Note: Descriptions are shown in the official language in which they were submitted.
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COMPUTER-IMPLEMENTED SYSTEMS AND METHODS FOR PREPARING
COMPLIANCE FORMS TO MEET REGULATORY REQUIREMENTS
SUMMARY
[0001] Embodiments of the present invention are directed to computerized
systems and methods for preparing and/or submitting compliance forms for
meeting compliance requirements, such as applications and forms for permits,
licenses, immigration cards, tax filings, etc.
[0002] There are many activities of individuals and businesses which are
subject to compliance with rules and regulations. Some examples or such
activities include obtaining permits and licenses to perform certain
activities such
as driver's licenses, vehicle registrations, marriage licenses, business
licenses,
building permits, licenses to sell alcohol, professional licenses (e.g.,
investment
licenses, attorney licenses, insurance licenses, healthcare provider licenses,
etc.), and the like. There are also regulatory compliance requirements for
taxation on property, taxation on business income (e.g., tax returns),
taxation on
personal income (e.g., tax returns), employer payroll tax forms, immigration
procedures, applications for government programs and assistance, etc. In each
case, the compliance entity, be it an individual or business entity, must
submit a
compliance form, such as an application or other form, to satisfy the required
compliance process. Once completed, the compliance form must be submitted
to an official agency in the jurisdiction which is responsible for reviewing
regulatory compliance. The responsible agency may accept paper compliance
forms and/or electronic submissions, depending on the systems and
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requirements of each agency. For example, some agencies have systems which
allow compliance forms to completed and/or submitted in electronic form, while
some agencies may require or allow compliance forms to be completed and
submitted on paper, and some agencies accept both paper forms and electronic
forms. As an example, the Internal Revenue Service ("IRS") accepts both paper
tax returns and electronic tax returns. Some agencies also have their own
websites which allow an entity to complete and submit an electronic compliance
form on the website. Similarly, some private entities, such as Intuit, Inc. of
Mountain View, California, have developed websites and software for preparing
electronic compliance forms, such as tax returns prepared using the website at
VVWW.TURBOTAX.COM TM or Turbotax TM desktop software, as well as
electronically transmitting the completed compliance forms to the responsible
agency.
[0003] Although various computerized systems and software programs have
previously been provided for preparing compliance forms such as tax returns,
there has been no computerized system that supports the processing of a
variety
of different types of compliance forms which is quickly and easily adaptable
to
prepare any of a multitude of different types of compliance forms, while also
allowing changes to compliance requirements to be simply and efficiently
implemented.
[0004] Accordingly, one embodiment of the present invention is directed to
a
computerized compliance form preparation system (also referred to as a
"compliance system" or "compliance form system") for preparing a plurality of
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different types of compliance forms for submission to a respective responsible
agency having the authority to review the respective type of compliance form.
The compliance system may also have the capability to check whether a
compliance form being prepared on the system has any errors and/or meets the
regulatory rules for an acceptable compliance form, provide an explanation of
errors identified in the compliance form, assist a user in correcting any
errors
and/or deficiencies in the compliance form, execute a payment transaction for
the
submission of the compliance form, and/or submit the compliance form to the
responsible agency.
[0005] More specifically, the compliance system includes a computing device
having a computer processor and memory. The compliance system further
includes a data store in communication with the computing device. The data
store is configured to store compliance data specific to a particular entity
for
which a compliance form is being prepared (also referred to as "entity-
specific
compliance data"), such as compliance form data fields and calculated
compliance form data fields. For example, the system may access entity-
specific
compliance data from any suitable source, such as data input by a user, or
data
electronically accessed from a database having applicable entity-specific
compliance data. The data may be input by a user in response to a series of
interview screens that selectively ask questions and/or request compliance
data
needed to complete a compliance form. As some examples, the entity-specific
data may include input data required for completing the compliance form, such
as the entity name, address, tax identification number (e.g., Employer
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Identification Number (EIN), social security number (SSN)), etc. The entity-
specific compliance data also includes data specific to the type of compliance
form. For instance, compliance data for a vehicle registration may require the
vehicle make, model, model year, license plate number, and/or vehicle
identification number. In contrast, compliance data for preparing an employer
payroll tax form may include an EIN, employer name and address, wages, tips
and compensation paid to employees, federal income tax withheld from the
employees, taxable social security wages and tips, taxable Medicare wages and
tips, adjustments, etc.
[0006] The compliance system also includes a compliance form preparation
software application (also referred to as the "compliance program") executable
by
the computing device. The compliance program includes a universal calculation
engine, logic agent and user interface manager which are each configured to
utilize a construct in which the rules and calculations for preparing each
type of
compliance form are established in type-specific domain models having
declarative data structures, rather than being rigidly programmed for
processing
a particular type of compliance form. In other words, the system has a
specific
domain model for each type of compliance form, and the calculation engine,
logic
agent and user interface manager are configured to process each type of
compliance form using the respective domain model for the particular type of
compliance form being prepared by the system. The declarative data structures
for each domain model are embodied in completeness model(s) and calculation
graph(s) which are independent and separate from the calculation engine, logic
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agent and user interface, allowing the universal calculation engine, logic
agent
and user interface to process a plurality of different types of compliance
forms by
utilizing the respective domain model for the particular compliance form.
[0007] For each domain model, the calculation graph(s) comprise a plurality
of
interconnected calculation nodes including one or more input nodes, function
nodes, and/or functional nodes embodying the calculations and logic operations
as defined by the rules and regulations required for preparing the particular
compliance form for such domain model. For a respective domain model, the
calculation graph which includes all of the calculations and logic operations
for a
particular domain model, or a plurality of calculation graphs in which each
calculation graph covers a one or more particular compliance topics and/or sub-
topics.
[0008] The calculation engine is configured to perform a plurality of
calculations and logic operations based on the calculation graph. The
compliance program is executable by the computing device to execute the
calculation engine to establish a connection to the data store, read and write
entity-specific compliance data to and from the shared data store, and perform
calculations and logic operations using the entity-specific compliance data
based
on the calculation graph.
[0009] For each domain model, the completeness model, also referred to
herein as a "completion model," comprises a data structure that captures all
the
conditions necessary to obtain all of the compliance data necessary to
complete
the respective compliance form for submission to a responsible agency. The
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completeness model may be embodied in various forms. The completeness
model may be completeness graph(s) (also referred to as "completion graphs")
such as a decision tree, or the completeness model(s) may be in the form of
decision tables representing compliance questions for obtaining
entity=specific
compliance data and the logic relating the compliance questions to other
compliance questions and/or completion of the a compliance topic or the entire
compliance form (e.g., the decision tables may be generated from completeness
graph(s), as described herein). For instance, answers and/or entry of
compliance data in response to certain compliance data questions are logically
related to other compliance data questions in the decision table and/or a
completion goal for compliance tax topic or the entire compliance form
indicating
that the tax topic or the compliance form is completed. Similar to the
calculation
graph(s), a single completeness graph can comprehensively cover an entire
compliance form, or the completeness graph can be a plurality of completeness
graphs each covering particular compliance topics and/or sub-topics which may
combined to form the overall completeness graph.
[0010] The logic agent is configured to review current run-time data
including
the compliance data currently obtained, evaluate missing compliance data
utilizing the completeness graph, and output one or more suggested compliance
questions for obtaining missing compliance data. The compliance program is
executable by the computing device to execute the logic agent to review
current
run-time data including the compliance data currently obtained, evaluate
missing
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tax data utilizing the completeness graph, and output one or more suggestions
for obtaining missing compliance data to the user interface manager.
[0011] The user interface manager is configured to receive the one or more
suggested compliance questions from the logic agent, and analyze the
suggested compliance question(s). The user interface manager utilizes the
suggested compliance question(s) to determine a compliance question to present
to a user of the compliance system. The user interface manager may select one
of the suggested compliance question(s), or it may ignore the suggestions and
present a different questions or prompt to the user. The user interface
manager
then presents the determined compliance question to the user. For example, the
user interface manager may be configured to generate and display a question
screen to the user. The question screen may include a question for the user
requesting compliance data for an entity and also be configured to receive the
compliance data from the user in the form of input from the user.
[0012] In the event that all compliance data needed to complete the
compliance form has been obtained, the logic agent, instead of outputting one
or
more suggested compliance questions for missing compliance data may output a
"done" instruction to the user interface manager. The compliance program is
configured to then prepare the compliance form based on the compliance data in
the shared data store. For instance, the compliance program may include a
services module configured to utilize the compliance data to prepare and/or
submit the compliance form, such as generating an electronic document of a
completed compliance form, print a paper copy of a completed compliance form,
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and/or electronically transmit a data file representing a completed compliance
form to the responsible agency.
