BACKGROUND

The creation of content for augmented reality (AR) devices currently suffers from various shortcomings. In particular, current content creation methods lack user friendly authoring tools, requiring the content creator to write complex code. This shortcoming is especially apparent in the creation of procedural content, content that offers some measure of guidance delivered via the functions of the AR device that directs the user through the steps of a procedure to which the user adheres rigidly or loosely, whose execution also requires actions by the operator requiring use of the operator's hands, which calls for the use of the various additional functions of the AR device to retrieve relevant information and may require deposition of new information in in appropriate digital repositories. Therefore, it is obvious a need exists for an AR content creation system that is better supports all levels of content creators, from those without a programming background to those who are experienced in content creation.

BRIEF SUMMARY

Disclosed herein are systems to facilitate the use of augmented reality (AR) devices to assist laboratory and other workers in the performance of laboratory and other work procedures normally requiring guidance. The systems utilize specific and intuitive controlled—vocabulary high level procedural languages, dedicated content generation environments, and other means including specific user interface elements designed to enable better human performance.

In the context of biomedical R&D, systems aiding generation of procedural content may aid laboratory training, performance of new procedures with which the investigator is unfamiliar, single person (autonomous) work within the lab, collaborative interaction with other lab workers, operation of instrumentation, and other activities for which guidance in performing procedures is useful.

Certain aspects of the functionality of the AR device such as object classification and object tracking may be carried out by a neural network, a combination of neural networks that may include, but are not limited to, convolutional neural networks (CNNs), Recurrent neural networks (RNNs), Generative adversarial networks (GANs), etc.

