US Patent Application 17723182. ESTIMATING AND PROMOTING FUTURE USER ENGAGEMENT OF APPLICATIONS simplified abstract

ESTIMATING AND PROMOTING FUTURE USER ENGAGEMENT OF APPLICATIONS

Organization Name

Microsoft Technology Licensing, LLC

Inventor(s)

Javier Hernandez Rivera of Somerville MA (US)

Mar Gonzalez Franco of Seattle WA (US)

Melanie J. Kneisel of Seattle WA (US)

Adam B. Glass of Woodinville WA (US)

Jarnail Chudge of Reading (GB)

Tiffany Liu of San Ramon CA (US)

Antonella Maselli of Terni (IT)

Amos Miller of Seattle WA (US)

ESTIMATING AND PROMOTING FUTURE USER ENGAGEMENT OF APPLICATIONS - A simplified explanation of the abstract

• This abstract for appeared for US patent application number 17723182 Titled 'ESTIMATING AND PROMOTING FUTURE USER ENGAGEMENT OF APPLICATIONS'

Simplified Explanation

The present disclosure describes a method for creating a model that can predict actions that will maintain user engagement or encourage users to reengage with an application. This model is generated by analyzing the features of the application and its users. Users are grouped based on similar features, and machine learning is used to create engagement models. These models predict whether a user will continue to engage with the application. The predictions are then used by a reengagement model to generate prompts that will increase user engagement. These prompts are based on an understanding of the preferences of the application users.

Original Abstract Submitted

Aspects of the present disclosure relate to generating an engagement model to predict actions that may have a high probability of maintaining user engagement in-application or causing a user to reengage with the application. To generate the engagement model, an approach has been developed which incorporates features analysis of the application and application users. Users may be grouped based on similar features that are used to generate machine learning engagement models. The output of an engagement model may be a prediction on whether a user will continue to engage with an application. The prediction may be provided to a reengagement model which may output prompts to help increase user engagement with the application. The prompts may be based on an understanding of application users and their preferences.