[0013] The computing device may be a remotely located computing device
that is separate from another computing device that contains a user interface.
For example, a user may run a browser or application on a mobile device such
as a laptop, tablet, Smartphone, or the like which contains the user
interface. Of
course, a personal computer may also be used in this manner in which a
remotely located computer is used to implement core functions of the
compliance
program. A remotely located computing device may execute one or more
modules of the system, for example, the calculation engine, the logic agent,
and
the user interface manager. Alternatively, software modules may be
incorporated into a single computing device that includes the user interface
aspect.
[0014] In another aspect of the present invention, the compliance system is
configured to analyze the compliance data and identify errors in the
preparation
of a compliance form. In one aspect, the compliance system further comprises a
schema error module having a plurality of error rules in the form of meta data
generated from schema requirements for each particular compliance form. An
error check engine is configured to check the compliance form being prepared
against the error rules to identify error in the preparation of the compliance
form.
In another aspect, the compliance system may utilize an error graph, similar
to
the calculation graphs, to identify errors. Each domain model has a respective
error graph for identifying error rules for the respective compliance form.
Each
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error graph semantically describes data dependent operations which embody
error rules for identifying errors in the preparation of a compliance form and
comprise a plurality of interconnected nodes including one or more of input
nodes, function nodes, and/or functional nodes, which are configured to be
processed by the error check engine to determine errors in the respective
compliance form. The error check engine is configured to process the error
graph to identify errors in the preparation of a respective compliance form.
[0015] In another aspect of the present invention, the compliance system is
also configured to provide explanations to a user regarding the preparation of
a
compliance form. The explanations may include explanations of a result of a
compliance form such as a result in a calculation of the compliance form,
and/or
explanations of changes in a compliance form caused by a change in an entity's
compliance data or a change in the rules or regulations for a compliance form.
On or more nodes of the calculation graph and/or the error graph are
associated
with a respective explanation of a result of each node. The compliance system
further comprises an explanation engine configured to generate a narrative
explanation utilizing the error explanation associated with a particular node.
The
explanation may then be provided the user via the user interface manager.
[0016] In yet another aspect of the present invention, the compliance
system
may also be configured to handle the payment of fees which are associated with
the submission of a compliance form. For instance, a compliance form may
require an application fee and/or payment of a fee as determined and/or
calculated by the compliance form, such as a tax, license fee, etc. The
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compliance system may further comprise a payment module configured to
process a variety of payment modes, such as automated clearing house
payments ("ACH"), electronic bank transfers, credit card payments, payments
via
online payment systems (e.g., PAYPALTM) and/or other suitable electronic
payment modes. The payment module may be integrated with the services
module, or it may be a separate module.
[0017] In still another aspect, the compliance system may also be
configured
to obtain payment from user for use of the compliance system to prepare and/or
submit a compliance form. The payment module may be configured to process a
payment for use of the compliance system same or similar to processing a
payment for fees associated with a compliance form, as described herein.
[0018] Another embodiment of the present invention is directed to computer-
implemented methods for preparing a plurality of types of compliance forms for
submission to a respective responsible agency having authority to review the
respective type of compliance form. The methods may be implemented, for
example, on the compliance system described above. In one embodiment, the
method includes a compliance form system, same or similar to that described
above, executing a compliance form preparation software application. The
payroll calculation engine reads the employer-specific tax data from the
shared
data store, performs a plurality of payroll calculation operations, and writes
calculated payroll data for a plurality of payroll data fields to the shared
data
store.
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[0019] The logic agent reads runtime data of the compliance form being
prepared and utilizes the completeness model to evaluate missing compliance
data needed to complete the compliance form. The logic agent determines one
or more suggested compliance questions for obtaining the missing compliance
data, and provides the suggested compliance questions for the user interface
manager.
[0020] The user interface manager receives the suggested compliance
questions from the tax logic agent, analyzes the suggested compliance
questions, and determines a compliance question to present to a user. The user
interface manager then presents the compliance question to the user.
[0021] In additional aspects of present invention, the computer-implemented
method may include any of the additional aspects described herein for the
system for preparing a plurality of types of compliance forms for submission
to a
respective responsible agency, such as identifying errors, providing
explanations
of results and errors, and/or processing payments for fees.
[0022] Another embodiment of the present invention is directed to an
article of
manufacture comprising a non-transitory computer readable medium embodying
instructions executable by a computer to execute a process according to any of
the method embodiments of the present invention for preparing a plurality of
types of compliance forms for submission to a respective responsible agency
having authority to review the respective type of compliance form. The
computer-readable medium may embody instructions executable by the
computing device of a compliance form system same or similar to the compliance
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system described above. For example, the process may comprise a compliance
form system executing a compliance form software program, as described
above. The calculation engine reads entity-specific compliance data from the
shared data store, calculates a calculation graph by performing calculations
and
logic operations based on the calculation graph using the entity-specific
compliance data, and writes calculated compliance data to the shared data
store.
The logic agent reads runtime data of the compliance form being prepared and
utilizes the completeness model to evaluate missing compliance data needed to
complete the compliance form. The logic agent determines one or more
suggested compliance questions for obtaining the missing compliance data, and
provides the suggested compliance questions for the user interface manager.
The user interface manager receives the suggested compliance questions from
the tax logic agent, analyzes the suggested compliance questions, and
determines a compliance question to present to a user. The user interface
manager then presents the compliance question to the user.
[0023] In additional aspects of present invention, the computer-implemented
method may include any of the additional aspects described herein for the
systems and methods for preparing a plurality of types of compliance forms for
submission to a respective responsible agency, such as identifying errors,
providing explanations of results and errors, and/or processing payments for
fees.
[0024] The compliance system of the present invention improves the
functioning of the computer by providing faster and more flexible computing
and
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generation of compliance forms. The compliance system is able to process and
compute different types of compliance forms by simply generating a domain
model for the particular compliance form, without having to re-program the
entire
compliance program. In addition, the use of calculation graphs and error
graphs
allows the compliance system to process and calculate a high volume of
compliance forms being prepared concurrently, or in short succession. The
calculation graphs and error graphs allow for more efficiently utilizing the
computing power of the system by optimizing the number of questions required
to be asked to obtain all of the required tax data for preparing a respective
compliance form and only requiring those calculations which are relevant to
each
respective compliance tax form. These features increase the flexibility of the
system, increase the speed of calculations resulting in faster calculations
and
reduced computer processing time, and require less memory resources when
preparing and calculating compliance forms. Especially when preparing and
calculating high volumes of compliance form, such as hundreds, thousands,
millions or more per time period, such as a day, week or month, the compliance
system of the present invention significantly improves the operation of the
computer, while also improving various technologies and/or technical fields,
including computerized preparation of compliance forms, computerized
calculation of compliance forms, and computerized form preparation systems.
Accordingly, the present invention is rooted in computer technology involving
specific computer components, intercommunications between computing
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modules, data structures and logic structures which improve the operation of
the
computer and also improve the technologies and technical fields recited above.
BRIEF DESCRIPTION OF THE DRAWINGS
[0025] FIG. 1 schematically illustrates how compliance form rules and
regulations are established in a respective domain model for each compliance
form, according to one embodiment.
[0026] FIG. 2 schematically illustrates how compliance form rules are
parsed
and represented by a completeness graph and a tax calculation graph, according
to one embodiment.
[0027] FIG. 3 illustrates an example of a simplified version of a
completeness
graph related to determining total taxes before adjustments on IRS Form 944,
according to one embodiment.
[0028] FIG. 4 illustrates another illustration of a completeness graph,
according to one embodiment.
[0029] FIG. 5 illustrates a decision table based on or derived from the
completeness graph of FIG. 4.
[0030] FIG. 6 illustrates another embodiment of a decision table that
incorporates statistical data.
[0031] FIG. 7 illustrates an example of a payroll calculation graph
according to
one embodiment.
[0032] FIG. 8 schematically illustrates a compliance system for calculating
a
compliance form using rules and calculations based on calculation graphs and
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identifying errors using a schema error module and/or error graphs, according
to
one embodiment.
[0033] FIG. 9 illustrates an explanation engine for generating error
explanations, according to one embodiment.
[0034] FIG. 10A illustrates an example of an error graph for identifying an
error regarding a mismatch between total tax after adjustment and total of
monthly tax liability, according to one embodiment.
[0035] FIG. 10B illustrates an example of an error graph for identifying an
error regarding a mismatch between social security/medicare exempt box
selected and social security/medicare wages reported, according to one
embodiment.
[0036] FIG. 10C illustrates an example of an error graph for identifying an
error regarding a taxable medicare wages and tips being less than sum of
taxable social security wages and tips, according to one embodiment.
