods, and computer program products for providing a notice regarding a benefit qualification status change. 
     In one embodiment, a computer-implemented method for providing a notice regarding a benefit qualification status change includes receiving a change in a qualification requirement for a benefit program. The method also includes obtaining a completeness graph (G i ) corresponding to the benefit program. The method further includes modifying the completeness graph (G i ) based on the received change in the qualification requirement for the benefit program. Moreover, the method includes obtaining a profile corresponding to an individual. In addition, the method includes obtaining a first qualification status relating to the individual&#39;s qualification for the benefit program before the received change in the qualification requirement for the benefit program. The method also includes determining a second qualification status by running the profile against the modified completeness graph. The method further includes, when the first and second qualification statuses are different, providing a notice for a corresponding benefit qualification status change. The completeness graph (G i ) may be modified through a user interface and/or an application programming interface. 
     In one or more embodiments, when the first and second qualification statuses are different, the method also includes accessing a recommendation corresponding to the benefit qualification status change, and providing the recommendation corresponding to the benefit qualification status change. The recommendation may include a proposed change to avoid the benefit qualification status change. The recommendation may include an ordered listing of a plurality of benefit programs for which the individual is likely to qualify. 
     In one or more embodiments, when the first and second qualification statuses are different, the method also includes generating a user interface configured to receive the recommendation, provide the notice, and provide the recommendation. Accessing the recommendation may include accessing an recommendation template. 
     In one or more embodiments, the profile corresponding to the individual includes a previously generated profile. The previously generated profile may have been previously generated for a benefit qualification analysis. 
     In one or more embodiments, the second qualification status is determined by running the profile against the modified completeness graph in real-time in real-time when the change in the qualification requirement for the benefit program is received. 
     In another embodiment, a computer-implemented method for providing a notice regarding a benefit qualification status change includes receiving a change in a qualification requirement for a benefit program. The method also includes obtaining a schema including a profile corresponding to an individual. The schema also includes a completeness graph (G i ) corresponding to the benefit program. The schema further includes a first qualification status relating to the individual&#39;s qualification for the benefit program before the received change in the qualification requirement for the benefit program. The method further includes modifying the completeness graph (G i ) and the schema based on the received change in the qualification requirement for the benefit program. Moreover, the method includes determining a second qualification status by running the profile against the modified completeness graph. In addition, the method includes, when the first and second qualification statuses are different, providing a notice for a corresponding benefit qualification status change. 
     In one or more embodiments, when the first and second qualification statuses are different, the method also includes accessing a recommendation corresponding to the benefit qualification status change, and providing the recommendation corresponding to the benefit qualification status change. The recommendation may include a proposed change to avoid the benefit qualification status change. The recommendation may include an ordered listing of a plurality of benefit programs for which the individual is likely to qualify. 
     In one or more embodiments, when the first and second qualification statuses are different, the method also includes generating a user interface configured to receive the recommendation, provide the notice, and provide the recommendation. Accessing the recommendation may include accessing an recommendation template. 
     In still another embodiment, a system for providing a notice regarding a benefit qualification status change, the system includes an input output module configured to receive a change in a qualification requirement for a benefit program. The input output module is also configured to obtain a completeness graph (G i ) corresponding to the benefit program. The input output module is further configured to obtain a profile corresponding to an individual. Moreover, the input output module is configured to obtain a first qualification status relating to the individual&#39;s qualification for the benefit program before the received change in the qualification requirement for the benefit program. The system also includes a benefits calculation engine configured to modify the completeness graph (G i ) based on the received change in the qualification requirement for the benefit program, and determine a second qualification status by running the profile against the modified completeness graph. The system further includes a user interface manager configured to, when the first and second qualification statuses are different, generate a user interface to provide a notice for a corresponding benefit qualification status change through the user interface. 
     In one or more embodiments, the user interface is configured to modify the completeness graph (G i ). The system may also also include an application programming interface configured to modify the completeness graph (G i ). 
     In one or more embodiments, when the first and second qualification statuses are different, the user interface is configured to access a recommendation corresponding to the benefit qualification status change and provide the recommendation. The recommendation may include a proposed change to avoid the benefit qualification status change. The recommendation may include an ordered listing of a plurality of benefit programs for which the individual is likely to qualify. The user interface may be configured to access an recommendation template to access the recommendation. 
     In one or more embodiments, the profile corresponding to the individual includes a previously generated profile. The previously generated profile may have been previously generated for a benefit qualification analysis. The benefits calculation engine may be configured to determine the second qualification status by running the profile against the modified completeness graph in real-time when the change in the qualification requirement for the benefit program is received. 
     In yet another embodiment, a computer program product including a non-transitory computer readable storage medium embodying one or more instructions executable by a benefits computing device having an input output module, a benefits calculation engine, and a user interface manager to perform a process for providing a notice regarding a benefit qualification status change. The process includes the input output module receiving a change in a qualification requirement for a benefit program, obtaining a completeness graph (G i ) corresponding to the benefit program, obtaining a profile corresponding to an individual, and obtaining a first qualification status relating to the individual&#39;s qualification for the benefit program before the received change in the qualification requirement for the benefit program, the benefits calculation engine modifying the completeness graph (G i ) based on the received change in the qualification requirement for the benefit program, and determining a second qualification status by running the profile against the modified completeness graph, and when the first and second qualification statuses are different, the user interface manager generating a user interface and providing a notice for a corresponding benefit qualification status change through the user interface. 
     In still another embodiment, a computer program product including a non-transitory computer readable storage medium embodying one or more instructions executable by a benefits computing device having an input output module, a benefits calculation engine, and a user interface manager to perform a process for providing a notice regarding a benefit qualification status change. The process includes the input output module receiving a change in a qualification requirement for a benefit program and obtaining a schema including a completeness graph (G i ) corresponding to the benefit program, a profile corresponding to an individual, and a first qualification status relating to the individual&#39;s qualification for the benefit program before the received change in the qualification requirement for the benefit program, the benefits calculation engine modifying the completeness graph (G i ) and the schema based on the received change in the qualification requirement for the benefit program, and determining a second qualification status by running the profile against the modified completeness graph, and when the first and second qualification statuses are different, the user interface manager generating a user interface and providing a notice for a corresponding benefit qualification status change through the user interface. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The foregoing and other aspects of embodiments are described in further detail with reference to the accompanying drawings, in which the same elements in different figures are referred to by common reference numerals, wherein: 
         FIGS. 1 to 4  are schematic diagrams illustrating the implementation of benefit analysis systems on computing devices according to various embodiments. 
         FIGS. 5 and 6  are block diagrams of computer systems according to various embodiments on which benefits analysis systems according to various embodiments may be implemented. 
         FIG. 7  is a block diagram of a computing apparatus or system in which various embodiments may be implemented or that may be utilized to execute various embodiments. 
         FIGS. 8 and 12  illustrate examples of a simplified version of a completeness graph according to two embodiments at two different benefits qualification periods. 
         FIG. 9  illustrates a completeness graph according to another embodiment. 
         FIG. 10  illustrates a decision table based on or derived from the completeness graph of  FIG. 9 . 
         FIG. 11  illustrates another embodiment of a decision table that incorporates statistical data. 
         FIG. 13  illustrates a differential benefit qualification completeness graph that illustrates the differences between the values contained within the common nodes of the two respective benefit qualification completeness graphs depicted in  FIGS. 8 and 12 . 
         FIG. 14  is a schematic diagram depicting a system for determining benefit qualification status using rules and calculations based on a declarative data structures according to one embodiment. 
         FIGS. 15 to 18  are flow charts depicting computer-implemented methods/algorithms for providing notifications of benefit qualification change by modifying a completeness graph corresponding to a benefit program based on a received benefit program qualification requirement change and running a profile corresponding to an individual against the modified completeness graph using benefits analysis systems according to various embodiments. 
     
    
    
     In order to better appreciate how to obtain the above-recited and other advantages and objects of various embodiments, a more detailed description of embodiments is provided with reference to the accompanying drawings. It should be noted that the drawings are not drawn to scale and that elements of similar structures or functions are represented by like reference numerals throughout. It will be understood that these drawings depict only certain illustrated embodiments and are not therefore to be considered limiting of scope of embodiments. 
