Intuit, Inc.

David Alan Hanekamp, Jr

Gang Wang

Kevin M McCluskey

Luis Felipe Cabrera

Michael A Artamonov

Daniel P Dunbar

Peter Lubczynski

Steven J Atkinson

Per-Kristian G Halvorsen

Ganesh Bhat

Kyle J Ryan

Matthew Allen Brincho

Paul A Parks

Alex G Balazs

Cem Unsal

Henry Tayloe Stansbury

Ion Niciu-Chiuaru

Nankun Huang

Saikat Mukherjee

Anu Sreepathy

Eric Knudtson

Anoop Makwana

Justin Rui Chang Chiang

Maria Regina Villanueva Garcia

Saneesh Joseph

Tracy Fung

Ernest Montoya

Edward Milton Renner

Alberto Garcia

Certain aspects of the present disclosure provide techniques for encoding rules defining a completeness of input, including receiving a first input comprising one or more tuples, wherein a tuple of the one or more tuples comprises one or more fields associated with an operation, one or more indicators, and one or more modifiers; receiving a second input associated with the one or more tuples; providing, to a knowledge engine, the first input and the second input; receiving, from the knowledge engine, a result based on the first input and the second input; determining, based on the result, a first symbol associated with a first tuple of the one or more tuples; and displaying the first symbol, wherein the first symbol indicates whether the first tuple is complete.

Defining and debugging module completeness graphs based on interactive user interface checklist elements

Methods, systems and articles of manufacture for efficiently calculating an electronic tax return, such as within a tax return preparation system. A computerized tax return preparation system accesses taxpayer-specific tax data from a shared data store. The system executes a tax calculation engine configured to perform a plurality of tax calculations based on a tax calculation graph and the taxpayer-specific tax data from the shared data store. The system is configured to perform only the calculations in the tax calculation graph which are changed by new taxpayer-specific tax data received since the preceding tax calculation executed by the tax calculation engine. The system may also determine whether the new taxpayer-specific tax data does, or does not change the calculated tax return and the reason why.

Methods systems and articles of manufacture for efficiently calculating a tax return in a tax return preparation application

Systems, methods and articles of manufacture for performing a comparison of tax results based on different sets of tax data, and generating an explanation as to why the tax results differ or do not differ due to the differences in the tax data. The system includes a computing device, a data store in communication with the computing device and a tax preparation software application executable by the computing device. The tax preparation software application has a tax calculation engine, a tax calculation graph, and a change analysis engine. The tax calculation engine is configured to perform a plurality of tax calculation operations based on the tax calculation graph. The change analysis engine is configured to determine whether tax results based on different tax data differ or do not differ. The system may also generate explanation(s) of the reasons that the tax results differ or do not differ due to the different tax data.

Systems and methods for analyzing and generating explanations for changes in tax return results

A computer-implemented method for generating explanations for a tax calculation or operation performed by tax preparation software is disclosed. A computing device executes a tax calculation engine in connection with the tax preparation software and operates on a tax calculation graph to perform a tax calculation. The tax calculation graph semantically describes data dependent tax operations comprising functional nodes connected to input nodes by one of a plurality of functions, wherein each tax operation is associated with one or more explanations. An explanation engine is executed to generate a narrative explanation from the one or more explanations associated with one of the tax operations and is presented to the user on the computing device.

System and method for generating explanations for tax calculations

Certain aspects of the present disclosure provide techniques for generating a user experience for a software program product based on a knowledge engine. Techniques for generating the user experience include a UI builder tool providing a set of tabular UI views and receiving in each tabular UI view corresponding input data for generating a calculation graph, a completeness graph, a client UI view, and a constants table. Based on the input data, the UI builder tool and knowledge engine can generate a set of artifact files. The knowledge engine can generate and/or execute the calculation graph(s), completeness graph(s), and constants table(s) as defined in the corresponding artifact files. The UI builder tool can generate an instance of the client UI view. With the generated calculation graph(s), completeness graph(s), constants table(s), and an instance of the client UI view, the user experience can be provided to a computing device.

Knowledge engine auto-generation of guided flow experience

Computer-implemented method, tax return preparation systems and computer program products for presenting an explanation for a tax calculation or operation performed by a tax return preparation system when the system is in 'forms' mode. An electronic version of a tax authority form that has been modified by inclusion of interface elements is displayed to a user. In response to the user selecting an interface element while in forms mode, presentation of an explanation regarding a tax calculation or operation for the associated field is invoked. The user interface controller provides data in response to selection of the interface element to an explanation engine, which determines an explanation based at least in part upon the tax calculation graph. The explanation is provided to the user interface controller for presentation to the user.

System and method for indicating sections of electronic tax forms for which narrative explanations can be presented

Systems, methods and articles of manufacture for analyzing past year tax data for a taxpayer and determining an estimated current year tax liability in order to compute estimated tax payments. The tax preparation system comprises a tax estimate engine configured to access a plurality of calculated past year tax calculation graphs. The tax estimate engine analyzes and compares the calculated past year tax calculation graphs to determine similar node(s) and/or dissimilar node(s) and also determines a similar node estimating value for the similar nodes and/or a dissimilar node estimating value for the dissimilar nodes. A tax calculation engine calculates a current year tax calculation graph using the similar node estimating values and/or dissimilar node estimating values. The calculated current year tax calculation graph includes an estimated tax liability for the current year, which the system uses to compute estimated tax payments for the taxpayer for the current tax year.