[0037] FIG. 10D illustrates an example of an error graph for identifying an
error regarding entry of negative amounts for monthly tax liability, according
to
one embodiment.
[0038] FIG. 10E illustrates an example of an error graph for identifying an
error regarding a mismatch between checking a box that total tax after
adjustment is less than a threshold (e.g., $2500), but total tax after
adjustment is
greater than the threshold, according to one embodiment.
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[0039] FIG. 1OF illustrates an example of an error graph for identifying an
error regarding entry of monthly tax liability amounts when total tax after
adjustment is less than a threshold (e.g., $2500), according to one
embodiment.
[0040] FIG. 11 illustrates the implementation of a compliance system having
a
compliance form preparation software application on various computing devices.
[0041] FIG. 12 illustrates generally the components of a computing device
that may be utilized to execute the software for automatically calculating or
determining tax liability and preparing a tax return based thereon.
DETAILED DESCRIPTION OF ILLUSTRATED EMBODIMENTS
[0042] Embodiments of the present invention are directed to systems,
methods and articles of manufacture for preparing and/or submitting a
plurality of
different types of compliance forms for submission to a respective responsible
agency having the authority to review the respective type of compliance form.
As
some examples, the various different types of compliance forms may include
applications and/or forms for payroll taxes, driver's licenses, vehicle
registrations,
professional licenses, insurance licenses, building permits, healthcare
provider
licenses, marriage licenses, immigration, etc. According to the present
invention,
the compliance form system is configured to utilize a universal calculation
engine, logic agent and user interface manager which are capable of processing
each of a plurality of compliance type-specific domain models in which the
respective rules and calculations for each compliance form are established in
declarative data structures, including a calculation graph and a completeness
graph. Thus, for each type of compliance form, the compliance system has a
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different domain model such that the calculation engine, logic agent and user
interface manager utilize the respective domain model to prepare a
corresponding compliance form for which the domain model is configured. For
example, a first domain model may be configured for preparing and/or
submitting
a payroll tax form for submission to the Internal Revenue Service ("IRS"), a
second domain model may be configured to prepare and/or submit an application
for a liquor license to a state liquor licensing agency, a third domain model
may
be configured to prepare and/or submit a work visa application to the U.S.
Department State, and so on. The rules and calculations for each compliance
form are established in a respective domain model having declarative data
structures. More specifically, each domain model includes a calculation graph
and a completeness graph which embody the rules and calculations for a
respective compliance form. The compliance system is configured to access
entity-specific compliance data for preparing a selected compliance form for
an
entity from data sources and/or through the user interface manager. The
calculation engine is configured to use the entity-specific compliance to
dynamically perform a plurality of calculations and logic operations based on
the
calculation graph for the selected compliance form. The logic agent is
configured
to review current run-time data in preparing the selected compliance form
(i.e.,
the accessed entity-specific compliance data), evaluate missing data utilizing
the
completeness graph, and output one or more suggested compliance questions
for obtaining the missing compliance data to the user interface manager. The
user interface manager is configured to analyze the suggested compliance
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questions to determine a compliance question to present to a user of the
compliance system. This process is continued until the logic agent determines
that all of the required entity-specific compliance data required to complete
the
selected compliance form has been obtained. The compliance system then
prepares the compliance form and may also automatically submit the completed
compliance form to the responsible agency.
[0043] In contrast to the rigidly defined user interface screens used in
prior
software applications for preparing compliance forms, such as tax return
preparation software, the present inventions provides a compliance system 40
(see Fig. 7) having a compliance form preparation software application 100
(referred to as "compliance program 100) that runs on computing devices 102,
103 (see Figs. 13) and operates on a new construct in which compliance rules
and the calculations based thereon are established in declarative data-
structures,
namely, completeness graph(s) and tax calculation graph(s). Completeness
graphs 12 (see e.g. FIGS. 1-3) and calculation graphs 14 (see e.g., FIG. 6)
are
data structures in the form of trees having nodes and interconnections between
the nodes indicating interdependencies. Completeness graph 12 identifies each
of the conditions (e.g. questions, criteria, conditions) which may be required
to be
satisfied to complete a particular tax topic or a complete tax return, and
also
identifies when all conditions have been satisfied to complete a particular
compliance form. The tax calculation graphs 14 semantically describe data
dependent nodes, including input nodes, functional nodes, functions, and tax
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operations, that perform calculations or operations in accordance with the
rules
and regulations for a particular compliance form.
[0044] Referring first to FIG. 1, a schematic graphically illustrates how
compliance rules and regulations 10 for each compliance form are established
in
respective domain models 11. The compliance rules and regulations 10 are
promulgated by a responsible authority, such as a legislative body,
administrative
agency, and/or agency responsible for the respective compliance form. Domain
model authoring tools 13 may be utilized to generate a domain model 11
embodying the compliance rules and regulations 10. The authoring tools 13 may
include a user interface (e.g., a graphical user interface) for generating the
components of the domain models 11, which allows a user to generate the
components using widgets, icons and other graphical tools to create
declarative
data structures (as described in more detail below), functions, forms,
graphics,
narrative text, etc. Each domain model 11 may include one or more of the
following components: a completeness graph 12; a calculation graph 14 error
rules and/or error graphs; filing schema and printing templates; and/or user
interface assets. Each of these components and their use in the compliance
system 40 are described in further detail below.
[0045] FIG. 2 illustrates graphically how compliance rules and regulations
10
for a first compliance form are established in declarative data structures
comprising a completeness graph 12 and a calculation graph 14. In one aspect
of the invention, compliance rules 10 may be parsed or broken into various
topics. For example, for a payroll tax form, there are a number of payroll
topics
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that need to be covered for completing a federal payroll tax form, such as IRS
forms Form 940, form 941 and Form 944. When compliance rules 10 are broken
down into various topics or sub-topics, in one embodiment of the invention,
each
particular topic (e.g., topics A, B) may each have their own dedicated
completeness graph 12A, 12B and payroll calculation graph 14A, 14B as seen in
FIG. 1. Still, when broken down into topics and/or sub-topics, the set of
completeness graphs and the set of calculation graphs are referred to
collectively
as the completeness graph" and the calculation graph" for the particular
compliance form. The set of the completeness graph 12 and calculation
graph 14 for a particular type of compliance form make up at least a part of a
first
domain model 11 (see Fig. 7) for the first compliance form.
[0046] Additional domain models 11 for different types of compliance forms
are generated similar to the first domain model 11 utilizing compliance rules
and
regulations for each respective compliance form, such as second compliance
rules and regulations for generating a second domain model 11comprising a
second completeness graph 12 and a second calculation graph 14, third
compliance rules for generating a third domain model 11 comprising a third
completeness graph 12 and a third calculation graph 14, and so on.
[0047] Note that in FIG. 1, the completeness graph 12 and the calculation
graph 14 are interdependent as illustrated by dashed line 16. That is to say,
some elements contained within the completeness graph 12 may be needed to
perform actual compliance form calculations using the calculation graph 14.
Likewise, aspects within the calculation graph 14 may be needed as part of the
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completion graph 12. Taken collectively, the completeness graph 12 and the tax
calculation graph 14 represent data structures that capture all the conditions
necessary to complete the computations that are required to complete each
respective compliance form. The completeness graph 12, for example,
determines when all conditions have been satisfied such that a complete
compliance form can be prepared with the existing compliance data. The
completeness graph 12 is used to determine, for example, that no additional
data
input is needed to prepare and ultimately print or file the respective
compliance
form. The completeness graph 12 is used to determine when a particular
schema contains sufficient information such that a completed compliance form
can be prepared and submitted. Individual combinations of completeness
graphs 12 and payroll calculation graphs 14 that relate to one or more topics
can
be used to complete the computations required for some sub-calculations. In
the
context of a payroll tax form, for example, a sub-selection of topical
completeness graphs 12 and tax calculation graphs 14 can be used for
intermediate tax results such as total taxes before adjustments, adjustments,
total taxes after adjustments, and the like.
[0048] The
completeness graph 12 and the tax calculation graph 14 represent
data structures that can be constructed in the form of tree. FIG. 3
illustrates an
exemplary 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 total taxes before adjustments for IRS Form 944. Each
node 20 contains a tax data field or a condition that needs to be completed
with
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data or an answer in order to complete the topic. The arcs 22 that connect
each
node 20 may illustrate the dependencies between nodes 20, or simply a flow of
data requirements. 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. 3, there are several pathways to completion.
[0049] FIG. 4 illustrates another example of a completeness graph 12 that
includes a beginning node 20a (Node A), intermediate nodes 20b-g (Nodes B-G)
and a termination node 20y (Node "Yes" or "Done"). Each of the beginning
node 20a and intermediate nodes 20a-g represents a question for obtaining
entity-specific compliance data. 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 (YIN), 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.
