Abstract: Methods and systems for generating content recommendations using AI models are disclosed herein. In some embodiments, the method includes receiving user input. The method includes converting the user input into a high-dimensional embedding using one or more AI models. The method also includes performing a hierarchical search using the high-dimensional embedding to retrieve and refine a search result of recommended items and presenting the recommended items to a user.

Abstract: A method, comprises: determining, by one or more computer processing units, a plurality of combinations based on a plurality of ingredients; training a neural network to determine a plurality of optimized weight values for a respective combination of the plurality of combinations for a user based on a plurality of expected blood chemistry values corresponding to the user and a plurality of standard deviation values corresponding to the user, wherein the optimized weight values correspond to neural network probability weightings with iterative feedback from one or more biological samples data from the user; determining, by the one or more computer processing units, a plurality of optimized combinations based on the plurality of optimized weight values, wherein the plurality of optimized combinations is a subset of the plurality of combinations; and providing data corresponding to at least one or more combinations of the plurality of optimized combinations.

Abstract: A computer implemented method for use in conjunction with a computing device, system, network, and cloud with touch screen two dimension display or augmented/mixed reality three dimension display comprising: obtaining, analyzing and detecting user blood and saliva chemistry data and mapping the blood and saliva data into a database associated with a specific user, applying the data with optimization equations and mapping equations to food chemistry such that a user may order food and beverage from a food/beverage distribution point or have food/beverage delivered to the user which has been specifically optimized for their specific blood characteristic target ranges. The method and system uses recursive techniques and neural networks to learn how to optimize food and beverage nutrient efficiency into the users blood chemistry.