Since the inception of human research studies, researchers often need to interact with participants on a set schedule to collect data. While some human research is automated, most is not; which costs researchers both time and money. Usually, user-provided data collection consists of surveys administered via telephone or email. While these methods are simplest, they are tedious for the survey administrators, which could incur fatigue and potentially lead to collection mistakes. A solution to this was the creation of "chatbots". Early developments relied on mostly rule-based tactics (e.g. ELIZA), which were suitable for uniform input. However, as the complexity of interactions increases, rule-based systems begin breaking down since there exist a variety of ways for a user to express the same intention. This is especially true when tracking states within a research study (or protocol). Recently, natural language processing (NLP) models and, subsequently, virtual assistants have become increasingly more sophisticated when communicating with users. Examples of these efforts range from research studies to commercial health products. This project leverages recent advancements in conversational artificial intelligence (AI), speech-to-text, natural language understanding (NLU), and finite-state machines to automate protocols, specifically in research settings. This application must be generalized, fully customizable, and irrespective of any research study. These parameters allow new research protocols to be created quickly once envisioned. With this in mind, I present SmartState, a fully-customizable, state-driven protocol manager combined with supporting AI components to autonomously manage user data and intelligently determine the intention of users through chat and end device interactions to drive protocols.

翻译：暂无翻译