[0050] As explained herein, the directed graph or completion graph 12 that
is
illustrated in FIG. 4 can be traversed through all possible paths from the
start
node 20a to the termination node 20y. By navigating various paths through the
completion graph 12 in a recursive manner one can determine each path from
the beginning node 20a to the termination node 20y. The completion graph 12
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along with the pathways to completion through the graph can be converted into
a
different data structure or format. In the illustrated embodiment shown in
FIG. 4,
this different data structure or format is in the form of a decision table 30.
In the
illustrated example, the decision table 30 includes rows 32 (five rows 32a-e
are
illustrated) based on the paths through the completion graph 12. In the
illustrated
embodiment, the columns 34a-g of the completion graph represent expressions
for each of the questions (represented as nodes A-G in FIG. 3) and answers
derived from completion paths through the completion graph 12 and column 34h
indicates a conclusion, determination, result or goal 34h concerning a
compliance topic or situation, e.g., "Yes ¨ wages tips and other compensation
subject to Social Security or Medicare must be entered" or No ¨ no entries are
required for wages tips and other compensation subject to social security or
Medicare."
[0051] Referring to FIG. 5, each row 32 of the decision table 30 represents
a
compliance rule. The decision table 30, for example, may be associated with a
federal payroll tax rule or a state payroll tax rule. In some instances, for
example, a state tax rule may include the same decision table 30 as the
federal
tax rule. The decision table 30 can be used, as explained herein, to drive a
personalized interview process for the user of compliance form preparation
software 100, or to simply access the needed payroll tax data and answers from
a data source, such as a financial accounting software application or
database.
In particular, the decision table 30 is used to select a question or questions
to
present to a user during an interview process, or to access a particular data
field
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from a database. In this particular example, in the context of the completion
graph 12 from FIG. 4 converted into the decision table 30 of FIG. 5, if the
first
question presented to the user during an interview process is question "A" and
the user answers "Yes" rows 32c-e may be eliminated from consideration given
that no pathway to completion is possible. The payroll tax rule associated
with
these columns cannot be satisfied given the input of "Yes" in question "A."
Note
that those cell entries denoted by "?" represent those answers to a particular
question in a node that is irrelevant to the particular pathway to completion.
Thus, for example, referring to row 34a, when an answer to QA is "Y" and a
path
is completed through the completion graph 12 by answering Question C as "N"
then answers to the other questions in Nodes B and D-F are "?" since they are
not needed to be answered given that particular path.
[0052] After an initial question has been presented and rows are eliminated
as
a result of the selection, next, a collection of candidate questions from the
remaining available rows 32a and 32b is determined. From this universe of
candidate questions from the remaining rows, a candidate question is selected.
In this case, the candidate questions are questions Qc and QG in columns 34c,
34g, respectively. One of these questions is selected and the process repeats
until either the goal 34h is reached or there is an empty candidate list.
[0053] FIG. 6 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 R1-R6). For example, the statistical
data 36
may represent a percentage or the like in which a particular demographic or
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category of user(s) satisfies this particular path to completion. The
statistical
data 36 may be mined from existing or current year tax filings. The
statistical
data 36 may be obtained from a proprietary source of data such as tax filing
data
owned by Intuit, Inc. The statistical data 36 may be third party data that can
be
purchased or leased for use. For example, the statistical data 36 may be
obtained from agency responsible for a particular compliance form or the like
(e.g., IRS, state or local licensing agency, department of motor vehicles,
etc.). In
one aspect, the statistical data 36 does not necessarily relate specifically
to the
individual or individuals preparing the particular tax return. For example,
the
statistical data 36 may be obtained based on a number of tax filers which is
then
classified one or more classifications. For example, statistical data 36 can
be
organized with respect to age, entity status (e.g., joint, separate, married
filing
separately, corporation, individual, partnership, LLC, LLP, etc.), an entity's
income range (gross income, adjusted gross income ("AGI"), etc.), an entity's
geographic location, an entity's number of employees, and the like).
[0054] FIG. 6 illustrates two such columns 38a, 38b in the decision table
30
that contain statistical data 36 in the form of percentages. For example,
column
38a (STAT1) may contain a percentage value that indicates employers having
under a certain number of employees where Rulei is satisfied. Column 38b
(STAT2) may contain a percentage value that indicates employers having over a
certain number of employees where Rulei 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 the number of employees. The statistical data 36 may be
used,
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as explained in more detail below, by the payroll tax form preparation
software
100 to determine which of the candidate questions (QA-QG) should be asked for
a
particular employer. The statistical data 36 may be compared to one or more
known employer data fields (e.g., number of employees, filing status,
geographic
location, or the like) such that the question that is presented to the user is
most
likely to lead to a path to completion. Candidate questions may also be
excluded
or grouped together and then presented to the user to efficiently minimize
compliance form 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 questions do not apply to you, please confirm that
this
is correct." This enables the elimination of many pathways to completion that
can optimize additional data requests of the taxpayer.
[0055] The completeness graph(s) 14 and/or decision table(s) 30 for a
particular type of compliance form compose the completeness model for the
respective type of compliance form.
[0056] FIG. 7 illustrates one example of a payroll tax calculation graph 14
for
a compliance form comprising a payroll tax form. The payroll calculation
graph 14 semantically describes data dependent payroll tax operations that are
used perform payroll calculation operations in accordance with the payroll tax
rules 10. The payroll tax calculation graph 14 in FIG. 7 is a view of data
dependent payroll tax operations that are used to determine the total taxes
before adjustments, which is line 5 for IRS Form 944 for 2015. The payroll tax
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calculation graph 14 is a type of directed graph and, in most situations
relevant to
payroll calculations, is a directed acyclic graph that encodes the data
dependencies amongst payroll concepts or topics.
[0057] In FIG. 7, various nodes 24 are leaf or input nodes. Examples of
leaf
nodes 24 in this particular example include data obtained from payroll data,
such
as from a financial accounting software application, like QUICKBOOKS, or other
database of payroll data. Typically, though not exclusively, leaf nodes 24 are
populated with data accessed from a payroll program or from user inputs. For
user inputs, the user may enter the data via a user interface as described
herein.
In other embodiments, however, the leaf nodes 24 may be populated with
information that is automatically obtained by the compliance form preparation
software 100. For example, in some embodiments, compliance data documents
may be imaged or scanned with relevant data being automatically extracted
using Object Character Recognition (OCR) techniques. In other embodiments,
prior compliance forms may be used by the compliance system 40 to extract
information (e.g., employer name, address, EIN, etc.) which can then be used
to
populate the leaf nodes 24. Online resources such as financial services
websites
or other webs ites can be crawled and scanned to scrape or otherwise download
compliance form data that can be automatically populated into leaf nodes 24.
In
still other embodiments, values for leaf nodes 24 may be derived or otherwise
calculated.
[0058] Still other internal nodes referred to as functional nodes 26
semantically represent a compliance form concept, such as a compliance form
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line item, such as a form field, and may be calculated or otherwise determined
using a function node 28 (also referred to as a "function 28"). The functional
node 26 and the associated function 28 define a particular tax operation 29.
For
example, as seen in FIG. 7, operation 29 refers to tax due for social security
wages and is the result of the multiplication function 28 which multiplies the
social security wages (X1) from leaf node 24 times a tax rate constant (K1).
The
functional node 26 may include a number in some instances. In other instances,
the functional node 26 may include a response to a Boolean expression such as
"true" or "false." The functional nodes 26 may also be constant values in some
instances. Some or all of these functional nodes 26 may be labeled as
"compliance concepts" or "compliance topics." The combination of a functional
node 26 and its associated function 28 relate to a specific compliance form
operation (in this example, a payroll tax operation) as part of the compliance
topic (in this example, a payroll tax topic).
[0059] Interconnected function nodes 26 containing data dependent
compliance concepts or topics are associated with a discrete set of functions
28
that are used to capture domain specific patterns and semantic abstractions
used
in the payroll tax calculation. The discrete set of functions 28 that are
associated
with any particular function node 26 are commonly re-occurring operations for
functions that are used throughout the process of calculating a particular
compliance form. For example, examples of such commonly re-occurring
functions 28 include copy, capping, thresholding (e.g., above or below a fixed
amount), accumulation or adding, look-up operations (e.g., look-up tax
tables),
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percentage of calculation, phase out calculations, comparison calculations,
exemptions, exclusions, and the like.
[0060] In some embodiments, the function 28 may also include any number of
mathematical or other operations. Examples of functions 28 include summation,
subtraction, multiplication, division, and comparisons, greater of, lesser of,
at
least one of, calling of look-ups of tables or values from a database or
library. It
should be understood that the function nodes 26 in the calculation graph 14
may
be shared in some instances.