     DETAILED DESCRIPTION OF ILLUSTRATED EMBODIMENTS 
     Embodiments describe methods, systems and articles of manufacture for generating notifications of benefit qualification change. In particular, some embodiments describe modifying a completeness graph corresponding to a benefit program based on received benefit program qualification requirement change, and determining a second qualification status by running an obtained profile corresponding to an individual against the modified completeness graph. A notice is provided when the second qualification status is different from an obtained first qualification status. 
     There are a large number of benefit programs available to individuals from both governmental and nongovernmental entities. As used in this application, a “benefit program” includes, but is not limited to, any program, service, project or activity that directly assists individuals who meet certain qualifications. As used in this application, an individual “qualifying for a benefit program” includes, but is not limited to, an individual having the requirements to receive benefits under a benefit program. 
     For instance, there are at least 10 benefit programs available to individuals residing in California (e.g., CalFresh, California CalWORKs (TANF), California Head Start, California Low Income Home Energy Assistance Program, California Medicaid, California National School Breakfast and Lunch Program, California Special Milk Program, California Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), California Unemployment Insurance, and California Weatherization Assistance Program). 
     Benefit programs are available in many different categories (e.g., Agriculture Loans, American Indian/Alaska Native, American Indian/Alaska Native Youth, Business Loans, Child Care/Child Support, Counsel/Counseling, Disability Assistance, Disaster Relief, Disaster Relief Loans, Education Loans, Education/Training, Employment/Career Development Assistance, Energy Assistance, Environmental Sustainability/Conservation, Family Social Security, Food/Nutrition, General Loans, Grants/Scholarships/Fellowships, Healthcare, HIV/AIDS, Housing, Housing Loans, Insurance, Living Assistance, Medicaid/Medicare, Military: Active Duty and Veterans, Social Security Disability Assistance, Social Security Insurance, Social Security Medicare, Social Security Retirement, Spouse &amp; Widower(er) Social Security, Supplemental Security Income, Tax Assistance, Veteran Loans, Veterans Social Security, and Volunteer Opportunities). 
     Benefit programs are also available from a variety of agencies (e.g., Barry Goldwater Scholarship Foundation, Christopher Columbus Fellowship Foundation, Harry S. Truman Scholarship Foundation, James Madison Fellowship Foundation, Library of Congress, National and Community Service, National Endowment for the Arts, The Udall Foundation, U.S. Department of Agriculture, U.S. Department of Commerce, U.S. Department of Education, U.S. Department of Energy, U.S. Department of Health and Human Services, U.S. Department of Homeland Security, U.S. Department of Housing and Urban Development, U.S. Department of Justice, U.S. Department of Labor, U.S. Department of State, U.S. Department of the Interior, U.S. Department of the Treasury, U.S. Department of Transportation, U.S. Department of Veterans Affairs, U.S. Office of Personnel Management, U.S. Railroad Retirement Board, U.S. Small Business Administration, U.S. Social Security Administration, Woodrow Wilson National Fellowship Foundation). 
     The large number of benefit programs available to individuals can result in information overload for individuals investigating whether they qualify for any benefit programs, which may frustrate these individuals, causing them to pause or end their investigation. This, in turn, can prevent individuals from receiving benefits to which they are entitled. The large number of benefit programs also make it difficult for individuals to keep track of their qualification status with regard to these benefit programs, making it difficult for individuals receiving benefits under these programs to plan their personal or household budgets. 
     Current benefit analysis systems and software must be actively accessed by users to determine the benefit qualification status for a particular user in a particular benefit program. Current benefit analysis systems and software do not track changes in benefit qualification rules and requirements, and this can lead to surprises when an individual expects to receive benefits under a particular program. 
     Nor are current benefit analysis systems and software capable of providing notice of changes in benefit qualification status to a user in real-time. As used in this application, “real-time” includes, but is not limited to, two processes or steps occurring within a short time of each other, such that the processes or steps appeared to be substantially simultaneous to an average individual. For instance, completion of a first process or step may trigger a second process or step. 
     Some embodiments described herein involve a profile corresponding to an individual and completeness graphs corresponding to respective benefit programs. Some embodiments modify a completeness graph in response to receiving a change in benefit program qualification requirement, and run the profile against the modified completeness graph to determine whether the qualification status has changed. Providing a notice of benefit qualification status change to a user facilitates more efficient and effective benefit program related budget planning. 
     Some embodiments provide a notice of benefit qualification status change to a user in real-time in response to receiving a benefit program qualification requirement change. A real-time notification of benefit qualification status change also facilitates more efficient and effective benefit program related budget planning. 
     Some embodiments also provide a recommendation relating to the benefit qualification status change along with the notice of the change. These recommendations can improve planning by facilitating changes in an individual&#39;s behavior to avoid the benefit qualification status change. 
     The embodiments described herein facilitate efficient and effective benefit program related budget planning by providing notification of benefit qualification status change and recommendations relating to that change to a user in real-time. More efficient and effective benefit program related budget planning can improve the impact of benefit programs in the individual and household budgets of those who need the programs the most. 
     As used in this application, a “completeness graph” or “completion graph” includes, but is not limited to, a graphical representation including a plurality of interconnecting functional nodes connected by one of a plurality of functions. As used in this application, “satisfying a completeness graph” includes, but is not limited to, data such as a profile corresponding to an individual filling or partially filling the nodes of a completeness graph such that running the completeness graph results in a determination that the profile (i.e. the individual corresponding to the profile) qualifies for (i.e., to receive benefits under) the benefit program corresponding to the completeness graph. 
     As used in this application, a “user” includes, but is not limited to, a person investigating whether an individual qualifies for a benefit program using benefit analysis software. The “user” may or may not be the individual for whom benefit program qualification is being investigated. As used in this application, “benefit qualification data” includes, but is not limited to, information that may affect an individual&#39;s qualifications for a benefit program. As used in this application, a “previously collected benefit qualification data” includes, but is not limited to, benefit qualification data that was previously collected (e.g., a previous year&#39;s benefit qualification data). 
     As used in this application, “benefit qualification data source” includes, but is not limited to, a source of benefit qualification data (e.g., tax preparers or financial management systems). As used in this application, a “financial management system” includes, but is not limited to, software that oversees and governs an entity&#39;s finances (e.g., income, expenses, and assets). An exemplary financial management system is MINT Financial Management Software, which is available from Intuit Inc. of Mountain View, Calif. A financial management system is executed to assist a user with managing its finances, and is used solely for financial management. Financial management systems manage financial transaction data from financial transaction generators such as accounts including checking, savings, money market, credit card, stock, loan, mortgage, payroll or other types of account. Such financial transaction generators can be hosted at a financial institution such as a bank, a credit union, a loan services or a brokerage. Financial transaction data may include, for example, account balances, transactions (e.g., deposits, withdraws, and bill payments), debits, credit card transactions (e.g., for merchant purchases). Financial management systems can also obtain financial transaction data directly from a merchant computer or a point of sale terminal. Financial management systems can include financial transaction data aggregators that manage and organize financial transaction data from disparate sources. While certain embodiments are described with reference to MINT Financial Management Software, the embodiments described herein can include other financial management systems such as QUICKEN Financial Management Software, QUICKRECIPTS Financial Management Software, FINANCEWORKS Financial Management Software, Microsoft Money Financial Management Software and YODLEE Financial Management Software (available from Yodlee, Inc. of Redwood City, Calif.). 
     As used in this application, “estimating a likelihood” includes, but is not limited to, a benefit program qualification calculated from less than all of the required benefit qualification data. As used in this application, “benefit code,” “benefit regulation,” and “benefit rule,” includes, but is not limited to, statutes, regulations, and rules relating to qualification for benefit programs in various jurisdictions (e.g., state and federal), including the United States of America and other jurisdictions around the world. As used in this application, an “unanswered question in or for a completeness graph” includes, but is not limited to, an empty or undetermined node in the completeness graph. 
     As used in this application, “computer,” “computer device,” or “computing device” includes, but are not limited to, a computer (laptop or desktop) and a computer or computing device of a mobile communication device, smartphone and tablet computing device such as an IPAD (available from Apple Inc. of Cupertino, Calif.). As used in this application, “benefit analysis system,” “benefit analysis computing device,” “benefit analysis computer,” “benefit analysis software,” “benefit analysis module,” “benefit analysis application,” or “benefit analysis program” includes, but are not limited to, one or more separate and independent software and/or hardware components of a computer that must be added to a general purpose computer before the computer can analyze whether an individual qualifies for a benefit program, and computers having such components added thereto. 
