Abstract: Systems and methods of the present disclosure provide processes for determining how much to adjust machine-learning parameter values in a direction of a gradient for gradient-descent steps in training processes for machine-learning models. Current parameter values of a machine-learning model are vector components that define an initial estimate for a local extremum of a cost function used to measure how well the machine-learning model performs. The initial estimate and the gradient of the cost function for the initial estimate are used to define an auxiliary function. A root estimate is determined for the auxiliary function of the gradient. The parameters are adjusted in the direction of the gradient by an amount specified by the root estimate.

Abstract: Systems and methods of the present disclosure provide processes for determining how much to adjust machine-learning parameter values in a direction of a gradient for gradient-descent steps in training processes for machine-learning models. Current parameter values of a machine-learning model are vector components that define an initial estimate for a local extremum of a cost function used to measure how well the machine-learning model performs. The initial estimate and the gradient of the cost function for the initial estimate are used to define an auxiliary function. A root estimate is determined for the auxiliary function of the gradient. The parameters are adjusted in the direction of the gradient by an amount specified by the root estimate.

Abstract: A method and system to learn new forms to be incorporated into an electronic document preparation system, or to learn the behavior of existing systems, receive form data related to a new form having a plurality of data fields that expect data values based on specific functions. The method and system gather training set data including previously filled forms having completed data fields corresponding to the data fields of the new form. The method and system include multiple analysis modules that each generate candidate functions for providing data values for the data fields of the new form. The method and system evaluate the candidate functions from each analysis technique and select the candidate functions that are most accurate based on comparisons with the training set data.

Abstract: 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.

Abstract: A method involves scanning a symbol presented on a point of sale (POS) system including a POS processor; decoding the symbol to obtain purchase data and bidirectional connection data, where the bidirectional connection data describes a bidirectional connection; generating payment data using the purchase data; establishing the bidirectional connection with the POS system; sending the payment data to the POS system over the bidirectional connection; and receiving payment confirmation from the POS system over the bidirectional connection in response to the sending the payment data.

Abstract: Systems and methods of the present disclosure provide processes for determining how much to adjust machine-learning parameter values in a direction of a gradient for gradient-descent steps in training processes for machine-learning models. Current parameter values of a machine-learning model are vector components that define an initial estimate for a local extremum of a cost function used to measure how well the machine-learning model performs. The initial estimate and the gradient of the cost function for the initial estimate are used to define an auxiliary function. A root estimate is determined for the auxiliary function of the gradient. The parameters are adjusted in the direction of the gradient by an amount specified by the root estimate.

Abstract: Systems and methods of the present disclosure provide processes for determining how much to adjust machine-learning parameter values in a direction of a gradient for gradient-descent steps in training processes for machine-learning models. Current parameter values of a machine-learning model are vector components that define an initial estimate for a local extremum of a cost function used to measure how well the machine-learning model performs. The initial estimate and the gradient of the cost function for the initial estimate are used to define an auxiliary function. A root estimate is determined for the auxiliary function of the gradient. The parameters are adjusted in the direction of the gradient by an amount specified by the root estimate.

Abstract: A method and system to learn new forms to be incorporated into an electronic document preparation system, or to learn the behavior of existing systems, receive form data related to a new form having a plurality of data fields that expect data values based on specific functions. The method and system gather training set data including previously filled forms having completed data fields corresponding to the data fields of the new form. The method and system include multiple analysis modules that each generate candidate functions for providing data values for the data fields of the new form. The method and system evaluate the candidate functions from each analysis technique and select the candidate functions that are most accurate based on comparisons with the training set data.

Abstract: A method and system to learn new forms to be incorporated into an electronic document preparation system, or to learn the behavior of existing systems, receive form data related to a new form having a plurality of data fields that expect data values based on specific functions. The method and system gather training set data including previously filled forms having completed data fields corresponding to the data fields of the new form. The method and system include multiple analysis modules that each generate candidate functions for providing data values for the data fields of the new form. The method and system evaluate the candidate functions from each analysis technique and select the candidate functions that are most accurate based on comparisons with the training set data.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method involves scanning a symbol presented on a point of sale (POS) system including a POS processor; decoding the symbol to obtain purchase data and bidirectional connection data, where the bidirectional connection data describes a bidirectional connection; generating payment data using the purchase data; establishing the bidirectional connection with the POS system; sending the payment data to the POS system over the bidirectional connection; and receiving payment confirmation from the POS system over the bidirectional connection in response to the sending the payment data.

Abstract: A system for calculating an estimated result for an electronic tax return to be prepared before a user begins to prepare the electronic tax return using an electronic tax return preparation program includes a server computer having a predictive model, and a user computer having a browser program. The user computer and the browser program are operatively coupled to the server computer and the predictive model by a network. The server computer is configured to obtain a first taxpayer datum associated with a taxpayer and execute the predictive model, which generates a predicted taxpayer datum for the taxpayer based on the first taxpayer datum. The server computer is configured to calculate the estimated result using the predicted taxpayer datum. The user computer is configured to display the estimated result to the user before the user begins to prepare the electronic tax return using the electronic tax return preparation program.

Abstract: A non-transitory computer readable medium including instructions that, when executed by a processor, perform a method involving: receiving, by a point of sale (POS) system, purchase data for a customer; generating bidirectional connection data for the customer, where the bidirectional connection data describes a bidirectional connection; encoding a symbol with the purchase data and the bidirectional connection data; presenting the symbol to the customer; enabling the bidirectional connection in response to a connection request from the customer; receiving payment data from the customer over the bidirectional connection; comparing the payment data to the purchase data; and sending a payment confirmation to the customer over the bidirectional connection in response to a determination that the payment data satisfies the purchase data.

Abstract: 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.

Abstract: 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.

Abstract: Computer-implemented methods, systems and articles of manufacture for assessing trustworthiness of electronic tax return data. Systems may include modular components including a confidence module that determines at least one attribute of a source of the electronic tax return data, determines a confidence score for the electronic tax return data based at least in part upon at least one source attribute, compares the confidence score and pre-determined criteria, and generates an output indicating whether the confidence score for the electronic tax return data satisfies the pre-determined criteria. When the confidence score does not satisfy the pre-determined criteria, the user can be presented with an alert or message. Confidence scores can be generated and may also be displayed for specific electronic tax return data or fields, a tax form or worksheet, an interview screen, a tax topic, or the tax return as a whole, e.g., for purposes of determining audit risk.

Abstract: A method and system to learn new forms to be incorporated into an electronic document preparation system, or to learn the behavior of existing systems, receive form data related to a new form having a plurality of data fields that expect data values based on specific functions. The method and system gather training set data including previously filled forms having completed data fields corresponding to the data fields of the new form. The method and system include multiple analysis modules that each generate candidate functions for providing data values for the data fields of the new form. The method and system evaluate the candidate functions from each analysis technique and select the candidate functions that are most accurate based on comparisons with the training set data.