[0061] FIG. 8 schematically illustrates a compliance system 40 for
preparing
and/or submitting a plurality of types of compliance forms using rules and
calculations based on a declarative data structures, according to one
embodiment. The system 40 includes a shared data store 42 that contains
therein, for each of the types of compliance forms, a schema 44 or canonical
model representative of compliance data fields (typically, fields for the
input data
values for preparing a compliance form) and the calculated compliance data
fields (the fields for the compliance data calculated using the compliance
data)
utilized or otherwise required to complete each type of compliance form. The
shared data store 42 may be a repository, file, or database that is used to
contain
the compliance data fields. The shared data store 42 is accessible by a
computing device 102, 103 as described herein (e.g., FIG. 12). The shared data
store 42 may be located on the computing device 102, 103 running the
compliance program 100 or it may be located remotely, for example, in a cloud
environment on another, remotely located computer. The schema 44 may
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include, for example, a schema based on the requirements of the responsible
agency, such as the Modernized e-File (MeF) system developed by the Internal
Revenue Service. MeF uses extensible markup language (XML) format that is
used when identifying, storing, and transmitting data. For example, each line
or
data element on a payroll tax form is given an XML name tag as well as every
instance of supporting data. The compliance program 100 may use XML
schemas and business rules to electronically prepare and transmit a completed
compliance form to the responsible agency. The responsible agency may then
validate the transmitted file for the compliance form against the schemas and
business rules in the schema 44.
[0062] As seen in FIG. 8, 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 compliance form related data to the shared data store 42.
This may occur through a user interface manager 80 as described herein or,
alternatively, data importation may occur directly to the shared data store 42
(not
illustrated in FIG. 8). The compliance form related data may include employer
identification data such as a name, address, and taxpayer ID (EIN).
[0063] For some compliance forms, such as tax related compliance forms like
payroll tax forms, compliance data (e.g., employer payroll tax data), may be
accessed from a financial accounting application 48e The financial accounting
system 48e may be any suitable financial accounting application, such as
QUICKBOOKS, available from Intuit Inc. of Mountain View, California. The
compliance data may be electronically transferred to the compliance system 40
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via the user interface manager 80, or directly to the shared data store 42, as
described above.
[0064] User
input 48a is also one type of data source 48. User input 48a may
take a number of different forms. For example, user input 48a may be generated
by a user using, for example, an input device such as a keyboard, mouse,
touchscreen display, voice input (e.g., voice to text feature), photograph or
image, or the like to enter information manually into the payroll tax form
preparation software 100. For example, as illustrated in FIG. 8, user
interface
manager 82 contains an import module 89 that may be used to select what data
sources 48 are automatically searched for payroll tax related data. Import
module 89 may be used as a permission manager that includes, for example,
user account numbers and related passwords. The Ul control 80 enables what
sources 48 of data are searched or otherwise analyzed for compliance form
related data. For example, a user may select prior compliance form 48b to be
searched but not online resources 48c. The compliance data may flow through
the U I control 80 directly as illustrated in FIG. 8 or, alternatively, the
compliance
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
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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.
[0065] User input 48a may also include some form of automatic data
gathering. For example, a user may scan or take a photographic image of a
document (e.g., a prior IRS Form 944, W-2, driver's license, vehicle
registration,
identification, business license, etc.) that is then processed by the
compliance
program 100 to extract relevant data fields that are then automatically
transferred
and stored within the data store 42. OCR techniques along with pre-stored
templates of common compliance form related documents 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.
[0066] Another example of a data source 48 is a prior compliance form 48b.
In other words, a prior compliance form 48b is a compliance form of the type
currently being prepared by the compliance system 40 that was previously
prepared by the same entity or even a different entity. A prior compliance
form
48b that is stored electronically can be searched and data is copied and
transferred to the shared data store 42. The prior compliance form 48b may be
in a proprietary format (e.g., .txf, .pdf) or an open source format. The prior
compliance form 48b may also be in a paper or hardcopy format that can be
scanned or imaged whereby data is extracted and transferred to the shared data
store 42. In another embodiment, a prior compliance form 48b may be obtained
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by accessing a responsible agency database (e.g., IRS records, DMV records,
licensing agency records, etc.).
[0067] An additional example of a data source 48 is an online resource 48c.
An online resource 48c may include, for example, websites for the entity for
which a compliance form is being prepared, or websites known to have entity
specific compliance data related to particular types of compliance forms. 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.
[0068] Still referring to FIG. 8, another data source 48 includes sources
of
third party information 48d that may be accessed and retrieved. For example,
other responsible agencies may have compliance data useful in preparing one or
more of the compliance forms.
[0069] Referring briefly to FIG. 12, the compliance program 100 including
the
system 40 of FIG. 8 is executed by the computing device 102, 103. Referring
back to FIG. 8, the payroll tax form preparation software 100 includes a
calculation engine 50 that performs one or more payroll calculations or
operations based on the available compliance data at any given instance within
the schema 44 in the shared data store 42. For example, for a payroll tax
form,
the calculation engine 50 may calculate the total balance due from the
employer,
the total taxes before adjustments, the current year's adjustments, the total
deposits for the year, overpayment amount, or one or more intermediary
calculations. The calculation engine 50 utilizes the one or more calculation
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graphs 14 as described previously in the context of FIGS. 2 and 7. The tax
calculation engine 50 reads the most current or up to date entity-specific
compliance data contained within the shared data store 42 for the compliance
form currently being processed and then performs compliance form calculations
based on the respective calculation graph 14 of the domain model 11 for the
selected compliance form. Updated calculation values are then written back to
the shared data store 42. As the updated calculation values are written back,
new instances 46 of the compliance form schema 46 are created.
[0070] Still referring to FIG. 8, the system 40 may also include a logic
agent
(LA) 60. The LA 60 operates in conjunction with the shared data store 42
whereby updated compliance data represented by instances 46 are read to the
LA 60. The LA 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 compliance schema 44 at runtime. The LA 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 LA 60 may be implemented as a dedicated module contained
within the payroll tax form preparation software 100.
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[0071] As seen in FIG. 8, The LA 60 uses the decision tables 30 (which are
part of the compliance model) to analyze the run time data 62 and determine
whether a compliance form is complete. Each decision table 30 created for each
topic or sub-topic is scanned or otherwise analyzed to determine completeness
for each particular topic or sub-topic. In the event that completeness has
been
determined with respect to each decision table 30, then the rule engine 64
outputs a "done" instruction 68 to the Ul control 80. If the rule engine 64
does
not output a "done" instruction 68 that means there are one or more topics or
sub-topics that are not complete, which, as explained in more detail below
presents interview questions to a user for answer. The LA 60 identifies a
decision table 30 corresponding to one of the non-complete topics or sub-
topics
and, using the rule engine 64, identifies one or more non-binding suggestions
66
to present to the Ul control 80. The non-binding suggestions 66 may include a
listing of compilation of one or more questions (e.g., Q1-Q5 as seen in FIG.
8)
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., Ri-R5) 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. 6 may be used to augment or aid this
ranking
process. Questions may also be presented that are most likely to increase the
confidence level of the calculated tax liability or refund amount. In this
regard, for
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example, those questions that resolve data fields associated with low
confidence
values may, in some embodiments, be ranked higher.
[0072] The following pseudo code generally expresses how a rule engine 64
functions utilizing a fact cache based on the runtime schema data 62 or the
instantiated representation of the schema data 46 at runtime and generating
non-
binding suggestions 66 provided as an input to Ul control 80.:
Rule engine (64)/ Logic Agent (LA) (60)
// initialization process
Load_Compliance_Knowledge_Base;
Create_Fact_Cache; While (new_data_from_application)
Insert_data_into_fact_cache;
collection = Execute_Compliance_Rules; // collection is all the fired
rules and corresponding conditions
suggestions = Generate_suggestions (collection);
send_to_application(suggestions);
[0073] Still referring to FIG. 8, the Ul 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 and
may
manifest itself, typically, on a visual screen or display 104 that is
presented on a
computing device 102, 103 (seen, for example, in FIG. 12). The Ul controller
80
utilizes the user interface assets 19 for the particular domain model 11 being
processed at the time. The user interface assets 19 for each domain model 11
may include one or more of the following components which are configured
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specifically for the compliance form of the respective domain model 11:
configuration files; a user interface presentation 84 (which may include
interview
screens and/or data input forms, as described below); and/or a suggestion
resolution element, as described in more detail below. 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 Ul generator 85 depending, for example, on the type of
display 104 that is utilized by the computing device. For example, an
interview
screen with many questions or a significant amount of text may be appropriate
for a computer, laptop, or tablet screen but such as presentation may be
inappropriate for a mobile computing device such as a mobile phone or
Smartphone. In this regard, different interface presentations 84 may be
prepared
for different types of computing devices 102. The nature of the interface
presentation 84 may not only be tied to a particular computing device 102 but
different users may be given different interface presentations 84.