     As used in this application, “server” or “server computer” includes, but is not limited to, one or more separate and independent software and/or hardware components of a computer that must be added to a general purpose computer before the computer can receive and respond to requests from other computers and software in order to share data or hardware and software resources among the other computers and software, and computers having such components added thereto. As used in this application, “obtaining data” includes, but is not limited to, accessing data (e.g., from a database through a network) and generating data (e.g., using one or more hardware and software components). 
     As used in this application, “input/output module” or “input output module” includes, but is not limited to, one or more separate and independent software and/or hardware components of a computer that must be added to a general purpose computer before the computer can communicate with and facilitate the receipt and transfer of information, including schema, completeness graphs, profiles, benefit qualification data and data relating to benefit qualification data sources, from and to other computers. As used in this application, “memory module” includes, but is not limited to, one or more separate and independent software and/or hardware components of a computer that must be added to a general purpose computer before the computer can store information, including schema, completeness graphs, profiles, benefit qualification data and data relating to benefit qualification data sources. As used in this application, a “benefits calculation engine” includes, but is not limited to, one or more separate and independent software and/or hardware components of a computer that must be added to a general purpose computer before the computer can manipulate data to modify a completeness graph based on a benefit program qualification requirement change, and run profiles corresponding to individuals against the modified completeness graph corresponding to benefit programs with the benefit program qualification requirement change. As used in this application, a “user interface manager” includes, but is not limited to, one or more separate and independent software and/or hardware components of a computer that must be added to a general purpose computer before the computer can receive information from and send information to an individual. As used in this application, “application programming interface” includes, but is not limited to, one or more separate and independent software and/or hardware components of a computer that must be added to a general purpose computer before the computer can receive information from and send information to a separate computer. 
     As used in this application, “website” includes, but is not limited to, one or more operatively coupled webpages. As used in this application, “browser” or “web browser” includes, but is not limited to, one or more separate and independent software and/or hardware components of a computer that must be added to a general purpose computer before the computer can receive, display and transmit resources from/to the World Wide Web. 
       FIG. 1  depicts an exemplary hardware system  100  on which a benefit analysis system  102  according to one embodiment can be executed. The hardware system  100  according to this embodiment includes a server computing device  104  and a user computing device  106 , which are operatively coupled via a network  108 . The network  108  may be two or more networks depending on the system configuration and communication protocols employed. One or more or all of networks  108  may be, for example, a wireless or cellular network, a Local Area Network (LAN) and/or a Wide Area Network (WAN). Reference to network  108  generally is not intended to refer to a specific network or communications protocol, and embodiments can be implemented using various networks  108  and combinations thereof. 
     The user computing device  106  has a browser  112  running thereon. The browser  112  is operatively coupled to the benefit analysis system  102  via the network  108 , to facilitate a user physically interfacing with the user computing device  106  to interface with the benefit analysis system  102  running on the server computing device  104 . The various computing devices  104 ,  106  may include visual displays or screens  114  operatively coupled thereto. In the embodiment depicted in  FIG. 1 , the user computing device  106  includes a display or a screen  114  through which a user physically interfaces with the user computing device  106  and the browser  112  running thereon. 
     While the user computing device  106  in  FIG. 1  is depicted as a desktop computer providing access to the benefit analysis system  102  through a browser  112  running thereon, in other embodiments, the user may access the benefit analysis system  102  using a specific program running on the user computing device  106 . In some embodiments, such as the ones shown in  FIG. 2 , the user computing device is a mobile computing device (e.g., tablet computer  106 ′ or mobile phone  106 ″). In such embodiments, the user may access the benefit analysis system  102  using an application; and communication between the two computers  104 ,  106  may occur over a private communication network (e.g., mobile phone network). 
       FIG. 3  depicts another exemplary hardware system  100 ′ on which a benefit analysis system  102  according to another embodiment can be executed. The hardware system  100 ′ depicted in  FIG. 2  is similar to the hardware system  100  depicted in  FIG. 1 . However, in addition to the server computing device  104  and the user computing device  106 , the hardware system  100 ′ also includes a benefit qualification data source computer  116  operatively coupled to the server computing device  104  and the user computing device  106  via the network  108 . The benefit qualification data source computer  116  hosts a benefit qualification data source program  118 , from which the server computing device  104  and the user computing device  106  may obtain benefit qualification data. 
     Exemplary benefit qualification data source programs  118  include financial management systems utilized by the taxpayer (such as MINT or QUICKEN financial management systems), accounts the taxpayer has with an online social media website, third parties databases or resources (such as government databases or documents, such as property tax records, Department of Motor Vehicle (DMV) records), and other external sources of benefit qualification data. MINT and QUICKEN are registered trademarks of Intuit Inc., Mountain View, Calif. While  FIG. 3  depicts the server computing device  104 , the user computing device  106  and the benefit qualification data source computer  116  as being operatively coupled by the same network  108 , in other embodiments, these computing devices may be operatively coupled by two or three networks. While  FIG. 3  depicts the benefit analysis system  102  and the benefit qualification data source program  118  as running on separate computing devices  104 ,  116 , in other embodiments, the benefit analysis system  102  and the benefit qualification data source program  118  may run on the same computing device (e.g., the server computing device  104 ). 
       FIG. 4  depicts still another exemplary hardware system  100 ″ on which a benefit analysis system  102  according to still another embodiment can be executed. The hardware system  100 ″ depicted in  FIG. 4  is similar to the hardware system  100  depicted in  FIG. 1 . However, in addition to the server computing device  104  and the user computing device  106 , the hardware system  100 ″ also includes a plurality of other user computing device  106   a  . . .  106   n  (belonging to other users of the benefit analysis system) operatively coupled to the server computing device  104  and the user computing device  106  via the network  108 . The plurality of other user computing device  106   a  . . .  106   n  hosts a respective plurality of other user programs  112   a  . . .  112   n  (e.g., browsers accessing the benefit analysis system), from which the server computing device  104  and the user computing device  106  may obtain benefit qualification data (e.g., for cohort analysis). 
     Exemplary benefit qualification data that may be obtained from the plurality of other user programs  112   a  . . .  112   n  include anonymized benefit qualification data associated with a plurality of users. While  FIG. 4  depicts the server computing device  104 , the user computing device  106  and the plurality of other user computing device  106   a  . . .  106   n  as being operatively coupled by the same network  108 , in other embodiments, these computing devices may be operatively coupled by a plurality of networks. While  FIG. 4  depicts the benefit analysis system  102  and the plurality of other user programs  112   a  . . .  112   n  as running on separate computing devices  104 ,  106   a  . . .  106   n , in other embodiments, multiple instantiations of the benefit analysis system  102  may run on user computing device  106   a  . . .  106   n  and the anonymized benefit qualification data may be collected on the server computing device  104 . 
       FIG. 5  depicts, in greater detail, another hardware system  100  configured to host a benefit analysis system  102  according to one embodiment. Like the hardware system  100  depicted in  FIG. 1 , the hardware system  100  in  FIG. 5  includes a server computing device  104  and a user computing device  106  that are operatively coupled by a network  108 . The server computing device  104  includes an input/output module  120 , a memory module  122  and a processor  124 . The user computing device  106  includes an input/output module  120 , a memory module  122  and a processor  124 . The input/output modules  120  are configured to communicate with and facilitate the receipt and transfer of benefit qualification data. The memory modules  122  are configured to store benefit qualification data, in proper formats for benefit qualification data acquisition and benefit qualification status determination. The processors  124  in the server computing device  104  and the user computing device  106  are configured to respectively run programs (e.g., benefit analysis system  102  and browser  112 ), as shown in  FIG. 5 . 
