17948942. APP USAGE MODELS WITH PRIVACY PROTECTION simplified abstract (MICROSOFT TECHNOLOGY LICENSING, LLC)

APP USAGE MODELS WITH PRIVACY PROTECTION

Organization Name

MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor(s)

Dhruv Joshi of Kirkland WA (US)

David William Brown of Kirkland WA (US)

Dolly Sobhani of Seattle WA (US)

Brian Eugene Kihneman of Bellevue WA (US)

APP USAGE MODELS WITH PRIVACY PROTECTION - A simplified explanation of the abstract

This abstract first appeared for US patent application 17948942 titled 'APP USAGE MODELS WITH PRIVACY PROTECTION

Simplified Explanation

The patent application describes a method, system, and computer program for generating a usage model to predict user commands in an app. Here is a simplified explanation of the abstract:

• Receiving model information from client devices
• Training a machine-learning program with app usage data on each client device to obtain a model
• Generating synthetic data using the models from client devices
• Training a global model with the synthetic data to predict the next user command in the app
• Transmitting the global model information to a client device to provide command options based on predictions

Potential Applications

This technology could be applied in various fields such as mobile app development, user experience design, and predictive analytics.

Problems Solved

This technology helps in improving user experience by predicting user commands, reducing the time taken to input commands, and enhancing app usability.

Benefits

The benefits of this technology include increased user engagement, improved app performance, personalized user experiences, and enhanced app recommendations.

Potential Commercial Applications

The potential commercial applications of this technology could include mobile app development companies, app designers, predictive analytics firms, and companies looking to enhance user interactions with their apps.

Possible Prior Art

One possible prior art for this technology could be predictive text input systems used in smartphones and other devices. These systems predict the next word a user is likely to type based on context and previous input.

How does this technology handle user privacy and data security concerns?

The patent application does not provide details on how user privacy and data security are addressed in the system. It would be important to understand how user data is collected, stored, and used to ensure compliance with privacy regulations.

What is the accuracy rate of the predictions made by the global model?

The patent application does not mention the accuracy rate of the predictions generated by the global model. It would be crucial to know the reliability of the predictions to assess the effectiveness of the technology.

Original Abstract Submitted

Methods, systems, and computer programs are presented for generating a usage model for predicting user commands in an app. One method includes receiving model information from client devices. The model is obtained at each client device by training a machine-learning program with app usage data. The server generates synthetic data using the models from the client devices. A machine-learning program is trained using the synthetic data to obtain a global model, which receives as input information about recent commands entered on the app and generates an output with a prediction for the next command expected to be received by the app. The information of the global model is transmitted to a first client device, and the app provides at least one command option in the app user interface based on a prediction, generated by the global model, of the next command expected.