[0074] The
user interface manager 82, as explained previously, receives non-
binding suggestions from the LA 60. The non-binding suggestions may include a
single question or multiple questions that are suggested to be displayed to
the
taxpayer via the user interface presentation 84. The user interface manager
82,
in one aspect of the invention, contains a suggestion resolution element 88,
is
responsible for resolving of how to respond to the incoming non-binding
suggestions 66. For this purpose, the suggestion resolution element 88 may be
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programmed or configured internally. Alternatively, the suggestion resolution
element 88 may access external interaction configuration files.
[0075] The 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.
[0076] A user interface presentation 84 may be include 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.
[0077] Alternatively, the user interface presentation 84 may comprise a
"forms
mode" which presents fillable form fields for the user to enter the entity-
specific
compliance data required for preparing the compliance form. The forms mode
may present the fillable form fields within a representation of the compliance
form
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being prepared, or in any other suitable presentation. The user interface
manager 82 may highlight or otherwise emphasize the fillable form fields based
on the suggestions 66 from the LA 60, such as by numbering the fillable form
fields based upon the order or sequence of the suggestions 66 from the LA 60.
[0078] As seen in FIG. 8, the Ul controller 80 interfaces with the shared
data
store 42 such that compliance 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 compliance program 100 using an input
device
that is associated with the computing device 102, 103. For example, a user may
use a mouse, finger tap, keyboard, stylus, voice entry, or the like to respond
to
questions. The user 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 requested of the user.
[0079] Still referring to FIG. 8, in one aspect, the LA 60 may output a
current
compliance form result 65 which can be reflected on a display 104 of a
computing device 102, 103. For example, the current compliance form result 65
may illustrate a payment due or other actions that must be taken by the user.
The current compliance form results 65 may also illustrate various other
intermediate calculations or operations used to calculate the compliance form.
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[0080] The LA 60 may also output completed compliance form data that is
used to generate the actual completed compliance form (either electronic
compliance form or printed paper compliance form). The compliance form itself
can be prepared by the LA 60 or at the direction of the LA 60 using, for
example,
the services engine 90 that is configured to perform a number of tasks or
services for the system provider. The LA 60 or the services engine 90 utilizes
the filing schema and/or printing templates 17 for the particular domain model
11
being processed at the time. The filing schema and/or printing templates 17
for
each domain model 11 are specifically configured for the respective compliance
form for each domain model 11.
[0081] For example, the services engine 90 can include a printing option
92.
The printing option 92 may be used to print a copy of a compliance form,
compliance data and calculated compliance data, summaries of such data, error
reports 155 (as described below), and the like. The services engine 90 may
also
electronically file 94 or e-file a compliance form with the responsible
agency.
Whether a paper or electronic compliance form is filed, data from the shared
data
store 42 required for particular compliance forms, is transferred over into
the
desired format. With respect to electronically filed compliance forms, the
compliance form may be filed using the particular schema required by the
responsible agency, such as the MeF web-based system of the IRS that allows
electronic filing of payroll tax forms via the Internet. Of course, other e-
filing
systems may also be used other than those that rely on the MeF standard.
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[0082] The compliance system 40 also has a payment module 98 which may
be integrated with the services engine 90, as shown in FIG. 7, or
alternatively, it
may be a separate module of the compliance system 50. The payment
module 90 is configured to process the payment of fees which are associated
with the submission of a compliance form. For instance, a compliance form may
require an application fee and/or payment of a fee as determined and/or
calculated in the preparation of the compliance form, such as a tax, license
fee,
etc. The payment module 90 is configured to process a variety of payment
modes, such as automated clearing house payments ("ACH"), electronic bank
transfers, credit card payments, payments via online payment systems (e.g.,
PAYPALTM) and/or other suitable electronic payment modes. The payment
module 90 may open a frame within a web browser or user interface screen and
connect to a payment website, such as a credit card payment website, or
payment website, or the payment module 90 may be configured to display a
payment screen in which the user can select payment types and then enter
payment details, such as payee name, payment credit card or bank account
number, payee address, user name and password for a payment system, etc.
[0083] The payment module 98 is also be configured to obtain payment from
user for use of the compliance system 40 to prepare and/or submit a compliance
form. The payment module 98 may obtain payment using any of the means
described above for processing a payment for fees associated with a compliance
form.
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[0084] Referring again to FIG. 8, the system 40 includes an explanation
engine 110 that operates within the compliance program 100 to generate a
narrative explanation from one or more explanations associated with a
particular
tax operation 29 (illustrated in FIGS 6A and 6B). Each of the nodes 26, 28 and
29 of the calculation graph 14 are associated with an explanation related to
the
particular node, such as an explanation of the result of the node. For
example, to
generate the narrative explanation for a particular tax operation 29, the
explanation engine 110 extracts the stored function 28 that is associated with
the
particular functional node 26. The stored function 28 is one function of a
defined
set and may be associated with a brief explanation. For example, a "cap"
function 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 26 within the calculation graph
14.
For example, the functional node 26 paired with the stored "cap" function 28
gives a contextual tax explanation that is more than merely "value exceeds
cap."
For instance, a pre-stored narrative associated with the particular functional
node
26 having to do with a child tax credit within a calculation graph 14 may be a
complete statement or sentence such as You cannot claim a child tax credit
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 "credit subject to income phase out"
or
"AGI too high." A particular functional node 26 and associated function 28 may
have multiple pre-stored narratives. The particular narrative(s) that is/are
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associated with a particular functional node 26 and associated function 28 may
be stored in entries 112 in a data store or database such as data store 42 of
FIG. 8. For example, with reference to FIG. 8, data store 42 contains the pre-
stored narratives that may be mapped or otherwise tagged to particular
functional
nodes 26 and associated functions 28 contained within a particular calculation
graph 14. The locations or addresses of the various functional nodes 26 and
the
associated functions 28 can be obtained using the calculation graphs 14.
[0085] The pre-stored narratives for each type of compliance form may be
embodied in a respective narratives module for each type of compliance form.
Each narrative module may then be a component of the domain model 11 for the
respective type of compliance form. Thus, the explanation engine 110 utilizes
the narratives module for the particular domain model 11 being processed at
the
time. These stored entries 112 can be recalled or extracted by the explanation
engine 110 and then displayed to a user on a display 104 of a computing device
102, 103. For example, explanation engine 110 may interface with the Ul
control
80 in two-way communication such that a user may ask the compliance
program 100 why a particular compliance calculation, operation, or decision
has
been made by the compliance 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 compliance calculation,
operation, or
decision was made by the compliance program 100. For example, in the context
the Affordable Care Act ("ACA") penalty calculation of an income tax return, a
user may see an ACA penalty of $1,210.00 listed on the screen of the computing
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device 102, 103 while he or she is preparing the tax return for a prior year.
The
user may be interested in why there is such a penalty. As one example, the
initial explanation provided to the user may be you have an ACA penalty
because you, your spouse, and your two child dependents did not have coverage
during the 2014 calendar year and the penalty is based on your income." This
explanation may be associated with, for example, a function node 26 and
functional node 28.
[0086] 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 traversing the calculation graph14 to identify
each of
the preceding or upstream input node(s) 24, function node(s) 26 and/or
function
node(s) 28. In the context of the ACA example discussed above, a user may not
be satisfied with the initial explanation described above, 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 the ACA penalty. In this
example,
the user may be provided with "Under the ACA your penalty is the greater of
1`)/0
of your taxable income or a fixed dollar amount based on your family
circumstances. In your situation, the 1`)/0 of taxable income exceeded the
fixed
dollar amount." This particular explanation may be associated with the
predecessor function node 26 and function 28.
[0087] With reference to FIG. 8, the explanation engine 110 may also
automatically generate explanations that are then communicated to the user
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interface manager 82. The automatically generated explanations may be
displayed on a display associated with the computing devices 102, 103. In some
embodiments, the explanations may be contemporaneously displayed alongside
other tax data and/or calculations. For example, as a user inputs his or her
information into the compliance program 100 and calculations are automatically
updated, explanations may be automatically displayed to the user. These
explanations may be 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.
[0088] In one aspect of the invention, the choice of what particular
explanation
will be displayed to a user may vary. For example, different explanations
associated with the same function node 26 and function 28 may be selected by
the explanation engine 110 for display to a user based on the user's
experience
level. A basic user may be given a general or summary explanation while a user
with more sophistication may be given a more detailed explanation. A
professional user such as a CPA or other tax specialist may be given even more
detailed explanations.
