  Occupation information can be utilized by digital assistants to provide
occupation-specific personalized task support, including interruption
management, task planning, and recommendations. Prior research in the digital
workplace assistant domain requires users to input their occupation information
for effective support. However, as many individuals switch between multiple
occupations daily, current solutions falter without continuous user input. To
address this, this study introduces WorkR, a framework that leverages passive
sensing to capture pervasive signals from various task activities, addressing
three challenges: the lack of a passive sensing architecture, personalization
of occupation characteristics, and discovering latent relationships among
occupation variables. We argue that signals from application usage, movements,
social interactions, and the environment can inform a user's occupation. WorkR
uses a Variational Autoencoder (VAE) to derive latent features for training
models to infer occupations. Our experiments with an anonymized, context-rich
activity and task log dataset demonstrate that our models can accurately infer
occupations with more than 91% accuracy across six ISO occupation categories.