       FIG. 6  depicts, in even greater detail, another hardware system  100  configured to host a benefit analysis system  102  according to one embodiment. Like the hardware system  100  depicted in  FIG. 1 , the hardware system  100  in  FIG. 6  includes a server computing device  104  and a user computing device  106  that are operatively coupled by a network  108 . The server computing device  104  includes an input/output module  120 , a memory module  122  and a processor  124 . The user computing device  106  includes an input/output module  120 , a memory module  122  and a processor  124 . The input/output modules  120  are configured to communicate with and facilitate the receipt and transfer of benefit qualification data. The memory modules  122  are configured to store benefit qualification data, in proper formats for benefit qualification data acquisition and benefit qualification status determination. The processors  124  in the server computing device  104  and the user computing device  106  are configured to respectively run programs (e.g., benefit analysis system  102  and browser  112 ). The benefit analysis system  102  includes a benefit calculation engine  110  configured to modify a completeness graph based on a benefit program qualification requirement change, and run profiles corresponding to individuals against the modified completeness graph corresponding to benefit programs with the benefit program qualification requirement change. The benefit analysis system  102  also includes a user interface manager  126  configured to generate a user interface for communication with a user. The browser  112  is configured to transmit information to and receive information from the benefit analysis system  102  (i.e., the benefits calculation engine  110  and the user interface manager  126 ). The browser  112  facilitates user interaction with the benefit analysis system  102  (i.e., the benefits calculation engine  110  and the user interface manager  126 ). 
       FIG. 7  generally illustrates certain components of a computing device  200  that may be utilized to execute embodiments and that includes a memory  210 , program instructions  212 , a processor or controller  220  to execute instructions  212 , a network or communications interface  230 , e.g., for communications with a network or interconnect  240  between such components. The memory  210  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  220  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 computing device or a hand held mobile communications device), the interconnect  240  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 network interface  230  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 computing device  200  may be located remotely and accessed via a network. Accordingly, the system configuration provided in  FIG. 7  is provided to generally illustrate how embodiments may be configured and implemented. The processors  124  in the server computing device  104  and the user computing device  106  are respectively programmed with the benefit analysis system  102  and the browser  112  so that they are no longer generic computing devices. 
     Having described various general hardware and software aspects of benefit analysis systems according to various embodiments, the benefit analysis software will now be described in greater detail, including data structures therein. 
     In existing systems, a user walks through a set of rigidly defined user interface interview screens that selectively ask questions that are relevant to a particular benefit program. In contrast to the rigidly defined user interface screens used in prior iterations of benefit analysis software, the embodiments described herein provide a benefit analysis system  102  that runs on server computing devices  104  (as seen in  FIGS. 1 to 4 ) that operates on a new construct in which benefit qualification rules and the determinations based thereon are established in declarative data-structures, namely, one or more completeness/completion graphs  12  (see  FIG. 8 ). Completeness graphs  12  are data structures in the form of graphs having interconnecting nodes  20  in which arcs  22  are directed from one node to another (e.g.,  22   a  from  20   a  to  20   b ). Completeness graphs  12  identify when all conditions have been satisfied to complete a particular benefit program qualification determination (i.e., whether an individual qualifies for a particular benefit program). Use of these data-structures permits the user experience to be loosely connected or even divorced from the benefit calculation engine and the data used in the perfect qualification determinations. Benefit qualification is dynamically determined based on benefit qualification data derived from sourced data, estimates, user input, etc. 
     The completeness graphs  12  identify when a particular benefit program qualification determination has been completed or additional information is needed. The benefit calculation engine can review current run time data and evaluate missing data fields as identified by the completeness graphs  12  and propose suggested questions to be asked to a user to fill in missing blanks. This process can be continued until completeness of all benefit program qualification completeness graphs has occurred. Then the benefit analysis system can present a list of benefit programs for which the individual qualifies in the relevant jurisdictions. 
     According to one embodiment, a computer-implemented method for generating a notification or other visual indicia reflective of changes in benefit qualification status over different benefit qualification periods (e.g., year-over-year) is provided. The method uses a computing device executing a benefit calculation engine that operates as part of the benefit analysis system. The benefit calculation engine operates on a different benefit qualification completeness graphs for different benefit qualification periods (e.g., different tax years) to perform respective benefit qualification determinations. For example, there may be a current year benefit qualification completeness graph and a prior year benefit qualification completeness graph for the immediately preceding benefit qualification year. 
     The completeness graph  12  represents data structures that can be constructed in the form of tree.  FIG. 8  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 an individual qualifies to receive benefits under the CalFresh benefit program. Each node  20  contains a condition or state 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. 8 , there are several pathways to completion. For example, one pathway to completion is where a negative (F) answer is given to the question of whether you are a US citizen  20   a  and a negative (F) answer is given to the question of whether you are a US permanent resident  20   c . If these condition are false, the individual does not qualify for CalFresh benefits because under CalFresh rules you cannot claim benefits if you are neither a US Citizen or US permanent resident. In another example, if an individual is a US citizen and their net household income is less than 200% of the federal poverty level (“FPL”), then the individual qualifies to receive CalFresh benefits. Again, this is a separate set of CalFresh requirements. 
     As one can imagine given the complexities and nuances of the benefit program rules and regulations, many benefit program qualification determinations may contain completeness graphs  12  that have many nodes with a large number of pathways to completion. However, many branches or lines  22  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. 9  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 completeness graph  12  that is illustrated in  FIG. 9  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 completeness graph  12  in a recursive manner one can determine each path from the beginning node  20   a  to the termination node  20   y . The completeness 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. 10 , 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 completeness graph  12 . In the illustrated embodiment, the columns  34   a - g  of the completeness graph represent expressions for each of the questions (represented as nodes A-G in  FIG. 9 ) and answers derived from completion paths through the completeness graph  12  and column  34   h  indicates a conclusion, determination, result or goal  34   h  concerning a benefit program qualification determination, e.g., “Yes—you qualify for the benefit program” or “No—your do not qualify for the benefit program.” 
     Referring to  FIG. 10 , each row  32  of the decision table  30  represents a benefit qualification rule. The decision table  30 , for example, may be associated with a federal benefit qualification rule or a state benefit qualification rule (or private benefit qualification rule). In some instances, for example, a state benefit qualification rule may include the same decision table  30  as the federal benefit qualification rule. The decision table  30  can be used, as explained herein, to drive a personalized interview process for the user of benefit analysis system  102 . 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 completeness graph  12  from  FIG. 9  converted into the decision table  30  of  FIG. 10 , 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 benefit qualification 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 completeness 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 an initial question has been presented and rows are eliminated as a result of the selection, 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. 11  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  to 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 benefit qualification determinations. The statistical data  36  may be obtained from a proprietary source of data such as benefit filing data owned by a company running a benefit analysis system  102 . 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 benefit program or the like (e.g., CalFresh). 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 individuals applying for benefit programs, which is then classified one or more classifications. For example, statistical data  36  can be organized with respect to age, employment status and type, 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 individuals under the age of thirty-five where Rule 1  is satisfied. Column  38   b  (STAT2) may contain a percentage value that indicates individuals 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 benefit analysis system  102  to determine which of the candidate questions (Q A -Q G ) should be asked to a user/individual. The statistical data  36  may be compared to one or more known benefit qualification data fields (e.g., age, income level, immigration 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 benefit qualification 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 user/individual. Predictive models based on the statistical data  36  may be used to determine candidate questions. 
       FIG. 12  illustrates a version of a benefit qualification completeness graph  12 ′ for a second benefit qualification period (e.g., a later year). The benefit qualification completeness graph  12 ′ is similar in many respects with the benefit qualification completeness graph  12  of  FIG. 8  with three differences. As seen in  FIG. 12 , the permanent residency for five years requirement as represented by node  20   d  in  FIG. 8  is no longer present in the benefit qualification completeness graph  12 ′ in  FIG. 12 . This disappearance of this requirement in the second benefit qualification. May have been the result of a change in the CalFresh qualification rules and regulations. Similarly, the net family income requirement as represented by node  20   b  in  FIG. 8  as seen in  FIG. 8  has been reduced from 200% of FPL to 150% of FPL. Also, a new immigration status has appeared; the asylum status which is stored in node  20   e . These changes are not uncommon occurrences as benefit rules and regulations do change and evolve over time. In the context of benefit qualification completeness graphs  12 ,  12 ′, note how the larger structure of the tree largely remains intact except for the addition of node  20   e  and the changes to nodes  20   d  and  20   b . Additional changes may occur beyond merely adding, removing or changing nodes from the benefit qualification completeness graph when comparing different benefit qualification years. 
     Often benefit qualification rules and regulations, and the like change from year to year. In such instances, the various nodes  20  and arcs  22  in the benefit qualification completeness graphs  12  may be modified from year-to-year. As one example, the reduction in the net also income may result in an individual not qualifying for CalFresh benefits in the second benefit qualification period corresponding to the completeness graph  12 ′ in  FIG. 12 . 