[0089] 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 compliance program 100 may
provide few or no explanations. In a more advanced edition (e.g., "Deluxe
edition"), additional explanation is provided. Still more explanation may be
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provided in the more advanced editions of the compliance program 100 (e.g.,
"Premier edition"). Versions of the compliance program 100 that are developed
for accountants and CPAs may provide even more explanation.
[0090] In still other embodiments a user may be able to "unlock" additional
or
more detailed explanations by upgrading to a higher edition of compliance
program 100. Alternatively, a user may unlock additional or more detailed
explanations in an a la carte manner for payment of an additional fee. Such a
fee can be paid through the compliance program 100 itself using known methods
of payment.
[0091] The explanation engine 110 may also include a natural language
generator 114 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 26 and associated function 28. For instance, the explanation
engine 110 may extract a brief explanation which indicates that the child
credit
tax is zero due to phase out from income level and then use the national
language generator 114 to convert the brief explanation into a more
understandable sentence that can be presented to the user. In one aspect of
the
invention, the natural language generator 114 may rely on artificial
intelligence or
machine learning such that results may be improved.
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[0092] Still referring to FIG. 8, the payroll system 40 may also include an
error
check engine 150 and a schema error module 152 for identifying errors in the
preparation of a compliance form using the compliance system 40. The error
check engine 150 utilizes the error rules 15 for the particular domain model
11
being processed at the time. The error rules 15 for each domain model 11 may
include one or more of the following components which are specifically
configured for the compliance form of the respective domain model 11: a schema
error module; and/or error graphs, as described in more detail below.
[0093] The schema error module 152 includes a plurality of error rules
wherein each error rule is associated with a particular compliance form field
or a
compliance form data field. Each error rule comprises meta data which is
configured to be usable by the error check engine 150 to check a data value
for a
respective data field and determine whether it conforms to the schema
requirements for the particular compliance form being prepared as set forth in
the
compliance rules and regulations 10, which includes schema requirements. The
error check engine 150 is configured to read/access the entity-specific
compliance data from the shared data store 44 and check such data against the
error rules for the respective data fields to identify one or more errors in
the
preparation of the compliance form. For instance, the error rules may include
meta data configured to check for errors in the formatting of the compliance
data
in respective compliance data fields. As several examples, an error rule may
check that the value for an EIN is only numbers and 9 digits; an error rule
may
check that the wages, tips and other compensation is only a positive number,
an
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error rule may check that the ZIP code includes only 5 numbers or 9 numbers,
an
error rule may check that the state includes a valid two letter state code,
etc. If
the error check engine 150 determines that a data value does not conform to
the
requirements of the error rule, then the error check engine 150 flags the
error
and creates an error record which identifies the error.
[0094] In
order to provide a more human understandable explanation of errors
according to the error rules, each of the error rules may also have a schema
error explanation associated with the error rule. The schema error explanation
may include a narrative explanation, fragments, expressions, and/or partial
statements. The error check engine 150 is further configured to utilize the
schema error explanation to generate a narrative explanation of errors
identified
according to a particular error rule. For instance, a schema error explanation
associated with an error rule for checking the format of an EIN may be a
complete sentence such as The EIN must include only numbers and 9 digits."
The schema error explanation may be a template having fillable fields and the
error check engine 150 may be configured to provide the explanation as well as
providing a description of the specific erroneous input, such as The EIN must
include only numbers and 9 digits, and the value provided is --
wherein the error check engine 150 is configured to fill in the blanks with
actual
value input to the payroll system 40. The error explanation may also include a
recommendation or requirement for correcting the error. In the EIN example,
the
recommendation may state something like, You must enter 9 numbers, and no
other characters."
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[0095] The errors identified by the error check engine 150 and the
explanations generated by the error check engine 150 may be compiled into a
report 155 for use by a user, such as an agent of a service provider utilizing
the
compliance system 40 to prepare compliance forms as a service. The report 155
may be as simple as a log file, or it may be an email, or an electronic
document
like a pdf or Microsoft Word file. The report 155 could also be a web page
configured for display on a web browser and made accessible via the internet.
The error check engine 150 may also transmit the errors to the Ul controller
80
which can then display the errors to a user, and/or utilize the errors in the
process of data entry via the Ul manager 82.
[0096] The error check engine 150 can identify errors on a field level or
entry
level as the data is being accessed and/or input into the compliance system
40.
Thus, it does not have to be executed on an entire compliance form. Moreover,
the error check engine 150 can check for errors using the error rules in the
schema error module 152 as the data is being input, such as being typed in by
a
user. In such case, the error check engine 150 and/or Ul manager 82 can be
configured to block entry of invalid data or display an error message when a
user
attempts to enter data which does not conform to the applicable error rule.
[0097] Still referring to FIG. 8, instead of the error check engine 150
generating explanations, the compliance system 40 may have a separate
explanation engine 154 which is configured to receive the errors identified by
the
error check engine 150 and then generate error explanations and/or an error
report 155, same or similar to those described above. The explanation engine
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154 can also transmit the error explanations to the Ul controller 80 which can
then display the explanations to a user, and/or utilize the errors in the
process of
data entry via the Ul manager 82. The explanation engine 154 may be
configured to utilize the narrative explanation, fragments, expressions,
and/or
partial statements of the error explanations to generate natural language
expressions that are more easily understood by a user. 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 26 and associated
function 28.
[0098] FIG. 9 illustrates additional details of the explanation engine 154,
according to one embodiment of the invention. In the example of FIG. 9, a
brief
explanation 115A extracted by the explanation engine 110 indicates that the
total
tax after adjustments does not equal the total monthly tax liabilities. In
this
example, the user is also provided with a natural language explanation 115B
that
is more readily understood by users which is generated by a natural language
generator 114. The natural language generator 114 may utilize artificial
intelligence or machine learning such that results may be improved.
[0099] The explanation engine 154 may also be configured to generate
additional, more detailed narrative explanations in response to user prompts.
For
instance, each of the error rules may be associated with a respective error
explanation, or plurality of error explanations such that a single error rule
has
multiple error explanations, such as a general explanation and additional more
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detailed explanations. The explanation engine 154 may display the general
explanation along with user prompts (e.g., selection buttons, hyperlinks, etc.
may
be used to allow the user to select them) which the user can select in order
to
view additional more detailed explanations. This allows a user to drill down
on
an error to view more detailed explanations.
[00100] In another optional feature for identifying errors and generating
error
explanations, the payroll system 40 may be configured to utilize the
declarative-
data structure construct in the form of error graphs 156 to identify more
complex
errors than the schema errors checked using the schema error module 152. For
instance, error graphs 156 may be utilized by the error check engine 150 to
identify errors involving multiple data fields, and multiple logic expressions
and
functions. Similar to the calculation graphs 14 discussed above, the error
graphs
156 comprise a plurality of interconnected nodes, including leaf or input
nodes
24, functional nodes 26 and/or functions 28.
[00101] FIGS. 10A-10F illustrate a number of examples of error graphs 156 for
identifying errors in the preparation of a compliance form. Similar to the
calculation graphs 14 described above, the error graphs 156 include leaf or
input
nodes 24 the values of which are accessed from the shared data store, such as
compliance data values and calculated payroll data values. The error graphs
156 also include functional nodes 26 which represent a compliance form
concept, or result from a function 28, such as a mathematical function or a
logical
expression. The functional node 26 may include a number or value in some
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instances, or a response to a logical function such as a Boolean expression
like
"true" or "false", in other instances.
[00102] For instance, FIG. 10A is an example of an error graph 156 for
identifying an error regarding the total tax after adjustment not being equal
to the
total of the monthly tax liability in preparing IRS Form 944 for 2015. The
error
graph 156 includes input nodes 24, including certain constants consisting of
thresholds, and calculated payroll data, like the total tax after adjustments
and
the total of the monthly tax liabilities. The error graph 156 also includes
function
nodes 28 having Boolean logical operators for comparing certain values, and
functional nodes 26 representing the results of the logical operators. The
"DONE" result for a functional node 26 indicates that there is no error for
the
based on that particular calculation path of the error graph 156.
[00103] The error graphs 156 in FIGS. 10B-10E each include input nodes 24,
function nodes 28 and functional nodes 26, similar to the error graph 156 in
FIG 10A, for identifying other various errors in the preparation of a payroll
tax
form. FIG. 10B illustrates an error graph 156 for identifying an error caused
by a
mismatch between a selection of the social security/medicare exempt box
selected and the reporting of actual social security/medicare wages in
preparing
IRS Form 944 for 2015. FIG. 10C illustrates an error graph 156 for identifying
an
error regarding the reported taxable medicare wages and tips being less than
the
sum of taxable social security wages and tips in preparing IRS Form 944 for
2015. FIG. 10D illustrates an example of an error graph 156 for identifying an
error caused by entry of negative amounts for monthly tax liability in
preparing
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IRS Form 944 for 2015. FIG. 10E illustrates an example of an error graph 156
for identifying an error caused by checking a box that total tax after
adjustment is
less than a threshold (e.g., $2500), but the calculated total tax after
adjustment is
greater than the threshold in preparing IRS Form 944 for 2015.
