Abstract: A method and system improve retention of a user of a tax return preparation system by personalizing a tax return preparation interview with questions that are at least partially based on user data processed by one or more predictive models, according to one embodiment. The method and system include receiving user data that is associated with a user, and applying the user data to one or more predictive models to cause the one or more predictive models to generate predictive output data, according to one embodiment. The predictive output data are scores for a subset of questions, and scores represent a relevance to the user of each of the subset of questions, according to one embodiment. The method and system include presenting selected ones of the subset of questions to the user, at least partially based on the scores, to personalize a tax return preparation interview for the user.

Abstract: A method and system applies analytics models to a tax return preparation system to determine a likelihood of qualification for an earned income tax credit by a user, according to one embodiment. The method and system receive user data and applying the user data to a predictive model to cause the predictive model to determine, at least partially based on the user data, a likelihood of qualification for an earned income tax credit for the user, according to one embodiment. The method and system display, for the user, an estimated tax return benefit to the user, at least partially based on the likelihood of qualification for the earned income tax credit exceeding a predetermined threshold, to reduce delays in presenting estimated earned income tax credit benefits to the user during a tax return preparation session in a tax return preparation system, according to one embodiment.

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: A method and system provides augmented OCR data to a user of a financial system. The method and system include receiving image data related to an image of a financial document of the user and generating OCR data based on the image data. The method and system further include receiving financial data related to the financial document, analyzing the financial document, and generating the augmented OCR data based on the OCR data and the financial data.

Abstract: A method and system facilitates the development of data science transformations in one programming language and the deployment of the data science transformations in another programming language, according to one embodiment. The method and system preserves relationships, functions, configurations, and characteristics between combinations of data transformations, according to one embodiment. The preservation of the relationships, functions, configurations, and characteristics is enabled by developing and providing a set of low-level (e.g., atomic) transformations that enable users to build their own models, libraries, and configurations into macro-transformations (e.g., conglomerate transformations), according to one embodiment.

Abstract: An estimate of a fractional chirp rate of a signal may be computed by using multiple frequency representations of the signal. A first frequency representation may be computed using a first fractional chirp rate and a first score may be computed using the first frequency representation that indicates a match between the first fractional chirp rate and a fractional chirp rate of the signal. A second frequency representation may be computed using a second fractional chirp rate and a second score may be computed using the second frequency representation that indicates a match between the second fractional chirp rate and the fractional chirp rate of the signal. The fractional chirp rate of the signal may be estimated using the first score and the second score, for example, by selecting a fractional chirp rate corresponding to a highest score.

Abstract: Biometric data is collected to obtain more detailed, connected, and reliable feedback data from users of an interactive software system that has a more empirical and objective basis. The biometric data is then used to create emotional pattern predictive model data representing emotional pattern predictive models associated with users of the interactive software system. The individual emotional pattern predictive models associated with multiple users of the interactive software system are then analyzed and processed to generate emotional pattern profile data for categories of users. These biometric data based predictive models are then used for targeted product diagnosis, targeted interventions, targeted marketing/upsell attempts, and grouping and analysis of feedback and user categories and feedback sources.

Abstract: Features that may be computed from a harmonic signal include a fractional chirp rate, a pitch, and amplitudes of the harmonics. A fractional chirp rate may be estimated, for example, by computing scores corresponding to different fractional chirp rates and selecting a highest score. A first pitch may be computed from a frequency representation that is computed using the estimated fractional chirp rate, for example, by using peak-to-peak distances in the frequency distribution. A second pitch may be computed using the first pitch, and a frequency representation of the signal, for example, by using correlations of portions of the frequency representation. Amplitudes of harmonics of the signal may be determined using the estimated fractional chirp rate and second pitch. Any of the estimated fractional chirp rate, second pitch, and harmonic amplitudes may be used for further processing, such as speech recognition, speaker verification, speaker identification, or signal reconstruction.

Abstract: A method and system facilitates the development of data science transformations in one programming language and the deployment of the data science transformations in another programming language, according to one embodiment. The method and system preserves relationships, functions, configurations, and characteristics between combinations of data transformations, according to one embodiment. The preservation of the relationships, functions, configurations, and characteristics is enabled by developing and providing a set of low-level (e.g., atomic) transformations that enable users to build their own models, libraries, and configurations into macro-transformations (e.g., conglomerate transformations), according to one embodiment.

Abstract: A method and system adaptively improves potential customer conversion rates, revenue metrics, and/or other target metrics by providing effective user experience options to some users while concurrently testing user responses to other user experience options, according to one embodiment. The method and system selects the user experience options by applying user characteristics data to an analytics model to identify a predictive model that selects or determines the user experience options, according to one embodiment. The method and system analyzes user responses to the predictive model and/or user experience options to update the analytics model, and to dynamically adapt the personalization of the user experience options, according to one embodiment. The method and system dynamically and automatically defines, evaluates, and updates analytics models to provide progressively improving personalization of user experiences in a software system.

Abstract: A method and system adaptively improves potential customer conversion rates, revenue metrics, and/or other target metrics by providing effective user experience options to some users while concurrently testing user responses to other user experience options, according to one embodiment. The method and system selects the user experience options by applying user characteristics data to an analytics model, according to one embodiment. The method and system analyzes user responses to the user experience options to update the analytics model, and to dynamically adapt the personalization of the user experience options, according to one embodiment. The method and system dynamically and automatically defines, evaluates, and updates analytics models to provide progressively improving personalization of user experiences in a software system.

