Abstract: A system for biologically determined artificial intelligence selection guidance includes a computing device designed and configured to receive at least a biological extraction and an item descriptor from a user, generate, using a classification algorithm and a plurality of past extractions, a user classifier matching user data to user sets, identify, using the classifier and the element user data, a user set identifier matching the user, produce a selection guidance using the user set identifier and the item category identifier, and provide the selection guidance to the user.

Abstract: A system for automated analysis of behavior modification data. The system includes at least a server. The system includes a receiving module operating on the at least a server designed and configured to receive at least a request for a behavior modification and extract at least an expert quality as a function of the at least a request for a behavior modification. The system includes an expert module operating on the at least a server designed and configured to generate at least an expert list as a function of the at least an expert quality and the at least a request for a behavior modification, receive at least a user input selecting at least a selected expert as a function of the at least an expert list, generate at least a request the selected expert and transmit the at least a request for a behavior modification to the selected expert.

Abstract: A system for generating a genotypic causal model of a disease state includes a computing device that generates a causal graph containing genotypic causal nodes and connected symptomatic causal nodes, which contains causal paths from gene combinations to symptomatic datums. Genotypic causal nodes and/or connected symptomatic causal nodes may be generated by feature learning algorithms from training data.

Abstract: In an aspect, a system for generating lifestyle change recommendations based on biological extractions includes a computing device designed and configured for receiving a biological extraction pertaining to a user generating, using a first machine-learning process, a plurality of lifestyle intervention combinations as a function of the biological extraction, assigning, to each lifestyle intervention combination of the plurality of lifestyle intervention combinations, a degree of projected user adherence to the lifestyle intervention combination, wherein assigning further comprises performing a second machine learning process, and selecting, from the plurality of lifestyle intervention combinations, a lifestyle intervention combination as a function of the degree of projected user adherence of the selected lifestyle intervention combination.

Abstract: A system for identifying compatible meal options. The system including a processor configured to receive a user biological marker. The processor may also be configured to determine a food tolerance score as a function of the user biological marker. The processor may be further configured to generate a food tolerance instruction set as a function of the food tolerance score. The processor may be configured to receive a geofence, where the geofence includes a predetermined geographic area selected by the user and identify statistical makeup data of a population as a function of the geofence. The processor may then be configured to generate a micronutrient band as a function of the statistical makeup data and generate alimentary data as function of the micronutrient band. One or more meal options may be identified as a function of the food tolerance instruction set and the alimentary data.

Abstract: A system for path selection using vehicle route guidance includes computing device configured to receive a plurality of requests for a plurality of alimentary combinations and a plurality of destinations, wherein each request specifies an alimentary combination of the plurality of alimentary combinations to be assembled by at least an alimentary provider and a destination of the plurality of destinations, compute a projected nutritionally guided order volume using a first machine-learning process, determine a plurality of assembly times, the plurality of assembly times including an assembly time for each alimentary combination, as a function of the nutritionally guided order volume, generate a plurality of predicted routes as a function of the determined assembly times, and pair a predicted route of the plurality of predicted routes with a courier, wherein pairing further pairing, with the courier, the predicted route that optimizes an objective function.

Abstract: A system for classification of scholastic works includes a computing device configured to receive a first scholastic work, identify an author and a category of the first scholastic work, determine at least a work theme by receiving theme training data, the theme training data including a plurality of entries, each entry including a training textual element and a correlated theme, training a theme classifier as a function of the training data, and determining the at least a work theme as a function of the plurality of textual elements and the theme classifier, calculate a reliability quantifier as a function of the at least a theme, the author, and the category, select the scholastic work as a function of the reliability quantifier, derive, from the scholastic work, at least a correlation between a dietary practice and alleviation of a disease state, and store the at least a correlation in an expert database.

Abstract: An artificial intelligence system for behavioral pattern matching and language generation includes at least a server. The system includes a behavior modification module operating on the at least a server, wherein the behavior modification module is designed and configured to receive at least a request for a behavior modification and generate a behavior modification model as a function of the at least a request for behavior modification. The system includes an artificial intelligence advisor operating on the at least a server, wherein the artificial intelligence advisor is configured to receive at least a user input from a user client device, generate at least a textual output using the behavior modification model and the at least a user input, and transmit the at least a textual output to the user client device.

Abstract: A system for using artificial intelligence to select a compatible element. The system includes at least a server wherein the at least a server is configured to receive training data. The at least a server is configured to receive at least a biological extraction from a user. The at least a server is configured to receive at least a datum of user activity data. The at least a server is configured to select at least a compatible element as a function of the training data, the at least a biological extraction, and the at least a user activity data. The at least a server is configured to transmit the at least a compatible element to a user client device.

Abstract: A system for generating a refreshment instruction set based on individual preferences, the system including a computing device the computing device configured to receive, from a remote device, a user selection relating to nourishment; identify, an alimentary style relating to the user selection; locate, a plurality of recipes relating to the alimentary style; identify a user location within a geofence and generate, a refreshment instruction set within the geofence, wherein the refreshment instruction set further comprises a plurality of recommended refreshments using the plurality of recipes.

Abstract: A system for initiating object transfer, the system comprising a computing device configured to receive a plurality of object transfer requests, generate, using the plurality of object transfer requests, a transfer apparatus interaction platform for a plurality of transfer apparatuses to proffer at least an object transfer request, determine transfer apparatus interaction data via the transfer apparatus interaction platform, wherein transfer apparatus interaction data comprises a proffer of an object transfer request, a proposed window of time for object transfer, and an object transfer compatibility metric, select the proffer from a plurality of proffers from the plurality of transfer apparatuses, wherein selecting comprises calculating a difference between the plurality of object transfer compatibility metrics associated with the plurality of proffers using a pairwise operation, and provide the transfer apparatus corresponding to the selected proffer the object transfer request.

