METHODS AND SYSTEMS FOR SELECTING AN OPTIMAL SCHEDULE FOR EXPLOITING VALUE IN CERTAIN DOMAINS
Aspects of the present disclosure generally relates to a method including receiving user data and identifying at least a domain target for the at least a domain as a function of the domain-specific data. Also, the method may include generating a plurality of candidate schedules. Further, the method may include selecting an optimal user schedule from the plurality of candidate schedules. Moreover, the method may include presenting, at a remote device, the optimal user schedule to a user, and tracking, by the computing device, a user's progress with regard to the optimal user schedule.
1 . A system for developing a personalized and interactive curriculum, wherein the system comprises:
at least a processor;
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive user data from a user, wherein the user data comprises scheduling data and domain-specific data, wherein domain-specific data comprises health data;
generate a plurality of candidate schedules as a function of the at least a domain target and the scheduling data;
select an optimal user schedule from the plurality of candidate schedules;
track a user's progress with regard to the optimal user schedule, wherein tracking the user's progress comprises periodically scanning a user device for medical data;
iteratively update the optimal user schedule as a function of the user's progress; and
display an updated optimal user schedule using a remote device.
2 . The apparatus of claim 1 , wherein the plurality of candidate schedules comprises a plurality of lessons related to a domain corresponding to the domain-specific data, wherein the plurality of lessons comprises online lessons.
3 . The apparatus of claim 2 , wherein the plurality of lessons comprises exercise lessons.
4 . The apparatus of claim 2 , wherein the plurality of lessons comprises nutritional lessons.
5 . The apparatus of claim 1 , wherein generating the plurality of candidate schedules comprises:
receiving scheduling training data correlating domain-specific data to scheduling data;
training a scheduling machine-learning model as a function of the scheduling training data, wherein the scheduling machine-learning model includes a neural network, wherein the scheduling training data further comprises at least a historical domain target input and outputs at least a plurality of candidate schedules, wherein outputting the at least a plurality of candidate schedules further comprises applying weighted values to the at least a historical domain target input and correlating the weighted values of the at least a historical datum target input to adjacent layers of at least a plurality of candidate schedules; and
generating a plurality of candidate schedules as a function of the scheduling machine-learning model.
6 . The apparatus of claim 1 , wherein iteratively updating the optimal user schedule comprises:
determining objective update data as a function of the user's progress;
generating evaluation results as a function of evaluating the objective update data;
iteratively updating the optimal user schedule as a function of the evaluation results.
7 . The apparatus of claim 6 , wherein generating evaluation results comprises generating evaluation results using an evaluation machine learning model.
8 . The apparatus of claim 6 , wherein tracking the user's progress comprises sending one or more notifications as a function of the evaluation results.
9 . The apparatus of claim 1 , wherein tracking the user's progress comprises comparing a geographic location of the user to lesson location data.
10 . The apparatus of claim 1 , wherein the memory instructs the processor to generate a score associated with each candidate schedule of the plurality of candidate schedules using an objective function.
11 . A method for developing a personalized and interactive curriculum, wherein the method comprises:
receiving, using at least a processor, user data from a user, wherein the user data comprises scheduling data and domain-specific data, wherein domain-specific data comprises health data;
generating, using at least a processor, a plurality of candidate schedules as a function of the at least a domain target and the scheduling data;
selecting, using at least a processor, an optimal user schedule from the plurality of candidate schedules;
tracking, using at least a processor, a user's progress with regard to the optimal user schedule, wherein tracking the user's progress comprises periodically scanning a user device for medical data;
iteratively updating, using at least a processor, the optimal user schedule as a function of the user's progress;
displaying an updated optimal user schedule using a remote device.
12 . The method of claim 11 , wherein the plurality of candidate schedules comprises a plurality of lessons related to a domain corresponding to the domain-specific data, wherein the plurality of lessons comprises online lessons.
13 . The method of claim 12 , wherein the plurality of lessons comprises exercise lessons.
14 . The method of claim 12 , wherein the plurality of lessons comprises nutritional lessons.
15 . The method of claim 11 , wherein generating the plurality of candidate schedules comprises:
receiving scheduling training data correlating domain-specific data to scheduling data;
training a scheduling machine-learning model as a function of the scheduling training data, wherein the scheduling machine-learning model includes a neural network, wherein the scheduling training data further comprises at least a historical domain target input and outputs at least a plurality of candidate schedules, wherein outputting the at least a plurality of candidate schedules further comprises applying weighted values to the at least a historical domain target input and correlating the weighted values of the at least a historical datum target input to adjacent layers of at least a plurality of candidate schedules; and
generating a plurality of candidate schedules as a function of the scheduling machine-learning model.
16 . The method of claim 11 , wherein iteratively updating the optimal user schedule comprises:
determining objective update data as a function of the user's progress;
generating evaluation results as a function of evaluating the objective update data;
iteratively updating the optimal user schedule as a function of the evaluation results.
17 . The method of claim 16 , wherein generating evaluation results comprises generating evaluation results using an evaluation machine learning model.
18 . The method of claim 16 , wherein tracking the user's progress comprises sending one or more notifications as a function of the evaluation results.
19 . The method of claim 11 , wherein tracking the user's progress comprises comparing a geographic location of the user to lesson location data.
20 . The method of claim 11 , wherein the method further comprises generating, using the at least a processor, a score associated with each candidate schedule of the plurality of candidate schedules using an objective function.