Intuit, Inc.

Joseph B Cessna

Massimo Mascaro

Peter Ouyang

Yao H Morin

Jonathan Lunt

Igor A Podgorny

Matthew Cannon

James Rodney Jennings

Christopher C Gielow

Christopher R Rivera

Madelaine Daianu

Sean Rowan

Frank Wisniewski

Linxia Liao

Tristan Cooper Baker

Caio Vinicius Soares

Abhishek Jain

Joel R Minton

Kenneth Grant Yocum

Christiana Mei Hui Chen

Nicholas R Johnson

Yaxian Li

Yao Morin

Systems and methods for labeling data are disclosed. An example method may be performed by one or more processors of a labeling system and include retrieving labeled data, identifying characteristics predictive of labels that would be entered for unlabeled data items having the respective characteristics based on the labeled data, training an analysis model to predict labels that would be entered for unlabeled data items, generating, for unlabeled data items, using the trained analysis model, a prediction of a label that will be entered for the respective unlabeled data item if the respective unlabeled data item is presented for labeling, selecting, based on the generated predictions, a subset of unlabeled data items to be presented for labeling, receiving labels for the subset of unlabeled data items, determining that a completion criteria associated with the trained analysis model is met, and generating labels for remaining unlabeled data items.

System and method for labeling machine learning inputs

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

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.

Systems and methods for intelligently grouping financial product users into cohesive cohorts

Systems and methods for call handling optimization of a call center are disclosed. A system is configured to: obtain a plurality of agent demand variables; obtain an indication of available agents that are associated with a plurality of skills; generate a multi-skill routing matrix depicting a routing problem from the agent demand variables and the available agents; deconstruct the multi-skill matrix into one or more band routing matrices; and identify, for each band routing matrix, a number of agents from the available agents to be used in solving the band routing matrix. Identifying the number of agents includes recursively simulating a plurality of possible routing solutions, measuring a quality metric for each simulation, and reducing the range of available agents based on the quality metric. The system may indicate the number of agents to be used for each routing sub-problem (which may be at the staff group level).

Artificial intelligence based call handling optimization

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.

Domain specific natural language understanding of customer intent in self-help

A method and system assists users to generate training sets of labeled data items for machine learning processes. The method and system receives data sets of unlabeled data items from users. The method and system presents data items from the data set for labeling by the users. The method and system analyzes data items that have been labeled by the user and selects future data items to be presented to the user based on analysis of the labeled data items.

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

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 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

Disclosed methods and systems crowdsource the detection of usability issues in user experience pages of a tax return preparation system, according to one embodiment. Testing and debugging combinations and sequences of user experience pages can be time consuming and costly, and crowdsourcing the detection of usability issues can reduce the length of time typically consumed in detecting issues in a user experience flow, according to one embodiment. The methods and system include monitoring users' navigation behavior (e.g., system access data) within user experience pages, using predictive models to detect when users are experiencing a potential issue, and providing issue resolution content to both help the users and characterize the potential issue, according to one embodiment. If the potential issue is a usability issue, the methods and systems include requesting more information about the usability issue, to facilitate resolving the usability issue for current and subsequent users, according to one embodiment.

Growing community of inventors

San Diego, CA, United States of America

Joseph B Cessna