Abstract: Augmented reality systems and methods for a decision augmentation system are provided. An example augmented reality system includes a ruleset generator system that creates rulesets based on empirical studies and a personalized program generation system that applies individualized data of a user to create a personalized program for the user based on one or more of the rulesets. In some instances a passive and active feedback system provides empirical results of actions and behaviors of the user and a multivariate causation system selectively adjusts the rulesets based on the empirical results of the actions and behaviors of the user. In various instances, a contextual filtering and adherence scoring system dynamically selects individualized suggestions according to context of the user and the personalized program for the user and provides augmented reality information overlays of the individualized suggestions.

Abstract: Augmented reality systems and methods for a decision augmentation system are provided. An example augmented reality system includes a ruleset generator system that creates rulesets based on empirical studies and a personalized program generation system that applies individualized data of a user to create a personalized program for the user based on one or more of the rulesets. In some instances a passive and active feedback system provides empirical results of actions and behaviors of the user and a multivariate causation system selectively adjusts the rulesets based on the empirical results of the actions and behaviors of the user. In various instances, a contextual filtering and adherence scoring system dynamically selects individualized suggestions according to context of the user and the personalized program for the user and provides augmented reality information overlays of the individualized suggestions.

Abstract: Augmented reality and blockchain technology for decision augmentation systems and methods using contextual filtering and personalized program generation are provided herein. An example method includes receiving any of an image, a video stream, and contextual data from a mobile device camera, evaluating any of the image, the video stream, and the contextual data for target food or beverage content by determining ingredient and nutritional components of the target food or beverage content, and applying an augmented reality overlay to the target food or beverage content based on the ingredient and nutritional components.

Abstract: Augmented reality and blockchain technology for decision augmentation systems and methods using contextual filtering and personalized program generation are provided herein. An example method includes receiving any of an image, a video stream, and contextual data from a mobile device camera, evaluating any of the image, the video stream, and the contextual data for target food or beverage content by determining ingredient and nutritional components of the target food or beverage content, and applying an augmented reality overlay to the target food or beverage content based on the ingredient and nutritional components.

Abstract: Augmented reality systems and methods for a decision augmentation system are provided. An example augmented reality system includes a ruleset generator system that creates rulesets based on empirical studies and a personalized program generation system that applies individualized data of a user to create a personalized program for the user based on one or more of the rulesets. In some instances a passive and active feedback system provides empirical results of actions and behaviors of the user and a multivariate causation system selectively adjusts the rulesets based on the empirical results of the actions and behaviors of the user. In various instances, a contextual filtering and adherence scoring system dynamically selects individualized suggestions according to context of the user and the personalized program for the user and provides augmented reality information overlays of the individualized suggestions.

Abstract: Provided herein are exemplary methods and systems that include a deep learning ingredient and nutrient identification system that can learn from websites, databases, ontologies, recipes, food lists and restaurant menu items to identify ingredients, ingredient quantity, nutrient composition and diet adherence of different products, recipes or restaurant menu items, a propagation algorithm that can enhance or correct ingredient lists, ingredient quantity, nutrient composition and diet adherence for similar items in the food or activity databases, an adaptive ontology that can be learned from the internet and can be encoded to aid the machine learning or artificial intelligence algorithms to better learn the relationships between different food elements and groupings; and a contextual filtering and adherence scoring system that identifies and selects recipes, food products, supplements, medications and restaurant menu items according to a personalized plan.