Abstract: A ground truth expansion system that generates an expanded set of synthetic questions and selects a targeted subset of questions for machine learning training. The machine learning may be used to train an automated inquiry system that responds to questions received from individuals about subject matter of interest. The automated inquiry system is particularly suitable for use in, for example, responding to questions raised by insured individuals about their healthcare benefits.

Abstract: A health recommendations system that collects data about multiple factors pertaining to an individual's health and uses such data to recommend contextual changes that are likely to have a positive health impact on the individual. The collected factor data is used by the system to generate a vector characterizing the health of the individual over time. Using the individual's health vector, the system generates a current health score of the individual, which characterizes whether the individual is healthy or unhealthy. By periodically assessing the health vector of the individual and generating health scores, the system also constructs a trend of the individual's health score as the individual's health varies over time. The system compares the individual's health score trend data with data reflecting the health score trend of similarly-situated people, and, based on that comparison, generates recommendations for actions or changes that the individual can take that are likely to improve the individual's health.

Abstract: A ground truth expansion system that generates an expanded set of synthetic questions and selects a targeted subset of questions for machine learning training. The machine learning may be used to train an automated inquiry system that responds to questions received from individuals about subject matter of interest. The automated inquiry system is particularly suitable for use in, for example, responding to questions raised by insured individuals about their healthcare benefits.