[00104] The error check engine 150 is configured to process each of the error
graphs 156 to identify whether there is an error in preparing the compliance
form
for which the respective error graph 156 is configured. The error check engine
150 simply traverses the nodes of the error graph 156, and accesses data for
input nodes 24, performs functions for function nodes 28 and fills in the
result of
the functional nodes 26, as needed by the particular error graph 156.
[00105] Similar to the error explanations associated with error rules
described
above, the nodes of the error graphs 156 may be associated with a node error
explanation which can be used to generate an narrative explanation of an error
associated with a particular node or calculation path including such node. The
node error explanation may include a narrative explanation, fragments,
expressions, and/or partial statements. The error check engine 150 and/or
explanation engine 154 are configured to utilize the node error explanations
to
generate a narrative explanation of errors identified according to a
particular error
graph 156, in the same or similar manner as that described above for error
rules.
For instance, a node error explanation associated with a node on error graph
156
of FIG. 9A may be a complete sentence such as The total taxes after adjustment
does not equal the total of the monthly liabilities. You must make the
necessary
adjustments to reconcile the amounts." The node error explanation may be a
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template having fillable fields and the error check engine 150 and/or
explanation
engine may be configured to provide the explanation as well as providing a
description of the specific erroneous input, such as The total tax after
adjustment is $ _____ , which is not equal to the total of monthly liabilities
which is $ ______ ." As shown in the example above, the error explanation
may also include a recommendation or requirement for correcting the error.
[00106] The errors identified by the error check engine 150 using the error
graphs 156 and the explanations generated by the error check engine 150 and/or
explanation engine 154 may be compiled into a report 155 for use by a user,
the
same as the errors and explanations regarding the error rules, as described
above. Similarly, the explanation engine can transmit the error explanations
to
the U I controller 80 which can then display the explanations to a user,
and/or
utilize the errors in the process of data entry via the Ul manager 82. The
explanation engine 154 may be configured to utilize the narrative explanation,
fragments, expressions, and/or partial statements of the error explanations
associated with nodes of the error graphs 156 to generate natural language
expressions that are more easily understood by a user, same or similar to the
error explanations associated with the error rules.
[00107] In addition, the compliance system 40 can be configured to include
both of the error checking systems, namely, the error checking utilizing the
schema error module 152 and the error checking utilizing the error graphs 156.
The errors and error explanations from both error checking systems can be
compiled together into a report 155, and/or reported collectively to a user
via the
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U I manager 82. Alternatively, the payroll system 40 can be configured to
include
only one of the error checking systems, either the schema error module 152
based system or the error graph 156 based system.
[00108] The algorithms for the operation of the compliance system 40 is
described above, but a summary of the algorithms for the overall operation
will
now be described with reference to an exemplary compliance system 40
implemented on various computing devices, as shown in FIG. 12. A user
initiates the compliance program 100 on a computing device 102, 103 as seen in
FIG. 10, in order to prepare a particular type of compliance form for
submission
to an agency responsible for reviewing the compliance form. The compliance
program 100 may present a compliance form selection screen to the user so that
the user can select the desired type of compliance form to be processed.
Alternatively, the compliance form may be automatically selected by the
compliance program 100 based on a selection factor, such as a website being
used by the user, a log-in identification of the user, other identification of
the
user, etc., which in some manner indicates the type of compliance form to be
processed. The compliance program 100 may reside on the actual computing
device 102 that the user interfaces with or, alternatively, the compliance
program
100 may reside on a remote computing device 103 such as a server or the like
as illustrated. In such an instances, the computing device 102 that is
utilized by
the user communicates via the remote computing device 103 using an
application 105 contained on the computing device 102. The compliance
program 100 may also be run using conventional Internet browser software.
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Communication between the computing device 102 and the remote computing
device 103 may occur over a wide area network such as the Internet.
Communication may also occur over a private communication network (e.g.,
mobile phone network).
[00109] A user initiating the compliance program 100, as explained herein, may
import entity-specific compliance data from one or more data sources 48.
Compliance data may also be input manually with user input 48a. The entity-
specific compliance data is written to the shared data store 42, such as by
populating the schema 44.
[00110] After entity-specific compliance data has been obtained, the
calculation
engine 50 computes one or more calculations and logic operations dynamically
using the calculation graph 14 for the domain model 11 of the selected
compliance form, based on the then available data at any given instance within
the schema 44 in the shared data store 42. In some instances, estimates or
educated guesses may be made for missing data. As the compliance program
100 is performing any of it operations, such importing compliance data or
executing the calculation engine, the error check engine 150 and explanation
engine 110 are executing to provide explanations, identify errors, generate
error
explanations, and provide the user with one or more narrative explanations
regarding calculations or operations being performed. The errors and/or error
explanations are reported to the user in a report or displayed to the user via
the
Ul manager 82.
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[001 1 1] Concurrently, the logic agent 60 reads the run time data 62 which
represents the instantiated representation of the canonical tax schema 44 at
runtime. The logic agent 60 then utilizes the completeness graph 12 and/or
decision tables 30 for the domain model 11 of the selected compliance form to
generate and send non-binding suggestions 66 to the Ul control 80.
Alternatively, the logic agent 60 may determine that completeness has been
achieved for the selected compliance form in which case a done instruction may
be delivered to the U I control 80. If not done, the process continues whereby
the
user interface manager 82 will then process the suggestion(s) 66 using the
user
interface assets 19 for the domain model 11 of the selected compliance form,
including the respective suggestion resolution element 88, user interface
presentation 84, configuration file, interview screens, and/or forms. The user
interface manager 82 then generates and presents a user interface presentation
84 to the user as seen whereby the user is presented with one or more prompts.
The prompts may include questions, affirmations, confirmations, declaratory
statements, and the like. The prompts are displayed on a screen 104 of the
computing device 102 whereby the user can then respond to the same by using
one or more input devices associated with the computing device 102 (e.g.,
keyboard, mouse, finger, stylus, voice recognition, etc.).
[00112] The response or responses that are given by the user of the
compliance program 100 are then written to the shared data store 42 to thereby
update all appropriate fields of the schema 44. The process then repeats and
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continues as explained above until a completeness state has been reached and
a done instruction is sent to the Ul control 80.
[00113] FIG. 12 generally illustrates components of a computing device 102,
103 that may be utilized to execute the software for automatically calculating
and
preparing a compliance form for electronic or paper submission. The
components of the computing device 102/103 include a memory 300, program
instructions 302, a processor or controller 304 to execute program
instructions
302, a network or communications interface 306, e.g., for communications with
a
network or interconnect 308 between such components. The computing device
102, 103 may include a server, a personal computer, laptop, tablet, mobile
phone, or other portable electronic device. The memory 300 may be or include
one or more of cache, RAM, ROM, SRAM, DRAM, RDRAM, EEPROM and other
types of volatile or non-volatile memory capable of storing data. The
processor
unit 304 may be or include multiple processors, a single threaded processor, a
multi-threaded processor, a multi-core processor, or other type of processor
capable of processing data. Depending on the particular system component
(e.g., whether the component is a computer or a hand held mobile
communications device), the interconnect 308 may include a system bus, LDT,
PCI, ISA, or other types of buses, and the communications or network interface
may, for example, be an Ethernet interface, a Frame Relay interface, or other
interface. The interface 306 may be configured to enable a system component to
communicate with other system components across a network which may be a
wireless or various other networks. It should be noted that one or more
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components of the computing device 102, 103 may be located remotely and
accessed via a network. Accordingly, the system configuration illustrated in
FIG.
14 is provided to generally illustrate how embodiments may be configured and
implemented.
[00114] The method embodiments described herein may also be embodied in,
or readable from, a non-transitory computer-readable medium or carrier, e.g.,
one or more of the fixed and/or removable data storage data devices and/or
data
communications devices connected to a computer. Carriers may be, for
example, magnetic storage medium, optical storage medium and magneto-
optical storage medium. Examples of carriers include, but are not limited to,
a
floppy diskette, a memory stick or a flash drive, CD-R, CD-RW, CD-ROM, DVD-
R, DVD-RW, or other carrier now known or later developed capable of storing
data. The processor 304 performs steps or executes program instructions 302
within memory 300 and/or embodied on the carrier to implement the method
embodiments.
[00115] 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.
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