Abstract: The present disclosure relates to generating estimated results of an interactive workflow based on user input of one or more representative data points and results of the interactive workflow for other users. According to one embodiment, a workflow processing system receives a request to estimate a result expected to be obtained by a first user in completing the interactive workflow and receives one or more representative data points characterizing the first user. The workflow processing system determines an estimate of the result expected to be obtained by a first user in completing the interactive workflow based on the representative data points and an analysis of results of the interactive workflow obtained by a plurality of second users in completing the interactive workflow, wherein each of the plurality of second users share the representative data points characterizing the first user.

Abstract: A computing platform identifies one or more characteristics of a user accessing application software or a software service via a user interface and a field to be filled or completed in the user interface of the application software or a software service. The computing platform further determines and presents, at one or more automatic fill or completion modules that are stored at least partially in memory and function in tandem with one or more computer processors in the computing platform, a list of one or more completion candidates in the user interface of the application software or a software service at least by performing one or more incremental searches based in part or in whole upon the one or more characteristics. The field is then populated with a completion candidate from the list of one or more completion candidates.

Abstract: A method and system adaptively improves potential customer conversion rates, revenue metrics, and/or other target metrics by providing effective user experience options, from a variety of different user experience options, to some users while concurrently testing user responses to other user experience options, according to one embodiment. The method and system selects the user experience options by applying user characteristics data to an analytics model, according to one embodiment. The characteristics of the analytics model are adjusted and/or tuned to control/reduce uncertainty in identifying effective user experience options, according to one embodiment. The method and system analyzes user responses to the user experience options to update the analytics model, and to dynamically adapt the personalization of the user experience options, at least partially based on feedback from users, according to one embodiment.

Abstract: A method and system adaptively improves potential customer conversion rates, revenue metrics, and/or other target metrics by providing effective user experience options, from a variety of different user experience options, to some users while concurrently testing user responses to other user experience options, according to one embodiment. The method and system selects the user experience options by applying user characteristics data to an analytics model, according to one embodiment. The user characteristics data include time data, e.g., when a user interacts with a software system, according to one embodiment. The method and system applies one or more time filters to data samples to train the analytics model, and the method and system analyzes user responses to the user experience options to update the analytics model, and to dynamically adapt the personalization of the user experience options, at least partially based on feedback from users, according to one embodiment.

Abstract: A method and system adaptively improves potential customer conversion rates, revenue metrics, and/or other target metrics by providing effective marketing experience options, from a variety of different marketing experience options, to some users while concurrently testing user responses to other marketing experience options, according to one embodiment. The method and system selects the marketing experience options by applying user characteristics data to an analytics model, according to one embodiment. The method and system analyzes user responses to the marketing experience options to update the analytics model, and to dynamically adapt the personalization of the marketing experience options, at least partially based on feedback from users, according to one embodiment.

Abstract: Octave errors may be reduced during pitch determination for noisy audio signals. Pitch may be tracked over time by determining amplitudes at harmonics for individual time windows of an input signal. Octave errors may be reduced in individual time windows by fitting amplitudes of corresponding harmonics across successive time windows to identify spurious harmonics caused by octave error. A given harmonic may be identified as either being associated with the same pitch as adjacent harmonics in the given time window or being spurious based on parameters of the fitting function.

Abstract: A method and system identifies users who benefit from filing itemized deductions over standardized deductions to reduce an average time consumed for users preparing tax returns with a tax return preparation system, according to one embodiment. The method and system receives user data that is associated with a user, and applies the user data to a predictive model to cause the predictive model to determine a likelihood that the user will decrease his/her taxable income by filing an itemized deduction, according to one embodiment. The method and system deemphasizes and/or postpones the presentation of tax return questions that are related to the itemized deduction, if the likelihood that the user will decrease his/her taxable income by filing the itemized deduction is below a threshold, to reduce a quantity of time consumed by the user to prepare his/her tax return with a tax return preparation system, according to one embodiment.

Abstract: Voice enhancement and/or speech features extraction may be performed on noisy audio signals using successively refined transforms. Downsampled versions of an input signal may be obtained, which include a first downsampled signal with a lower sampling rate than a second downsampled signal. Successive transforms may be performed on the input signal to obtain a corresponding sound model of the input signal. The successive transforms performed may include: (1) performing a first transform on the first downsampled signal to yield a first pitch estimate; (2) performing a second transform on the second downsampled signal to yield a second pitch estimate and a first harmonics estimate based on the first pitch estimate; and (3) performing a third transform on the input signal to yield a third pitch estimate and a second harmonics estimate based on the second pitch estimate and the first harmonics estimate.

Abstract: A dataflow of a distributed application is visualized in a locally simulated execution environment. A scheduler receives a job graph which includes a graph of computational vertices that are designed to be executed on multiple distributed computer systems. The scheduler queries a graph manager to determine which computational vertices of the job graph are ready for execution in a local execution environment. The scheduler queries a cluster manager to determine the organizational topology of the distributed computer systems to simulate the determined topology in the local execution environment. The scheduler queries a data manager to determine data storage locations for each of the computational vertices indicated as being ready for execution in the local execution environment. The scheduler also indicates an instance of each computational vertex to be spawned and executed in the local execution environment based on the organizational topology and indicated data storage locations.