       FIG. 13  illustrates a differential benefit qualification completeness graph  15  that has a similar hierarchy as benefit qualification completeness graphs  12 ,  12 ′. In addition to the nodes  20   a  to  20   e  in benefit qualification completeness graph  12 ′, the differential benefit qualification completeness graph  15  contains differential nodes  20   a ′,  20   b ′,  20   c ′,  20   d ′,  20   e ′, that each contain a value that corresponds to the differences in the Boolean value stored at that particular common node found in benefit qualification completeness graphs  12 ,  12 ′. More particularly, the value contained within each differential node  20   a ′,  20   b ′,  20   c ′,  20   d ′,  20   e ′ of the differential benefit qualification completeness graph  15  represents the change in Boolean value at the particular node from the second benefit qualification period compared to the value at the same particular node from the first benefit qualification period. The differential benefit qualification completeness graph  15  also includes node  20   e ′ for added node  20   e . Thus, nodes  20   d ′ and  20   b ′ show that the individual no longer qualifies for CalFresh benefits in the second benefit qualification period. Nodes  20   e  and  20   e ′ are uncommon nodes in that they are not shared within benefit qualification completeness graphs  12  and  12 ′. 
     Still referring to  FIG. 13 , each node  20   b ,  20   d ,  20   e  having a change is associated with a respective textual explanation that succinctly captures the difference in the particular node. For example, node  20   b  includes an explanation that “net income lower by 50% FPL.” Node  20   d  includes an explanation that “lost permanent residency five year requirement.” Node  20   e  includes an explanation  27   c  that “asylum new node.” 
     The value of each differential node  20   a ′,  20   b ′,  20   c ′,  20   d ′,  20   e ′ may be stored in a memory location that is mapped according the hierarchy of the differential benefit qualification completeness graph  15 . In addition, portions of the text for each explanation  20   b ,  20   d ,  20   e  may be stored within respective memory locations that are tied to particular differential nodes  20   b ′,  20   d ′,  20   e ′ within the differential benefit qualification completeness graph  15 . For example, pointers or the like may direct a specific explanation node  20   b ,  20   d ,  20   e  to its corresponding differential node  20   b ′,  20   d ′,  20   e ′, and  26   j ′. The particular wording of the explanation may change depending on the Boolean value contained within the differential nodes. For example, appropriate wording for the explanation  20   b ,  20   d ,  20   e  can be generated in response to the value (e.g., qualify/not qualify) of the corresponding differential node  20   b ′,  20   d ′,  20   e′.    
     In one aspect, all or a portion of the text for an explanation  20   b ,  20   d ,  20   e  may be pre-generated and stored for later retrieval. The explanations  20   b ,  20   d ,  20   e  may include aspects that change depending on the value contained in the differential nodes  20   b ′,  20   d ′,  20   e ′ of the differential benefit qualification completeness graph  15 . In other embodiments, the explanations  20   b ,  20   d ,  20   e  may be generated in a natural language format using a natural language generator  16   114  (described herein in more detail). 
       FIG. 14  schematically illustrates a system  40  for determining benefit qualification status using rules and calculations based on a declarative data structures according to one embodiment. The system  40  include a shared data store  42  that contains therein a schema  44  or canonical model representative to the data fields utilized or otherwise required to determine benefit qualification status. The shared data store  42  may be a repository, file, or database that is used to contain the benefit-related data fields. The shared data store  42  is accessible by a computing device  104  as described herein (e.g.,  FIGS. 1 to 4 ). The shared data store  42  may be located on the computing device  104  running the benefit analysis system  102  or it may be located remotely, for example, in a cloud environment on another, remotely located computer. 
     The schemas  44  may vary depending on the different benefit programs. For example, CalFresh may have a benefit schema  44  that varies from California Head Start. 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 determine qualification for benefit programs. This may include, for example, all fields required for any benefit application forms, 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 schema  44  stored therein at any particular time. For example,  FIG. 14  illustrates several instances  46  of the schema  44  (labeled as SCH 1 , SCH 2 , and SCHN). These instances  46  may be updated as additional data is input into the shared data store  42 . 
     As seen in  FIG. 14 , the shared data store  42  may import data from one or more data sources  48 . A number of data sources  48  may be used to import or otherwise transfer benefit qualification data to the shared data store  42 . This may occur through a user interface control  80  as described herein or, alternatively, data importation may occur directly to the shared data store  42  (not illustrated in  FIG. 14 ). The benefit qualification data may include personal identification data such as a name, address, or age. Benefit qualification data may also relate to, for example, details regarding an individual&#39;s employment during a preceding tax year. This may include, employer name, employer federal ID, dates of employment, and the like. Benefit qualification data may include residential history data (e.g., location of residence(s) in benefit qualification period (state, county, city, etc.) as well as type of housing (e.g., rental unit or purchased home). Benefit qualification data may also include dependent-related information such as the number of family members in a household including children. Benefit qualification data may pertain to sources of income, including both earned and unearned income as well. Benefit qualification data also include information that pertains to tax deductions or tax credits. Benefit qualification data may also pertain to medical insurance information. In some embodiments, benefit qualification data can be automatically imported into the shared data store  42  (e.g., using an application programming interface or “API”). 
     User input  48   a  is also one type of data source  48 . User input  48   a  may take a number of different forms. For example, user input  48   a  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 benefit analysis system  102 . For example, as illustrated in  FIG. 14 , user interface manager  82  contains an import module  89  that may be used to select what data sources  48  are automatically searched for benefit qualification data. Import module  89  may be used as a permission manager that includes, for example, user account numbers and related passwords. The UI control  80  enables what sources  48  of data are searched or otherwise analyzed for benefit qualification data. For example, a user may select prior year benefit qualification files  48   b  to be searched but not online resources  48   c . The benefit qualification data may flow through the UI control  80  directly as illustrated in  FIG. 14  or, alternatively, the benefit qualification data may be routed directly to the shared data store  42 . The import module  89  may also present prompts or questions to the user via a user interface presentation  84  generated by the user interface manager  82 . For example, a question or prompt may ask the user to confirm the accuracy of the data. For instance, the user may be asked to click a button, graphic, icon, box or the like to confirm the accuracy of the data prior to or after the data being directed to the shared data store  42 . Conversely, the interface manager  82  may assume the accuracy of the data and ask the user to click a button, graphic, icon, box or the like for data that is not accurate. The user may also be given the option of whether or not to import the data from the data sources  48 . 
     User input  48   a  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 benefit analysis system  102  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 benefit qualification files  48   b . A prior year benefit qualification files  48   b  that is stored electronically can be searched and data is copied and transferred to the shared data store  42 . The prior year benefit qualification files  48   b  may be in a proprietary format (e.g., .txf, .pdf) or an open source format. The prior year benefit qualification files  48   b  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 benefit qualification files  48   b  may be obtained by accessing a benefit agency database (e.g., CalFresh records). 
     An additional example of a data source  48  is an online resource  48   c . An online resource  48   c  may include, for example, websites for the taxpayer(s) that contain benefit qualification data. 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  48   c  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&#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 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  FIG. 14 , another data source  48  includes sources of third party information  48   d  that may be accessed and retrieved. For example, credit reporting bureaus contain a rich source of data that may implicate one or more benefit programs. For example, credit reporting bureaus may show that a taxpayer has taken out a student loan or home mortgage loan that may be the source of possible benefit program qualification. Other examples of sources of third party information  48   d  include government databases. For example, the state department of motor vehicles may contain information relevant to some benefit programs (e.g., electric car rebates). Other government databases that may be accessed include the IRS (e.g., IRS tax return transcripts), and state taxing authorities. Third party resources  48   d  may also include one of the state-based health insurance exchanges or the federal health insurance exchange (e.g., www.healthcare.gov). 
