Yao Morin, Ph.D.

This disclosure relates to customizing deployment of an application to a user interface of a client device. An exemplary method includes training a model based on historical context information of a plurality of users by identifying correlations between the historical context information and a plurality of user interface components. The method further includes receiving context information from the client device. The method further includes determining a user intent based on the context… Show more

This disclosure relates to customizing deployment of an application to a user interface of a client device. An exemplary method includes training a model based on historical context information of a plurality of users by identifying correlations between the historical context information and a plurality of user interface components. The method further includes receiving context information from the client device. The method further includes determining a user intent based on the context information using the model. The method further includes customizing one or more widgets by selecting one or more user interface components to include in the one or more widgets based on the user intent. The method further includes generating a custom user interface definition comprising the one or more widgets. The method further includes transmitting, to the user interface of the client device, the custom user interface definition. Show less

A method and system provide estimated tax refund data to a user of a tax return preparation system throughout personalized tax return preparation interview. The method and system receive current user tax related data associated with the user, retrieve tax rules data, and gather historical tax related data associated with historical users of the tax return preparation system. The method and system further generate probabilistic inference data including inferences about tax related… Show more

A method and system provide estimated tax refund data to a user of a tax return preparation system throughout personalized tax return preparation interview. The method and system receive current user tax related data associated with the user, retrieve tax rules data, and gather historical tax related data associated with historical users of the tax return preparation system. The method and system further generate probabilistic inference data including inferences about tax related characteristics of the user based on the historical tax related data and the tax rules data. The method and system provide estimated tax refund data to the user based on the probabilistic inference data. Show less

Systems and methods are provided that, in some embodiments that extract user data from at least one data warehouse. The user data is sorted within each dimension, and partitions each dimension into bins. Clusters are defined as each bin that includes user data for a number of users that exceeds a threshold. Clusters are determined for every combination of dimensions. Each combination of clusters that exceed the threshold is defined as clusters that are formed from multiple dimensions. All… Show more

Systems and methods are provided that, in some embodiments that extract user data from at least one data warehouse. The user data is sorted within each dimension, and partitions each dimension into bins. Clusters are defined as each bin that includes user data for a number of users that exceeds a threshold. Clusters are determined for every combination of dimensions. Each combination of clusters that exceed the threshold is defined as clusters that are formed from multiple dimensions. All clusters and other clusters are stored into a cluster definition table. The clusters are used to analyze the profile of specific users. Show less

Computing systems, computer-implemented methods, articles of manufacture for making personalized assessments regarding whether a taxpayer should be presented with a standardized flow of interview screens, questions or topics, or with an itemized deduction flow of interview screens, questions or topics. This assessment is made utilizing a generated user interface and analytic data elements that generate outputs that reflect the taxpayer's data, e.g., in the form of ranges of numerical data that… Show more

Computing systems, computer-implemented methods, articles of manufacture for making personalized assessments regarding whether a taxpayer should be presented with a standardized flow of interview screens, questions or topics, or with an itemized deduction flow of interview screens, questions or topics. This assessment is made utilizing a generated user interface and analytic data elements that generate outputs that reflect the taxpayer's data, e.g., in the form of ranges of numerical data that are based on the taxpayer's data. User interface elements representing response options in the form of range data may be selected by the user without entering specific electronic tax return data for the purpose of making standardized v. itemized determinations and to determine which questions or topics can be bypassed. Show less

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… Show more

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. Show less

Method and apparatus for providing a personalized self-support service to a user of an online application coupled with an online community forum. Embodiments include obtaining a plurality of questions from the online community forum and obtaining historical user data. Embodiments further include identifying one or more part-of-speech words in the plurality of questions and generating a high-dimensional vector for each question of the plurality of questions based on a frequency of the one or… Show more