Abstract: A system for generating a vibrant compatibility plan using artificial intelligence. The system includes at least a server wherein the at least a server is designed and configured to receive at least a composition datum from a user client device wherein the at least a composition datum includes at least an element of user body data and at least an element of desired dietary state data. At least a server is configured to select at least a correlated dataset. At least a server is configured to create at least an unsupervised machine-learning model including at least a hierarchical clustering model to output at least a compatible food element. At least a server is configured to generate at least a vibrant compatibility plan wherein the at least a vibrant compatibility plan further comprises a plurality of compatible food elements each containing at least a food element compatibility index value score as a function of the at least a hierarchical clustering model.

Abstract: A system for customizing treatments. The system includes a computing device configured to record a user biological extraction containing an element of user physiological data. The computing device is configured to receive condition state training data and generate a condition state model utilizing a first machine-learning algorithm. The computing device is configured to calculate a condition state label using the condition state model. The computing device is configured to select a treatment model utilizing the condition state label. The computing device is configured to generate a treatment model and output a plurality of treatments utilizing the treatment model.

Abstract: A system for a configurable device environment, the system comprising a computing device configured to receive remote data corresponding to a subject and a plurality of signals from at least a sensor proximate to the subject, retrieve a biometric profile of the subject, identify a pattern of accessory device states for a plurality of accessory devices, wherein identifying includes determining a coordinated state change for a group of accessory devices of the plurality of accessory devices as a function of the remote data and the biometric profile and identifying the pattern of accessory device states as a function of the coordinated state change, determine an automation rule for the group of accessory devices as a function of the pattern of accessory device states, and transmit, to the group of accessory devices, the automation rule.

Abstract: In an aspect, a system for generating a cardiovascular disease nourishment program includes a computing device configured to receive a cardiovascular sample relating to a user, generate a cardiovascular parameter as a function of the cardiovascular disease sample, determine a cardiovascular profile as a function of the a cardiovascular parameter wherein the cardiovascular profile includes a numerical cardiovascular health score correlated to the cardiovascular parameter and an atherosclerosis indicator correlated to the cardiovascular parameter, identify a nutrition element as a function of the cardiovascular profile, wherein identifying comprises obtaining a nutrient composition correlated to a nutrition element, determining a nourishment score as a function of the effect of the nutrition element on the cardiovascular profile, and identifying a nutrition element as a function of the nourishment score and nutrition element machine-learning model, and generate a cardiovascular disease nourishment program as a

Abstract: A method for access to a digital content distribution platform includes receiving a request for initial access of a user and granting access to the digital content distribution platform based upon the request for initial access. Granting access includes providing the user with a line-of-credit configured to renew at least a portion of the line of credit when a debt accrued in the line-of-credit is satisfied. The method includes receiving a selection of a digital content file and providing the user with access to the selected digital content file in exchange for the payment from the line-of-credit. The method includes incorporating an interactive advertisement into the selected digital content file according to a parameter associated with the user with the interactive advertisement configured to receive an interaction. The method includes renewing, in response to the interaction, at least a portion of the line of credit.

Abstract: A system for a generating an alimentary element prediction machine-learning model, the system comprising a computing device configured to provide, to a user, a plurality of compatible alimentary elements as a function of user biochemistry, receive training data relating a plurality of temporally preceding alimentary elements as a function of the plurality of compatible alimentary elements presented to a user, train, using a machine-learning process, a computer model as a function of the user-selection training data to predict user-selectable alimentary elements, generate an alimentary profile as a function of the computer model, receive a user input for an alimentary element, and present, as a function of the user input, the alimentary element as a function of the alimentary profile.

Abstract: A method of path selection using vehicle route guidance is disclosed. The method receives a plurality of requests including a plurality of alimentary combinations and a plurality of destinations; computes a projected nutritionally guided order volume as a function of a first machine-learning process; determines a plurality of assembly times, the plurality of assembly times including an assembly time for each alimentary combination as a function of the nutritionally guided order volume; selects a runner route from a plurality of runner routes, for the delivering of at least one alimentary combination of the plurality of alimentary combinations as a function of the plurality of assembly times; generates a plurality of predicted routes as a function of a proximity of the plurality of destinations to the aggregation depot; and pairing with the courier, the predicted route that optimizes the objective function. A system of path selection is also disclosed.

Abstract: A system of determining an alimentary preparation provider is disclosed. The system comprises a computing device. The computing device is configured to receive input from a user device at a current geographical location. The input comprises an alimentary request and a plurality of request parameters. The computing device generates a plurality of alimentary preparation providers as a function of the plurality of request parameters. A request parameter classifier is generated by the computing device. The computing device classifies, by the request parameter classifier, the plurality of request parameters and the plurality of alimentary preparation providers. The classification outputs an ordered list of request parameters. The computing device assigns the alimentary request to a first alimentary preparation provider as a function of an ordered list of request parameters. A method for determining an alimentary preparation provider is disclosed.

Abstract: A system for nutritional recommendation using artificial intelligence analysis of immune impacts includes a computing device designed and configured to receive a test result detecting an effect of at least an aliment on at least a biomarker, determine an immune system impact of the at least an aliment as a function of the at least a biomarker using a machine-learning process, the machine-learning process trained using a first training set relating biomarker levels to immune system function, generate a nutritional recommendation using the determined immune system impact, and provide the nutritional recommendation to the user.