     Referring briefly to  FIG. 1 , the benefit analysis system  102  including the system  40  of  FIG. 14  is executed by the server computing device  104 . Referring back to  FIG. 14 , the benefit analysis system  102  includes a benefit calculation engine  50  that performs one or more benefit qualification status determinations based on the available data at any given instance within the schema  44  in the shared data store  42 . The benefit calculation engine  50  may determine a benefit program qualification or disqualification. The benefit calculation engine  50  utilizes the one or more benefit qualification completeness graphs  12  as described previously in the context of  FIGS. 8 to 13 . In one embodiment, a series of different completeness graphs  12  are used for respective benefit program qualification determinations. These different completeness graphs  12  may be glued together or otherwise compiled as a composite completeness graph  12  to obtain a total benefit amount based on the information contained in the shared data store  42 . The benefit calculation engine  50  reads the most current or up to date information contained within the shared data store  42  and then performs benefit qualification status determinations. Updated benefit qualification status values are then written back to the shared data store  42 . As the updated benefit qualification status values are written back, new instances  46  of the canonical model  46  are created. The benefit qualification status determinations performed by the benefit calculation engine  50  may include the calculation of an overall benefit amount. The benefit qualification status determinations may also include per program determinations used to determine an overall benefit amount. 
     Still referring to  FIG. 14 , the system  40  includes a benefits logic agent (BLA)  60 . The BLA  60  operates in conjunction with the shared data store  42  whereby updated benefit qualification data represented by instances  46  are read to the BLA  60 . The BLA  60  contains run time data  62  that is read from the shared data store  42 . The run time data  62  represents the instantiated representation of the canonical benefit schema  44  at runtime. The BLA  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 BLA  60  may be implemented as a dedicated module contained within or executed in connection with the benefit analysis system  102 . 
     As seen in  FIG. 14 , The BLA  60  uses the decision tables  30  to analyze the run time data  62  and determine whether a benefit qualification status determination is complete. Each decision table  30  created for each benefit program or sub-topic is scanned or otherwise analyzed to determine completeness for each particular benefit program 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 benefit programs or sub-topics that are not complete, which, as explained in more detail below presents interview questions to a user for answer. The BLA  60  identifies a decision table  30  corresponding to one of the non-complete benefit programs 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 or compilation of one or more questions (e.g., Q 1  to Q 5  as seen in  FIG. 14 ) 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  to 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 benefit qualification status 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 prior year benefit qualification files, demographic information, financial information and the like may be used as part of this ranking process. 
     The BLA  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 individuals residing within a particular zip code are more likely to be homeowners than renters. The BLA  60  may use this knowledge to weight particular benefit programs or questions related to these benefit programs. For example, in the example given above, questions relevant to home mortgage assistance programs may be promoted or otherwise given a higher weight. The statistical knowledge may apply in other ways as well. For example, individuals&#39; professions may be associated with transactions that may affect benefit qualification status. For instance, an individual may list their 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 BLA  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 benefit qualification data of a large body of individuals. For example, entities having access to tax filings may be able to mine their own proprietary data to establish connections and links between various individual characteristics and benefit program qualification determinations. 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 individual but is rather generalized to characteristics shared across a number of individuals although in other embodiments, the data may be more specific to an individual. 
     Still referring to  FIG. 14 , the UI controller  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  may manifest itself, typically, on a visual screen or display  114  that is presented on a computing device  106  (seen, for example, in  FIG. 4 ). 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  114  that is utilized by the computing device  106 . 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  106 . The nature of the interface presentation  84  may not only be tied to a particular computing device  106  but different users may be given different interface presentations  84 . For example, an individual 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 BLA  60 . The non-binding suggestions may include a single question or multiple questions that are suggested to be displayed to the individual 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. 14 , the UI controller  80  interfaces with the shared data store  42  such that data that is entered by a user in response to the user interface presentation  84  can then be transferred or copied to the shared 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 benefit analysis system  102  using an input device that is associated with the computing device  106 . For example, an individual may use a mouse, finger tap, keyboard, stylus, voice entry, or the like to respond to questions. The individual 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 individual. For example, with regard to dependent children, the individual may be prompted to provide more details about the dependent children to satisfy requirements set by a particular benefit program. 
     Still referring to  FIG. 14 , in one aspect, the BLA  60  outputs a current benefit qualification result  65  which can be reflected on a display  114  of a computing device  106 . For example, the current benefit qualification result  65  may illustrate a certain number of qualified benefit programs. The current benefit qualification results  65  may also illustrate various other intermediate calculations or operations used to determine benefit qualification status. For example, different discrete topics such as Adjusted Gross Income may be illustrated. This information may be displayed contemporaneously with other information, such as user input information, or user interview questions or prompts or even narrative explanations as explained herein. 
     The BLA  60  also outputs a benefit qualification data that can be used in a variety of ways. For example, the system  40  includes a services engine  90  that is configured to perform a number of tasks or services for the individual. For example, the services engine  90  can include a printing option  92 . The printing option  92  may be used to print a summary of qualified and not qualified benefit programs, completed benefit program application forms, and the like. The services engine  90  may also electronically file  94  or e-file a benefit program application with a benefit program. Whether a paper or electronic application is filed, data from the shared data store  42  required for particular benefit program application forms, and the like is transferred over into the desired format. The services engine  90  may also make one or more recommendations  96  based on the run-time data  62  contained in the BLA  60 . For instance, the services engine  90  may identify that an individual will not qualify for a benefit program because they have not had a job interview in the last 90 days, and may recommend to the individual to seek out job interviews. 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 benefit qualification algorithm. The calculator  98  can also be used to estimate benefit qualification status based on certain changed assumptions (e.g., how would my benefit qualification change if I was married and had a dependent child?). The calculator  98  may also be used to compare analyze differences between tax years. 
     By using benefit qualification completeness graphs  12  to drive benefit qualification status determinations, the year-over-year benefit qualification completeness graphs  12  can be used to readily identify differences and report the same to a user. Benefit qualification completeness graphs  12  from non-continuous years may also be compared in the same manner. In addition, the benefit qualification completeness graph  12  may include a calculation/Boolean determination that reflects the determination of benefit qualification status according the laws and regulations that will be in effect in a future time period. For example, many times, it is known well in advance about certain benefit qualification rule and regulation changes that have been enacted but will not go into effect until some future date. Benefit qualification completeness graphs  12  for such a future state can be developed and utilized by the individual to engage in benefit qualification planning. For example, it may be preferable to defer certain benefit qualification impacting events until a later date when benefit qualification rules and regulations are more favorable. Conversely, such future benefit qualification completeness graphs may be used to recommend accelerating certain activities into a current benefit qualification year to gain a benefit. Differences can be found using commonly used graph isomorphism algorithms over the two respective benefit qualification completeness graphs  12  to automatically generate the differential benefit qualification completeness graph  15 . Topological differences between the nodes or sub-nodes within the respective benefit qualification completeness graphs  12  can be identified automatically by systematically traversing each node or sub-node in a recursive manner. 
     Referring to  FIG. 14 , the system includes an explanation engine  10  that operates in connection with the benefit analysis system  102  to generate a narrative explanation from the one or more explanations associated with a particular benefit program and associated nodes  20   a ,  20   b ,  20   c ,  20   d ,  20   e ,  20   a ′,  20   b ′,  20   c ′,  20   d ′,  20   e ′ (illustrated in  FIGS. 8, 12 and 13 ). To generate the narrative explanation for a node  20   a ,  20   b ,  20   c ,  20   d ,  20   e ,  20   a ′,  20   b ′,  20   c ′,  20   d ′,  20   e ′ and the associated benefit program, the explanation engine  10  extracts the particular narrative associated with the node  20   a ,  20   b ,  20   c ,  20   d ,  20   e ,  20   a ′,  20   b ′,  20   c ′,  20   d ′,  20   e ′ or benefit program. For example, a “cap” function and associated node may be associated with an explanation of “value exceeds cap.” This brief explanation can be combined with a stored explanation or narrative that is associated with the particular functional node  20   a ,  20   b ,  20   c ,  20   d ,  20   e ,  20   a ′,  20   b ′,  20   c ′,  20   d ′,  20   e ′ within the benefit qualification completeness graph  12 . For example, the functional node  26  paired with the stored “cap” function  28  gives a contextual tax explanation in that is more than merely “value exceeds cap.” For instance, a pre-stored narrative associated with the particular functional node  20  having to do with the child tax credit within the benefit qualification completeness graph  12  may be a complete statement or sentence such as “You do not qualify for CalFresh because your income is too high.” In other embodiments, the pre-stored narrative may be only a few words or a sentence fragment. In the above example, the pre-stored narrative may be “income too high.” The narrative may also explain how this particular benefit program or sub-topic changes over different benefit qualification periods as explained in the context of  FIGS. 8, 12 and 13 . For example, the narrative may include “the income Is lowered by 25%.” A particular functional node  20  may have multiple pre-stored narratives. The particular narrative(s) that is/are associated with a particular functional node  20  may be stored in entries  14  in a memory, data store or database such as data store  42  of  FIG. 14 . For example, with reference to  FIG. 14 , data store  42  contains the pre-stored narratives that may be mapped, pointed to, or otherwise tagged to particular functional nodes  20  contained within the benefit qualification completeness graphs  12 . The locations or addresses of the various functional nodes  20  can be obtained using the benefit qualification completeness graphs  12 . 