Method and apparatus for providing a personalized self-support service to a user of an online application coupled with an online community forum. Embodiments include obtaining a plurality of questions from the online community forum and obtaining historical user data. Embodiments further include identifying one or more part-of-speech words in the plurality of questions and generating a high-dimensional vector for each question of the plurality of questions based on a frequency of the one or more part-of-speech words. Embodiments further include identifying one or more user features of the plurality of users based on the historical user data and establishing, based on the historical user data, one or more statistical correlations between user features and part-of-speech words. Embodiments further include training a predictive model based on the one or more statistical correlations. Embodiments further include using the predictive model to predict to provide one or more relevant questions to the user. Show less

The present disclosure relates to dynamically generating user interfaces based on a user's emotional state. An example method generally includes a computer system receiving emotional response data from a client device. The computer system identifies a version of a user experience to present on the client device based on the received emotional response data and generates code for rendering a user interface associated with the identified version of the user experience. The generated code is… Show more

The present disclosure relates to dynamically generating user interfaces based on a user's emotional state. An example method generally includes a computer system receiving emotional response data from a client device. The computer system identifies a version of a user experience to present on the client device based on the received emotional response data and generates code for rendering a user interface associated with the identified version of the user experience. The generated code is transmitted to the client device for rendering and presentation on the client device. Show less

A method and system provides personalized user experiences to users of a tax return preparation system, at least partially based on likelihoods of occurrence of life events for the users in a tax year, according to one embodiment. The method and system applies the user data to one or more predictive models to determine the likelihood that one or more available life events occurred in a user's life in a tax year, according to one embodiment. The method and system display life event icons that… Show more

A method and system provides personalized user experiences to users of a tax return preparation system, at least partially based on likelihoods of occurrence of life events for the users in a tax year, according to one embodiment. The method and system applies the user data to one or more predictive models to determine the likelihood that one or more available life events occurred in a user's life in a tax year, according to one embodiment. The method and system display life event icons that represent the one or more available life events, and the life event icons are ranked, sorted, and/or emphasized, based on the likelihood that the one or more available life events occurred in a user's life, to increase a user's confidence in the tax return preparations system's capability to address the user's life changes while preparing the user's tax return, according to one embodiment. Show less

An input signal may be classified by computing correlations between feature vectors of the input signal and feature vectors of reference signals, wherein the reference signals correspond to a class. The feature vectors of the input signal and/or the reference signals may be segmented to identify portions of the signals before performing the correlations. Multiple correlations of the segments may be combined to produce a segment score corresponding to a segment. The signal may then be classified… Show more

An input signal may be classified by computing correlations between feature vectors of the input signal and feature vectors of reference signals, wherein the reference signals correspond to a class. The feature vectors of the input signal and/or the reference signals may be segmented to identify portions of the signals before performing the correlations. Multiple correlations of the segments may be combined to produce a segment score corresponding to a segment. The signal may then be classified using multiple segment scores, for example by comparing a combination of the segment scores to a threshold. Show less

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… Show more

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. Show less

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… Show more

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. Show less

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… Show more

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. Show less

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… Show more

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. Show less

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… Show more

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. Show less

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… Show more

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. Show less

An estimate of a pitch of a signal may be computed by using peak-to-peak distances in a frequency representation of the signal. A frequency representation of the signal may be computed, peaks in the frequency representation may be identified, for example, by identifying peaks larger than a threshold value. Peak-to-peak distances may be determined using the locations in frequency of the peaks. The pitch of the signal may be estimated by, for example, estimating cumulative distribution function… Show more

An estimate of a pitch of a signal may be computed by using peak-to-peak distances in a frequency representation of the signal. A frequency representation of the signal may be computed, peaks in the frequency representation may be identified, for example, by identifying peaks larger than a threshold value. Peak-to-peak distances may be determined using the locations in frequency of the peaks. The pitch of the signal may be estimated by, for example, estimating cumulative distribution function of the peak-to-peak distances or computing a histogram of the peak-to-peak distances. Show less

Devices, systems and methods are disclosed for estimating characteristics of noise included in one-dimensional data. For example, a number of data points associated with noise below each of a plurality of thresholds may be determined to calculate a cumulative distribution function. A probability density function may be derived from the cumulative distribution function. A variance may be calculated from the cumulative distribution function and/or the probability density function. The noise may… Show more

