Intuit, Inc.

Peter Ouyang

Joseph B Cessna

Massimo Mascaro

Leah Zhao

Vitor R Carvalho

Christopher R Rivera

Janani Kalyanam

Yao H Morin

Carol Ann Howe

Xiao Xiao

Andre Felipe Luis

Madelaine Daianu

Ken Yocum

Ye Ji Kim

Crystal Chung

Mohammad Daraghmeh

A method for computer estimations based on statistical tree structures involves obtaining a statistical tree structure for reference elements. The statistical tree structure includes leaf nodes segmenting a statistic for a data label according to data features in the reference elements, and intermediate nodes connecting a first node to the leaf nodes. Each of the first node and the intermediate nodes provide a branching based on one of the data features. The method further includes obtaining target data, including values for the data features, and a value for the data label. The method also includes selecting the first node, associated with a first data feature, traversing the statistical tree structure to a leaf node by matching the values of the data features to the branching of the intermediate nodes, and assessing the value for the data label in the target data based on the statistic associated with the leaf node.

Computer estimations based on statistical tree structures

Disclosed are techniques for generating a personalized flow for a software delivery model. These techniques identify user information expressed in natural language for a specific user. One or more user clusters may be determined for the specific user based on at least one user vector representation of a form of the user information. One or more personalized information clusters may be identified for a user cluster of the one or more user clusters based at least in part on the at least one user vector representation. A personalized software application flow may be generated and presented to the specific user by using at least the one or more personalized information clusters for the specific user.

Methods, systems, and computer program product for generating a personalized flow for a software delivery model

Certain aspects of the present disclosure provide techniques for providing a routing system to a user of a product. An example technique includes receiving from a user of a product a query and a personal ID. Based on the personal ID of the user, the user's profile is retrieved which comprises user attribute data, a clickstream history of the user, and a product SKU of the product. Based on the query and the user profile, processed user data is generated. Additionally, agent profile data for each available agent is retrieved, and based on the user attribute data, the processed user data, and the agent profile data of each agent, a predicted quality score is generated for each agent. The agent with the highest predicted quality score is determined, and the user is routed to the agent with the highest predicted quality score.

Routing system to connect a user with a qualified agent

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 filters out user experience options from delivery to users, if the user experience options are non-compliant with one or more business rules, to maintain business relations for the service provider and to maintain user confidence in the services provided by the service provider (e.g., a tax return preparation system), according to one embodiment.

Method and system for providing personalized user experiences in compliance with service provider business rules

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, among a variety of 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, at least partially based on feedback from users, according to one embodiment. The method and system determines bias weights from characteristics of the analytics model and uses the bias weights to compensate for data biases when updating or generating analytics models.

Method and system for generating user experience analytics models by unbiasing data samples to improve personalization of user experiences in a tax return preparation system

A method for performing transactions using an aggregate payment medium involves obtaining payee data associated with a payee, obtaining transaction data associated with a transaction to be performed between a payer and the payee, and determining, for each payment medium, a benefit and a cost associated with using the payment medium for the transaction. The method further includes obtaining, for each of the payment media, a return from using the payment medium for the transaction, based on the benefit and the cost, selecting, from the payment media, a payment medium with a highest return, and providing, by the aggregate payment medium, payment medium data associated with the selected payment medium to the payee to enable execution of the transaction.

Method and system for performing transactions using aggregate payment media

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.

Method and system for applying dynamic and adaptive testing techniques to a software system to improve selection of predictive models for personalizing user experiences in the software system

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.

Method and system for updating analytics models that are used to dynamically and adaptively provide personalized user experiences in a software system

Disclosed are techniques for generating a personalized flow for a software delivery model. These techniques identify a plurality of business clusters and a plurality of expense categories for the plurality of business clusters and natural language description about a first characteristic of a specific user from an electronic tax return in an electronic tax return preparation and filing software application delivery model. One or more business clusters may be determined from the plurality of business clusters for the specific user based in part or in whole upon a user description vector of at least a part of the description. One or more expense categories may be identified for the electronic tax return of the specific user based in part or in whole upon the one or more business clusters.

Methods, systems, and computer program product for providing expense information for an electronic tax return preparation and filing software delivery model

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.

Method and system for adjusting analytics model characteristics to reduce uncertainty in determining users' preferences for user experience options, to support providing personalized user experiences to users with a software system

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San Diego, CA, United States of America

Peter Ouyang