Systems and methods for analyzing and determining estimated data

Computer-implemented systems, methods and articles for generating an error report listing errors identified in the computerized preparation of a payroll tax form. The system includes a computing device, a data store, and a payroll tax form software application executable by the computing device. The payroll tax form software includes a payroll calculation engine, an error check engine, and one or more error graph(s). Each error graph includes a plurality of interconnected nodes configured to utilize tax data and calculated payroll data to identify errors. The calculation engine is configured to read tax data from the shared data store, perform payroll calculation operations, and write calculated payroll data to the shared data store. The error check engine is configured to read the tax data and payroll data and process the error graph(s) to identify errors and generate an error report listing the errors and/or explanations of the errors.

Systems and methods for generating an error report listing errors in the preparation of a payroll tax form

Systems, methods and articles of manufacture for generating a sub-graph of a tax calculation graph usable by a tax calculation engine to perform tax calculation operations. The system includes a computing device, a data store in communication with the computing device and a tax preparation software application executable by the computing device. The system comprises a tax calculation graph and a sub-graph engine which executes on the computing device. The sub-graph engine is configured to generate a sub-graph of the tax calculation graph based upon a target node and one or more user enterable nodes selected from the nodes of the tax calculation graph. The sub-graph engine analyzes the tax calculation graph in view of the target node and user enterable nodes and generates a sub-graph which excludes all nodes which are not necessary to calculate the target node.

Systems, methods and articles for generating sub-graphs of a tax calculation graph of a tax preparation system

A computer-implemented method for generating an explanation for changes in tax liability over different tax reporting periods includes a computing device executing a tax calculation engine associated with tax preparation software. The tax calculation engine operates on first and second tax calculation graphs representing different tax reporting periods. The first and second tax calculation graphs are different in at least one respect. The first and second tax calculation graphs each describe data dependent tax operations comprising a plurality of interconnecting functional nodes connected by one of a plurality of functions. The computing device automatically identifies the differences among nodes within the respective first and second tax calculation graphs and executes an explanation engine associated with the tax preparation software to generate a textual explanation identifying one or more differences among the nodes.

System and method for generating explanations for year-over-year tax changes

Systems, methods and articles of manufacture for determining impact correlations from a calculation graph for calculating taxes. The system includes a computing device, a data store in communication with the computing device and a tax preparation software application executable by the computing device. The tax preparation software application has a tax calculation engine, a tax calculation graph, and an impact chain engine. The tax calculation engine is configured to perform a plurality of tax calculation operations based on the tax calculation graph. The impact chain engine is configured to analyze the tax calculation graph and determine an impact correlation between a change in a value of a first node caused by a change in a value of a second node utilizing an impact chain which includes both the first node and the second node.

Systems and methods for determining impact correlations from a tax calculation graph of a tax preparation system

A method and system generate sample data set for efficiently and accurately testing a new calculation for preparing a portion of an electronic document for users of an electronic document preparation system. The method and system receive the new calculation and gather historical use data related to previously prepared electronic documents for a large number of historical users. The method and system group the historical users into groups based on which sections of a previous version of electronic document preparation software were executed for each historical user in preparing electronic documents for the historical users. The groups are then sampled by selecting a small number of historical users from each group.

System and method for identifying a subset of total historical users of a document preparation system to represent a full set of test scenarios based on code coverage

Systems, methods and articles of manufacture for determining tax recommendations for a taxpayer by using a tax calculation graph to identify tax variables that a taxpayer can control and modify. The tax preparation system of comprises a recommendation engine configured to analyze a tax calculation graph which is calculated using tax data of the taxpayer. The recommendation engine determines tax variables from the tax calculation graph which can affect the tax result. The recommendation engine analyzes these tax variables to determine which of them can be reasonably controlled by the taxpayer using a controllability model. The recommendation engine then executes a tax calculation engine to calculate the tax calculation graph by varying the taxpayer controllable variables to determine how varying the user controllable variables affects the tax result. The recommendation engine then analyzes the affect on the tax result and determines one or more tax recommendation for the taxpayer.

Systems, methods and articles for determining tax recommendations

A method and system generates sufficient sample data sets for efficiently and accurately testing a new calculation for preparing a portion of an electronic document for users of an electronic document preparation system. The method and system prepares the new calculation and gathers historical user data related to previously prepared electronic documents for a large number of historical users. The method and system generates a representative value for each historical user data, based on the sections of a previous version of electronic document preparation software which were executed for each historical user in preparing electronic documents for the historical users. The method and system groups the historical users based on the hash values which indicates the historical user's behavior in the software. The groups are then sampled by selecting a small number of historical users from each group.

System and method for identifying a subset of total historical users of a data management system to represent a full set of test scenarios based on prehashing of code coverage information

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Carlsbad, CA, United States of America

David Alan Hanekamp, Jr