Intuit, Inc.

Microsoft Technology Licensing, LLC

Amazon Technologies, Inc.

Other

Microsft Corporation

Microsoft Corporation

Luis Felipe Cabrera

M Shannon Lietz

Gang Wang

Kevin M McCluskey

Norbert Paul Kusters

Peter Nicholas Desantis

Javier Godinez

Eric Jason Brandwine

Christopher G Kaler

Peter Sirota

Ian Paul Nowland

Richendra Khanna

Richard Jeffrey Cole

James Robert Hamilton

Thomas Bishop

David Alan Hanekamp, Jr

Per-Kristian G Halvorsen

Catharine Van Ingen

Jai Vasanth

Allan Henry Vermeulen

Steven E Lucco

John Peter Shewchuk

Swaminathan Sivasubramanian

Stefano Stefani

Timothy Andrew Rath

Hilliard Bruce Siegel

Ethan Zane Evans

Erik B Christensen

Jakub Kulesza

David Wortendyke

Ankur Jain

William Taylor Laaser

Rande Anthony Blackman

Marvin Michael Theimer

Bradford H Lovering

Alex G Balazs

Pradeep Vincent

Jonathan A Jenkins

Matthew David Klein

Ravisankar V Pudipeddi

Grant Alexander Macdonald McAlister

Nathan Bartholomew Thomas

Donald F Box

David E Langworthy

Chiranjeeb Buragohain

Peter D Cohen

Elangovan Shanmugam

Giovanni Moises Della-Libera

Patrick James Helland

Thomas Jay Miller

Massimo Mascaro

Jeffrey C Schlimmer

Michael J Graves

Rod N Gamache

Kartik Nadipuram Raghavan

Venkata N Padmanabhan

Bradley Jeffery Behm

Stephen James Millet

Ruben Ernesto Ortega

Roland Alexander Paterson-Jones

Amir R Eftekhari

Mark Joseph Cavage

Satish Ramchandra Thatte

Paul Adrian Oltean

Quinton Robin Hoole

Jay J Yu

Johannes Klein

Alexander Thomas Weinert

Stephen Edward Schmidt

Oliver J Sharp

Robert S Wahbe

Peter W Wieland

Craig Andrew Critchley

Matthew L Sivertson

Geary L Eppley

Daniel P Dunbar

Bradley E Marshall

David R Richardson

John David Cormie

Kenneth David Wolf

Ryan Justin Snodgrass

Tom Killalea

Mark Joseph Zbikowski

Henrik Frystyk Nielsen

Shaun Douglas Pierce

Raymond W McCollum

Aravind Yalamanchi

Yao H Morin

Nicholas J Maniscalco

Yaron Sheffer

Alan Geller

Umesh Madan

Nankun Huang

Michael James Coulson

Keith W Ballinger

David Golds

Ferhan Elvanoglu

Elliot Lee Waingold

Roland Paterson-Jones

Michael Steven Vernal

A system that implements a scalable data storage service may maintain tables in a non-relational data store on behalf of clients. The system may provide a Web services interface through which service requests are received, and an API usable to request that a table be created, deleted, or described; that an item be stored, retrieved, deleted, or its attributes modified; or that a table be queried (or scanned) with filtered items and/or their attributes returned. An asynchronous workflow may be invoked to create or delete a table. Items stored in tables may be partitioned and indexed using a simple or composite primary key. The system may not impose pre-defined limits on table size, and may employ a flexible schema. The service may provide a best-effort or committed throughput model. The system may automatically scale and/or re-partition tables in response to detecting workload changes, node failures, or other conditions or anomalies.

System and method for implementing a scalable data storage service

Systems and methods for mapping an electronic document to a particular transaction category are disclosed. An example method may be performed by one or more processors of a categorization system and include receiving, from a user via an interface, an electronic document associated with a transaction between the user and a particular establishment, receiving, from the user via the interface, an image of the particular establishment, identifying in the image, using an image recognition engine, at least one of a sign or a symbol representative of the particular establishment, extracting, using an analytics module, location information from at least one of the image or a mobile device, determining, using the analytics module, a name of the particular establishment based on at least one of the location information or the at least one sign or symbol, and mapping the electronic document to a particular transaction category based on the determined name.

Augmenting electronic documents with externally produced metadata

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 of calculating tax liability using automatically generated estimates uses a computing device connecting to a shared data store configured to store user-specific tax data therein. The computing device executes an estimation module configured to estimate user-specific tax data and write the estimated user-specific tax data to the shared data store. A tax calculation engine configured to read user-specific tax data from the shared data store and write calculated tax data to the shared data store is executed, wherein at least some of the read data comprises estimated user-specific tax data.

