Systems and methods for automated insertion of supplemental content into a virtual environment using a machine learning model

Abstract

Insertion of supplemental content into a virtual environment is automated using a machine learning model. The machine learning model is trained to calculate a confidence value that a candidate virtual object fits into a virtual environment based on an input that includes a candidate virtual object, a list of persistent virtual objects, and a list of temporary virtual objects. The machine learning model is trained using the persistent and temporary objects displayed in the current virtual environment until it predicts that a selected virtual object fits into the current virtual environment. The trained machine learning model is then used to select a virtual object comprising supplemental content to be inserted as a new virtual object in the virtual environment.