Intuit, Inc.

Other

Christopher R Rivera

Yao H Morin

Massimo Mascaro

Joseph B Cessna

Peter Ouyang

Jonathan Lunt

Kenneth Grant Yocum

Joseph Michael Mandozzi

Christopher Aprea

A method for using piecewise forecasts involves obtaining, by a model discovery service, a plurality of models and generating, by a demand prediction service, a plurality of values for a time series variable. The plurality of values corresponding to a plurality of days to be predicted. The method further involves inputting the plurality of values for the time series variable as part of a piecewise forecast to a headcount estimation service and generating, by the headcount estimation service with the piecewise forecast, an estimated headcount from the time series variable.

Contact center call volume prediction

Systems described herein apply an ordered combination machine-learning models to identify users who are likely to abandon use of an application, predict the reasons why those users are likely to abandon, and identify intervening actions that the application can perform to reduce the probability that the users will abandon the application. A first machine-learning model determines a retention-prediction value indicating a probability that the user will complete a target action in the application before a session terminates. If the retention-prediction value satisfies a threshold condition, a second machine-learning model determines a reason why the session is likely to terminate before the user completes the target action. A third machine-learning model determines an intervention action for the application to perform to increase the probability that the user will complete the target action before the session terminates.

Machine-learning models to facilitate user retention for software applications

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

An ordered combination of machine-learning models may be used to identify users who are likely to abandon use of an application, predict the reasons why those users are likely to abandon, and identify intervening actions that the application can perform to reduce the probability that the users will abandon the application. For example, a first machine-learning model determines a retention-prediction value indicating a probability that the user will complete a target action in the application before a session terminates. If the retention-prediction value satisfies a threshold condition, a second machine-learning model determines a reason why the session is likely to terminate before the user completes the target action. A third machine-learning model determines an intervention action for the application to perform to increase the probability that the user will complete the target action before the session terminates.

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 retrofittable submerged terrarium for an aquarium comprising a bottle with a horizontal passageway tube and an upwardly extending access tube with an upper end extending above the water level. Alternatively, two bottles are attached to opposite ends of a passageway tube and an upwardly extending access tube extends above the water level that is attached either to the passageway tube or to one of the bottles. Preferably, an airway tube is provided into a bottle so that air can be circulated freely throughout the terrarium. Brackets are preferably provided to retain the apparatus near the bottom of the aquarium.

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Carlsbad, CA, United States of America

Christopher R Rivera