According to some embodiments, the method further includes a step of requesting the user's follow-up input (e.g. through the app) regarding the perceived efficacy of the solution after its implementation. According to some embodiments, the follow-up may be requested 1 minute after implementation of the solution, 5 minutes after implementation of the solution, 10 minutes after implementation of the solution, half an hour after implementation of the solution, one hour after implementation of the solution, 2 hours after implementation of the solution, 5 hours after implementation of the solution, 1 day after implementation of the solution, 2 days after implementation of the solution, 1 week after implementation of the solution, or any other time frame within the range of 1 minutes and 1 week after implementation of the solution. Each possibility is a separate embodiment.

According to some embodiments, the solution algorithm may be updated, based on the user's follow-up indication. According to some embodiments, the updating may include using machine learning modules on the implemented solutions. In this way the algorithm “l(fā)earns” the user's individual preferences, thus advantageously improving the ability of the algorithm to provide solutions that, when implemented, will be found satisfactory by the user. According to some embodiments, the solution algorithm may be routinely updated based on solutions that proved to be efficient for other users.