     These stored entries  14  can be recalled or extracted by the explanation engine  10  and then displayed to a user on a display  114  of a computing device  106 . For example, explanation engine  10  may interface with the UI control  80  in two-way communication such that a user may be ask the benefit analysis system  102  why a particular benefit qualification status determination has been made by the system  40 . For instance, the user may be presented with an on-screen link, button, or the like that can be selected by the user to explain to the user why a particular benefit qualification status determination was made by the benefit analysis system  102 . For example, in the context of  FIG. 12  described herein, a user may see that they no longer qualify for CalFresh on the screen of the computing device  106 . The individual may be interested in why they no longer qualify. As one example, the initial explanation provided to the user may be “you don&#39;t qualify because your income is too high.” This explanation may be associated with, for example, function node  20   b  in  FIG. 12 . In some instances, a user is able to further “drill down” with additional questions to gain additional explanatory detail. This additional level of detailed explanations is possible by retracing the benefit qualification completeness graphs  12  to identify a predecessor or upstream functional node  20 . In the context of the example listed above, a user may not be satisfied and may want additional explanation. In this instance, for example, the word “income” may be highlighted or linked with a hyperlink. A user clicking on this would then be provided with additional explanation on the detail regarding CalFresh qualification. In this example, the user may be provided with “CalFresh qualification requires net household income to be less than 150% of FPL. In your situation, the net household income exceeded the cap.” This particular explanation may be associated with the predecessor function node  20   b  in  FIG. 8 . Additional details may be provided by further retracing, in a backwards fashion, the benefit qualification completeness graph  12 . 
     With reference to  FIG. 14 , the explanation engine  10  may also automatically generate explanations that are then communicated to the user interface manager  80 . The automatically generated explanations may be displayed on a display  114  associated with the computing device  106 . In some embodiments, the explanations may be contemporaneously displayed alongside other benefit qualification data and/or determinations. For example, as a user inputs their information into the benefit analysis system  102  and determinations are automatically updated, explanations maybe automatically displayed to the user. These explanations maybe displayed in a side bar, window, panel, pop-up (e.g., mouse over), or the like that can be followed by the user. The explanations may also be fully or partially hidden from the user which can be selectively turned on or off as requested by the user. 
     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  20  may be selected by the explanation engine  10  for display to a user based on the user&#39;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 benefits 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 benefit analysis system  102  may little or no explanations. In a more advanced edition (e.g., “Deluxe Edition” or “Home and Business Edition”), additional explanation is provided. Still more explanation may be provided in the more advanced editions of the benefit analysis system  102  (e.g., “Premier edition”). Version of the benefit analysis system  102  that are developed for benefit professionals 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 benefit analysis system  102 . 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 benefit analysis system  102  itself using known methods of payment. 
     The explanation engine  10  in  FIG. 14  also includes a natural language generator  16  that converts fragments, expressions or partial declaratory statements into natural language expressions that are better understood by users. The natural language expressions may or may not be complete sentences but they provide additional contextual language to the more formulaic, raw explanations that may be tied directly to the explanation associated with a function node  20 . For example, a brief explanation extracted by the explanation engine  10  which indicates that the individual does not qualify for CalFresh because their net household income is too high is then subject to post-processing to convert the same into a more understandable sentence that can be presented to the user. The user is provided with a natural language explanation that is more readily understood by users (e.g., “You are not eligible for the CalFresh program because your net household income is above the limit set by CalFresh.”) 
     In one aspect of the invention, the natural language generator  16  may rely on artificial intelligence or machine learning such that results may be improved. For example, the explanation engine  10  may be triggered in response to a query that a user has typed into a free-form search box within the benefit analysis system  102 . The search that has been input within the search box can then be processed by the explanation engine  10  to determine what benefits program determination the user is inquiring about and then generate an explanatory response. 
     Narrative explanations and associated sub-explanations can be constructed as an explanation tree with the root of the tree representing a particular benefit program qualification determination. The explanation trees are readily constructed based on the function nodes  20  contained within the benefit qualification completeness graph  12 . For example, one is able to “drill down” into more detailed explanations by walking up the directed graph that forms the benefit qualification completeness graph  12 . For example, the initial explanation that is displayed on the screen may be associated with node D of the benefit qualification completeness graph  12  of  FIG. 3 . By selecting a hyperlink, a predecessor node (e.g., node B) is used to generate the narrative explanation. Yet another predecessor node (node A) can also be used to generate the narrative explanation. In this manner, explanations can be presented to the user in a recursive manner by reversely traversing the benefit qualification completeness graph  12 . Conversely, walking progressively down the benefit qualification completeness graph  12  shows how one node  20  affects other downstream nodes  20 . 
     Note that the system may also work with partial benefit qualification data for the current benefit qualification year and does not necessarily require that the current year benefit qualification data be complete. For example, explanations may be provided to the user during the interview or data capture process with explanations reflected the then-current state of data for the current benefit qualification year. In this regard, explanations may be provided to the user in real-time or near real-time as data is input by the user as part of the interview process or is automatically captured and stored within, for example, data store  42 . Likewise, the invention will also work on completed benefit qualification data for the current year which will provide the most accurate explanations for differences in benefit program qualification over different benefit qualification periods. 
     Encapsulating the benefit qualification rules and regulations within benefit qualification completeness graphs  12  results in much improved testability and maintainability of the benefit analysis system  102 . Software bugs can be identified more easily when the benefit qualification completeness graphs  12  are used because such bugs can be traced more easily. In addition, updates to the benefit qualification completeness graphs  12  can be readily performed when benefit qualification rules or regulations change with less effort. 
     Further, the degree of granularity in the explanations that are presented to the user can be controlled. Different levels of details can be presented to the user. This can be used to tailor the benefit analysis system  102  to provide scalable and personalized benefit qualification explanations to the user. The narrative explanations can be quickly altered and updated as needed as well given that they are associated with the completeness graphs  12  and are not hard coded throughout the underlying software code for the benefit analysis system  102 . 
     Note that one can traverse the benefit qualification completeness graphs  12  in any topologically sorted order. This includes starting at a node  20  and working forward through the benefit qualification completeness graph  12 . Alternatively, one can start at the final or terminal node  20  and work backwards (e.g., recursively). One can also start at in intermediate node  20  and traverse through the directed graph in any order. By capturing the benefit qualification rules and regulations in the completeness graph  12 , targeted determinations can be made for benefit program qualification or related sub-topics. Of course, there are many such benefit qualification completeness graphs  12  for the various benefit programs or sub-topics. This has the added benefit that various benefit programs or sub-topics can be isolated and examined separately in detail and can be used to explain intermediate operations and determinations that are used to generate a final benefit qualification status. For example, custom-created benefit calculators on various benefit aspects can be generated (e.g., income, immigration status, and the like). 
     While the benefits calculation engine  50 , the benefits logic agent  60  and explanation engine  110  are depicted in  FIG. 14  as separate components, in other embodiments, the benefits calculation engine  50 , the benefits logic agent  60  and explanation engine  110  may be all part of one component. For instance, the benefits logic agent  60  and explanation engine  110  may be parts of the benefits calculation engine  50 . 
     Having described various aspects of benefit analysis systems  102  according to various embodiments, computer-implemented methods for providing notice when there is a benefit program qualification status change using a benefit analysis system  102  will now be described. The methods also include modifying a completeness graph based on a benefit program qualification requirement change, and running profiles corresponding to individuals against the modified completeness graph. 
       FIG. 15  depicts a computer-implemented method/algorithm  300  for providing notice when there is a benefit program qualification status change according to one embodiment. The method  300  may be implemented using a benefit analysis system (e.g.,  102 ) running on a hardware system (e.g.,  100 ,  100 ′,  100 ″) depicted in  FIGS. 1 to 6 . 
