Intuit, Inc.

Tristan Cooper Baker

Ashok N Srivastava

Joseph B Cessna

Sricharan Kallur Palli Kumar

Frank Wisniewski

Caio Vinicius Soares

Alon Amit

Abhishek Jain

Nikolaos Ioannou

Christopher Joel Gast

Patricia Nations

A method implements a dataset rank metric for measuring dataset relevance. Metadata is identified for a plurality of datasets. A graph structure is generated in storage. The graph structure includes a multitude of nodes connected by a multitude of edges. Each node of the multitude of nodes representing a respective dataset of a multitude of datasets, the multitude of edges connecting the multitude of nodes according to a data lineage determined from metadata of the multitude of datasets. A composite score is generated for each node of the graph. The computer processor iteratively processes the composite scores for the multitude of nodes of the graph to generate a dataset rank for each dataset. The multitude of datasets is presented in an interface, sorted according to the respective dataset rank of each dataset.

Dataset ranking based on composite score

Certain aspects of the present disclosure provide techniques for operation of a feature management platform. A feature management platform is an end-to-end platform developed to manage the full lifecycle of data features. For example, to create a feature the feature management platform can receive a processing artifact (e.g., a configuration file and code fragment) from a computing device. The processing artifact defines the feature, including the data source to retrieve event data from, when to retrieve the event data, the type of transform to apply, etc. Based on the processing artifact, the feature management system generates a processing job, which when initiated generates a vector that encapsulates the feature data. The vector is transmitted to the computing device that locally hosts a model, which generates a prediction. The prediction is transmitted to the feature management platform and can be transmitted to other computing devices, upon request.

Feature management platform

Computer systems for escrowing of selected data of an online tax return preparation application for use in executing models such as predictive models to generate tax return topic rankings, which are provided to the online tax return application and displayed to a user and are personalized in that they reflect or are based on actual tax return data of the user. Escrow system components may operate independently of the online application such that small portions of a very large amount of tax return preparation data can be retrieved (e.g., periodically) from a data store maintained by the online application, and this retrieved data is cached or stored to a different data store and analyzed relative to an escrow contract specifying types of data required to trigger model execution. Upon satisfying escrow contract requirements, a corresponding model is executed, and a topic ranking is generated and provided to the online application.

Escrow personalization system

A method and system provides a personalized question response to a tax-related question that is received from a user of an electronic tax return preparation system, according to one embodiment. The method and system include receiving user data that is associated with the user of the electronic tax return preparation system, according to one embodiment. The method and system include monitoring interactions between the user and a user interface of the electronic tax return preparation system to determine user analytics, according to one embodiment. The method and system include receiving a tax-related question having question content, according to one embodiment. The method and system include determining a question response that satisfies the tax-related question, according to one embodiment. The method and system include providing the question response to the user through the user interface, according to one embodiment.

Method and system for providing personalized responses to questions received from a user of an electronic tax return preparation system

A system and method for deploying predictive models is presented which personalizes application program functionality and user interface presentation to the consumer user. That personalization takes place within a decision engine that decouples decision models from application processes so that rapid deployment may take place. When a consumer user specific decision is required, the application program interacts with the decision model to trigger a consumer-user appropriate decision tailored to characteristics of the consumer user. New models are introduced into a decision process without requiring any application program change.

System and method for deploying predictive models

A system that generates a financial aid application. During operation, the system determines whether a person has authorized access to a tax return belonging to the person for purposes of generating a financial aid application. If so, the system retrieves the tax return for the person, extracts information about the person from the tax return, and uses the extracted information to populate one or more fields in the financial aid application.

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San Diego, CA, United States of America

Tristan Cooper Baker