Abstract

This document describes techniques for tailoring the initial setup and default configuration of computing devices such as smartphones and smartwatches, to individual users. The proposed computing device personalization engine utilizes a multi-faceted analysis of user data, environmental context, and device usage patterns, collectively referred to as contextual signals, to derive a set of abstract dimensions and scores that encapsulate user preferences. In some instances, the personalization engine uses machine learning models to derive the abstract dimensions and scores. The set of abstract dimensions and scores are translated into a predefined set of default Enum values that are indicative of a type of user who is setting up the computing device and a corresponding set of default configuration parameters for that type of user. The Enum values may be communicated to a smartwatch Original Equipment Manufacturer (OEM) and used to configure a highly personalized default experience during computing device setup without sharing any user-specific data with the OEM.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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