     At step  302 , the system  100 ,  100 ′,  100 ″ (in particular, the input output module  120 ) receives a benefit program qualification requirement change. The benefit program qualification requirement change may be received by a server computing device  104  from a benefit qualification data source computer  116  (e.g., as shown in  FIG. 3 ) or directly from a programmer through a user interface. The benefit qualification data source computer  116  may be a third party computer, as described above. In embodiments where the profile (D) is obtained from a benefit qualification data source computer  116 , the profile (D) may be obtained without user intervention. In some embodiments, all benefit program qualification requirement changes are automatically sent to the server computing device  104 . The benefit program qualification requirement change may be information that affects the individual&#39;s qualification for various benefit programs. 
     At step  304 , the system  100 ,  100 ′,  100 ″ (in particular, the input output module  120 ) obtains a completeness graph (G i ) corresponding to a particular benefit program for which the qualification requirement change is received in step  302 . As described above, the completeness graph (G i ) includes nodes and arcs that represent the rules and regulations for qualifying for the benefit program in a tree structure as shown in  FIGS. 8 and 12 . The completeness graph (G i ) corresponds to the benefit program qualification requirements before the requirement change. 
     At step  306 , the system  100 ,  100 ′,  100 ″ (in particular, the benefits calculation engine  110  of the benefit analysis system  102 ) modifies the completeness graph (G i ) based on the benefit program qualification requirement change. The benefits calculation engine  110  can modify the completeness graph (G i ) automatically or with the help of a programmer through user interface. 
     At step  308 , the system  100 ,  100 ′,  100 ″ (in particular, the input output module  120 ) obtains a profile (D) corresponding to with an individual. The profile (D) may be obtained from a previous benefit analysis (e.g., last year&#39;s benefit analysis) stored in a memory in a server computing device  104 . Alternatively, the profile (D) may be obtained from a benefit qualification data source computer  116  (e.g., as shown in  FIG. 3 ) or directly from a user through a user interface. The benefit qualification data source computer  116  may be a third party computer, as described above. In embodiments where the profile (D) is obtained from a benefit qualification data source computer  116 , the profile (D) may be obtained without user intervention. 
     At step  310 , the system  100 ,  100 ′,  100 ″ (in particular, the input output module  120 ) obtains a first qualification status corresponding to the profile (D)/individual and the completeness graph (G i ) before the benefit program qualification requirement change. The first qualification status may be obtained from a previous benefit analysis (e.g., last year&#39;s benefit analysis) stored in a memory in a server computing device  104 . The first qualification status may be yes or no. 
     At step  312 , the system  100 ,  100 ′,  100 ″ (in particular, the benefits calculation engine  110  of the benefit analysis system  102 ) runs the profile (D) against the modified completeness graph (G i ) to determine a second qualification status. As described above, the completeness graphs (G) each include nodes and arcs that represent the rules and regulations for qualifying for the benefit program in a tree structure as shown in  FIGS. 8 and 12 . The second qualification status may be yes or no or undetermined due to changes in the benefit program qualification requirements. 
     At step  314 , the system  100 ,  100 ′,  100 ″ (in particular, the benefits calculation engine  110  of the benefit analysis system  102 ), when the first and second qualification statuses are different, provides a notice of the corresponding benefit qualification status change. The notice can be provided through a user interface. 
       FIG. 16  depicts another computer-implemented method/algorithm  300 ′ for providing notice when there is a benefit program qualification status change according to another embodiment. The method  300 ′ may be implemented using a benefit analysis system (e.g.,  102 ) running on a hardware system (e.g.,  100 ,  100 ′,  100 ″) depicted in  FIGS. 1 to 6 . Steps  302 ,  306 ,  312 ,  314  of the method  300 ′ depicted in  FIG. 16  are identical to corresponding steps  302 ,  306 ,  312 ,  314  of the method  300  depicted in  FIG. 15 . 
     However, in method  300 ′ steps  304 ,  308 , and  310  are replaced by step  316 , which is inserted between remaining steps  302  and  306 . In step  316 , the system  100 ,  100 ′,  100 ″ (in particular, the input output module  120 ) obtains a schema  44 / 46 . The schema  44 / 46  includes a completeness graph (G i ) corresponding to a particular benefit program for which the qualification requirement change is received in step  302 . As described above, the completeness graph (G i ) includes nodes and arcs that represent the rules and regulations for qualifying for the benefit program in a tree structure as shown in  FIGS. 8 and 12 . The completeness graph (G i ) corresponds to the benefit program qualification requirements before the requirement change. 
     The schema  44 / 46  also includes a profile (D) corresponding to with an individual. The schema  44 / 46  further includes a first qualification status corresponding to the profile (D)/individual and the completeness graph (Gi) before the benefit program qualification requirement change. The schema  44 / 46  may be obtained from a previous benefit analysis (e.g., last year&#39;s benefit analysis) stored in a memory in a server computing device  104 . The first qualification status may be yes or no. The schema  44 / 46  data structure can be stored in the shared data store  42  depicted in  FIG. 14  and increases the efficiency of processing completeness graphs (G). 
       FIGS. 17 and 18  depicts still two other computer-implemented methods/algorithms  300 ″,  300 ′″ for providing notice when there is a benefit program qualification status change according to still two other embodiments. The methods  300 ″,  300 ′″ may be implemented using a benefit analysis system (e.g.,  102 ) running on a hardware system (e.g.,  100 ,  100 ′,  100 ″) depicted in  FIGS. 1 to 6 . Steps  302 ,  304 ,  306 ,  308 ,  310 ,  312 ,  314  of the method  300 ″ depicted in  FIG. 17  are identical to corresponding steps  302 ,  304 ,  306 ,  308 ,  310 ,  312 ,  314  of the method  300  depicted in  FIG. 15 . Steps  302 ,  306 ,  312 ,  314 ,  316  of the method  300 ′″ depicted in  FIG. 18  are identical to corresponding steps  302 ,  306 ,  312 ,  314 ,  316  of the method  300 ′ depicted in  FIG. 16 . 
     However, in methods  300 ″ and  300 ′″ step  318  is inserted after steps  314 . In step  318 , the system  100 ,  100 ′,  100 ″ (in particular, the benefits calculation engine  110  of the benefit analysis system  102  and explanation engine  10  running on the server computing device  104 ) provides a recommendation corresponding to the benefit qualification status change. As discussed above, these recommendations can help individuals plan for their individual and household budgets, and effect changes that can avoid the benefit qualification status change. An exemplary recommendation would be “You should seek out some job interviews to help you qualify for unemployment benefits.” 
     Providing notifications and recommendations corresponding to benefit qualification status changes can help individuals with their spending and saving decisions. It can also help individuals with other life decisions that may affect their benefit qualification status. Providing notifications and recommendations in real time in response to benefit program qualification requirement change maximizes the amount of time individuals have to make changes in response to the requirement change. 
     The embodiments described herein improve the efficiency of computing devices used for providing notifications and recommendations regarding benefit qualification status change. The use of completeness graphs and schema, along with the prioritization algorithms described herein, increases processing efficiency and reduces memory footprint size. The embodiments described herein address the computer centric issue of analyzing multiple benefit program qualification requirement changes using completeness graph data structures. The embodiments described herein include transforming user data into profiles, benefit qualification rules and regulations into completeness graphs, profiles, completeness graphs, and qualification status into schema, and benefit program qualification requirement change into notifications and recommendations regarding benefit qualification status changes. The embodiments described herein also improve the technical fields of information storage, information processing, and computer human interactions. 
     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  220  performs steps or executes program instructions  212  within memory  210  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. 
     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 embodiments 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. 
     Where methods and steps described above indicate certain events occurring in certain order, those of ordinary skill in the art having the benefit of this disclosure would recognize that the ordering of certain steps may be modified and that such modifications are in accordance with the variations of the disclosed embodiments. Additionally, certain of the steps may be performed concurrently in a parallel process as well as performed sequentially. Thus, the methods shown in various flow diagrams are not intended to be limited to a particular sequential order, unless otherwise stated or required. 
     Accordingly, embodiments are intended to exemplify alternatives, modifications, and equivalents that may fall within the scope of the claims.