System and method of generating estimates used to calculate taxes

Techniques are described for managing distributed execution of programs. In some situations, the techniques include dynamically modifying the distributed program execution in various manners, such as based on monitored status information. The dynamic modifying of the distributed program execution may include adding and/or removing computing nodes from a cluster that is executing the program, modifying the amount of computing resources that are available for the distributed program execution, terminating or temporarily suspending execution of the program (e.g., if an insufficient quantity of computing nodes of the cluster are available to perform execution), etc.

Dynamically modifying a cluster of computing nodes used for distributed execution of a program

Big data analysis methods and machine learning based models are used to provide offer recommendations to consumers that are probabilistically determined to be relevant to a given consumer. Machine learning based matching of user attributes and offer attributes is first performed to identify potentially relevant offers for a given consumer. A de-duplication process is then used to identify and eliminate any offers represented in the offer data that the consumer has already seen, has historically shown no interest in, has already accepted, that are directed to product or service types the user/consumer already owns, for which the user does not qualify, or that are otherwise deemed to be irrelevant to the consumer.

Replacing a potentially threatening virtual asset

A computer-implemented method for the targeted gathering of tax data for use with tax preparation software includes a computing device presenting to the user a plurality of interview questions or statements. The computing device creates a user profile based on the responses to the interview questions or statements. The computing device identifies highly relevant tax topics based on user profile and executes a user interface manager to automatically generate interview questions or statements on said highly relevant tax topics. The user then confirms whether other tax topics apply to the user. The computing device executes a tax calculation engine of the tax preparation software configured to compute a tax liability or refund amount.

System and method for targeted data gathering for tax preparation

A method and system facilitates importation and categorization of financial documents in a financial system. The method and system include receiving source data and externally generated metadata associated with a financial document, such as a receipt of purchase. The method and system further include analyzing the metadata and categorizing the financial document based on the metadata.

System and method to augment electronic documents with externally produced metadata to improve processing

External events are correlated with patterns of characteristics in virtual assets. Upon detection of a pattern in a different asset that matches a pattern corresponding to an event, that detection is treated as a trigger event, with resulting responsive action(s) and other process operations. Security threats are managed in a similar manner, with first security threats being added to a collection of security threats. When a virtual asset detects a change in operating characteristics, a request is provided for the collection of current security threats, and the collection of security threats is provided responsive to the request.

Method and system for enabling self-monitoring virtual assets to correlate external events with characteristic patterns associated with the virtual assets

Techniques are described for managing distributed execution of programs. In at least some situations, the techniques include decomposing or otherwise separating the execution of a program into multiple distinct execution jobs that may each be executed on a distinct computing node, such as in a parallel manner with each execution job using a distinct subset of input data for the program. In addition, the techniques may include temporarily terminating and later resuming execution of at least some execution jobs, such as by persistently storing an intermediate state of the partial execution of an execution job, and later retrieving and using the stored intermediate state to resume execution of the execution job from the intermediate state. Furthermore, the techniques may be used in conjunction with a distributed program execution service that executes multiple programs on behalf of multiple customers or other users of the service.

Saving program execution state

A computer-implemented method for inferring or estimating user-related data for use with tax preparation software is disclosed. The method uses a computer that connects to the one or more remotely located data sources and executing a data capture utility, the data capture utility capturing at least some tax data pertaining to the user. The computing device executes an estimation module that receives the captured tax data pertaining to the user and generates one or more estimates and stores the one or more estimates in a data store associated with the tax preparation software, the data store configured to store user-specific tax data therein. The computing device executes a tax calculation engine of the tax preparation software configured to read the user-specific tax data contained in the data store and compute an intermediate or final tax liability or refund amount.

System and method for automated data estimation for tax preparation

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

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

Methods, systems and articles of manufacture for a method for generating a database of tax correlation data which can be used for tailoring a user experience in preparing an electronic tax return. A computing device accesses a data source having a plurality of data records. Each data record comprises a taxpayer attribute and a tax related aspect for a respective taxpayer. The computing device analyzes the plurality of data records and determines a correlation between the taxpayer attribute and the tax related aspect and determines a probability for the correlation. The computing device utilizes the probability for the correlation to determine a quantitative relevancy score for a tax matter, which can be incorporated into the tax correlation data of the life/knowledge module.

Methods systems and articles of manufacture for tailoring a user experience in preparing an electronic tax return

A method and system provides a tax refund confidence indicator to a user of a tax return preparation system, according to one embodiment. The method and system include receiving user current tax related data from a user and receiving historical tax related data associated with previously prepared tax returns. The method and system further includes generating estimated tax refund data and confidence score data indicative of the reliability of the estimated tax refund data. The method and system include providing the estimated tax refund data and the confidence score data to the user.

Method and technique to calculate and provide confidence score for predicted tax due/refund

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Bellevue, WA, United States of America

Luis Felipe Cabrera