Abstract: Techniques disclosed herein relate to glucose management recommendations based on nutritional information. In some embodiments, the techniques may involve obtaining user input that includes textual input indicating quantitative information for a food item. The techniques may also involve determining, based on the quantitative information for the food item, nutritional information for the food item, where the quantitative information for the food item is different from the nutritional information for the food item. The techniques may further involve generating, based on the nutritional information, a glucose management recommendation.

Abstract: Disclosed herein are techniques related to insulin delivery recommendations based on nutritional information. In some embodiments, the techniques may involve obtaining user input indicative of quantitative information for a food item. The techniques may further involve determining, based on the user input, nutritional information for the food item. Additionally, the techniques may involve generating, based on the nutritional information, an insulin delivery recommendation.

Abstract: An algorithm and method to provide personal recommendations for nutrition based on preferences, habits, medical and activity profiles for users, and constraints. The algorithm can also be fed and takes into account real-time feedback from the user. The method allows creating a personal nutritional schedule based on a set of constraints, which are solved using an optimization algorithm to find the diet best fitting each user. The method also includes analyzing a single user by applying various statistical techniques, enabling the algorithm to infer the user's preferences and updating of the constraints, analyzing and clustering of the general user population based on statistical principles, giving the algorithm insightful information and allowing improved performance by means of “machine-learning,” and creating a list of recommended food items/recipes to help users live a balanced, healthier lifestyle.

Abstract: Device, systems, and techniques for supporting a patient's diabetes management with food item recommendations are described in this disclosure. The device, systems, and techniques may be configured to execute a training process for a model to predict a patient nutrition state of a patient based on a predetermined food item consumed by the patient within a time period. The training process is further configured to determine an estimated biomarker level based on the predetermined food item profile having a set of nutritional attributes for the food item and the model; receive an actual biomarker level of the patient after the patient consumes the food item within the time period; and calibrate the model based on comparing the estimated biomarker level to the actual biomarker level; repeat the training process for one or more food items of a set of predetermined food items; and output the trained model.

Abstract: The present disclosure provides methods and systems for providing personalized food and health management recommendations. A dietary glucose monitor may comprise a food analysis module in communication with a glucose level monitor, where the food analysis module may be configured to (1) analyze data indicative of foods consumed by a user, and (2) determine an effect of individual foods on a glucose level of the user, based on changes to the user's glucose level as measured by the glucose level monitor.

Abstract: An algorithm and method to provide personal recommendations for nutrition based on preferences, habits, medical and activity profiles for users, and constraints. The algorithm can also be fed and takes into account real-time feedback from the user. The method allows creating a personal nutritional schedule based on a set of constraints, which are solved using an optimization algorithm to find the diet best fitting each user. The method also includes analyzing a single user by applying various statistical techniques, enabling the algorithm to infer the user's preferences and updating of the constraints, analyzing and clustering of the general user population based on statistical principles, giving the algorithm insightful information and allowing improved performance by means of “machine-learning,” and creating a list of recommended food items/recipes to help users live a balanced, healthier lifestyle.