Intuit, Inc.

Workday, Inc.

Jonathan Goldman

Massimo Mascaro

William Taylor Laaser

Luis Felipe Cabrera

Lucian Vlad Lita

Efraim David Feinstein

Vladimir Giverts

Kristy Gateley

Adeyemi Ajao

Barzel Segal

Phil Monroe

Jan Prach

Tarek Rabbani

Anuranjita Tewary

Igor A Podgorny

Todd Frey Goodyear

Nima Sarshar

Methods, systems and articles of manufacture for using one or more predictive models to predict which tax matters are relevant to a particular taxpayer during preparation of an electronic tax return. A tax return preparation system accesses taxpayer data such as personal data and/or tax data regarding the particular taxpayer. The system executes a predictive model which receives the taxpayer data as inputs to the predictive model. The predictive model generates as output(s) one or more predicted tax matters which are determined to be likely to be relevant to the taxpayer. The system may then determine tax questions to present to the user based at least in part upon the predicted tax matters determined by the predictive model.

Methods systems and articles of manufacture for using a predictive model to determine tax topics which are relevant to a taxpayer in preparing an electronic tax return

Computer-implemented methods, articles of manufacture and computerized systems for identifying or alerting a user of certain data in electronic tax returns. A computerized tax return preparation system including a tax return preparation software application executed by a computing device receives first and second tax data and populates respective fields of the electronic tax return. The system executes a predictive model such as logistic regression, naive bayes, K-means clustering, clustering, k-nearest neighbor, and neural networks. First tax data is an input into the predictive model, which generates an output, which is compared with second tax data. An alert is generated when the second tax data does not satisfy pre-determined criteria relative to the first output generated by the predictive model. The same or other predictive model may be used as additional tax data is received for subsequent tax data analysis.

Predictive model based identification of potential errors in electronic tax return

A computer analytic system for allocating resources of an electronic tax return preparation system, the system includes an information module configured to collect taxpayer data of a user, the taxpayer data including an indicator of an increased likelihood that the user will abandon the electronic tax return preparation program. The system also includes a memory module configured to store the collected taxpayer data. The system further includes a retention module configured to analyze the collected taxpayer data and generate a resource allocation recommendation based on the collected taxpayer data.

Systems for allocating resources based on electronic tax return preparation program user characteristics

A computer-implemented method of generating rich content webpages from a question and answer (Q&A) library includes providing a topic and one or more seed questions related to the topic. The computing device searches the one or more seed questions against all questions in the Q&A library and identifies questions related to the topic. The computing device clusters the text of the questions related to the topic into a plurality of clusters and then removes substantial duplicates from the plurality of clusters. The computing device generates a rich content webpage by aggregating a question from each cluster onto a single webpage containing the topic.

System and method for auto-curation of Q and A websites for search engine optimization

Computer-implemented methods, articles of manufacture and computerized systems for identifying or alerting a user of certain data in electronic tax returns. A computerized tax return preparation system including a tax return preparation software application executed by a computing device receives electronic tax return data and populates a field of the electronic tax return. The system executes a constraint engine that compares the electronic tax return data with a constraint of a tax authority requirement expressed in a declarative format. An alert is generated for the user of the tax return preparation software application when the electronic tax data does not satisfy the declarative constraint.

Identification of electronic tax return errors based on declarative constraints

A system for determining a canonical title includes an interface and a processor. The interface is to receive a new title. The processor is to generate a set of n-grams based on the new title; look up n-grams in canonical database to assign one or more labels; and map the new title to a canonical title based on the one or more labels.

Method for systematic mass normalization of titles

A method for normalizing raw titles to canonical titles is described. The method includes designating a set of canonical titles, generating a set of n-grams for each canonical title, assigning a set of attributes to each n-gram, assigning a set of labels to each of the attributes, and storing the labeled canonical title and labeled n-grams in a database. In some examples, a new title may be mapped to an existing canonical title in the database by generating a set of n-grams for the new title, looking up the n-grams in the database of canonical titles, retrieving the set of labels assigned to n-grams in the database that match n-grams from the new title, and assigning those labels to the corresponding attributes of the new title. The new title may then be mapped to a canonical title on the basis of similarly labeled attributes.

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Mountain View, CA, United States of America

Jonathan Goldman