Devices, systems and methods are disclosed for estimating characteristics of noise included in one-dimensional data. For example, a number of data points associated with noise below each of a plurality of thresholds may be determined to calculate a cumulative distribution function. A probability density function may be derived from the cumulative distribution function. A variance may be calculated from the cumulative distribution function and/or the probability density function. The noise may be modeled using the variance and other characteristics determined from the cumulative distribution function and/or the probability density function. Show less

Devices, systems and methods are disclosed for reducing noise in input data by performing a hysteresis operation followed by a lateral excitation smoothing operation. For example, an audio signal may be represented as a sequence of feature vectors. A row of the sequence of feature vectors may, for example, be associated with the same harmonic of the audio signal at different points in time. To determine portions of the row that correspond to the harmonic being present, the system may compare an… Show more

Devices, systems and methods are disclosed for reducing noise in input data by performing a hysteresis operation followed by a lateral excitation smoothing operation. For example, an audio signal may be represented as a sequence of feature vectors. A row of the sequence of feature vectors may, for example, be associated with the same harmonic of the audio signal at different points in time. To determine portions of the row that correspond to the harmonic being present, the system may compare an amplitude to a low threshold and a high threshold and select a series of data points that are above the low threshold and include at least one data point above the high threshold. The system may iteratively perform a spreading technique, spreading a center value of a center data point in a kernel to neighboring data points in the kernel, to further reduce noise. Show less

Devices, systems and methods are disclosed for reducing noise in input data by performing a hysteresis operation followed by a lateral excitation smoothing operation. For example, an audio signal may be represented as a sequence of feature vectors. A row of the sequence of feature vectors may, for example, be associated with the same harmonic of the audio signal at different points in time. To determine portions of the row that correspond to the harmonic being present, the system may compare an… Show more

Devices, systems and methods are disclosed for reducing noise in input data by performing a hysteresis operation followed by a lateral excitation smoothing operation. For example, an audio signal may be represented as a sequence of feature vectors. A row of the sequence of feature vectors may, for example, be associated with the same harmonic of the audio signal at different points in time. To determine portions of the row that correspond to the harmonic being present, the system may compare an amplitude to a low threshold and a high threshold and select a series of data points that are above the low threshold and include at least one data point above the high threshold. The system may iteratively perform a spreading technique, spreading a center value of a center data point in a kernel to neighboring data points in the kernel, to further reduce noise. Show less

An estimate of a pitch of a signal may be computed by using correlations of frequency portions of a frequency representation of the signal. An initial pitch estimate may be obtained and frequency portions of the frequency representation may be identified using multiples of the initial pitch estimate. Correlations of the frequency portions may be computed, and a score for the initial pitch estimate may be determined using the correlations. A second pitch estimate may be determined using the… Show more

An estimate of a pitch of a signal may be computed by using correlations of frequency portions of a frequency representation of the signal. An initial pitch estimate may be obtained and frequency portions of the frequency representation may be identified using multiples of the initial pitch estimate. Correlations of the frequency portions may be computed, and a score for the initial pitch estimate may be determined using the correlations. A second pitch estimate may be determined using the first score, and the process may be repeated. Show less

Voice enhancement and/or speech features extraction may be performed on noisy audio signals. An input signal may convey audio comprising a speech component superimposed on a noise component. The input signal may be segmented into discrete successive time windows including a first time window spanning a duration greater than a sampling interval of the input signal. A transform may be performed on individual time windows of the input signal to obtain corresponding sound models of the input signal… Show more

Voice enhancement and/or speech features extraction may be performed on noisy audio signals. An input signal may convey audio comprising a speech component superimposed on a noise component. The input signal may be segmented into discrete successive time windows including a first time window spanning a duration greater than a sampling interval of the input signal. A transform may be performed on individual time windows of the input signal to obtain corresponding sound models of the input signal in the individual time windows. A first sound model may describe a superposition of harmonics sharing a common pitch and chirp in the first time window of the input signal. Linear fits in time of the sound models over individual time windows of the input signal may be obtained. The linear fits may include a first linear fit in time of the first sound model over the first time window. Show less