Systems and methods of utilizing and creating opportunity gain for personal enrichment

1. A computer-implemented method of utilizing opportunity gain for personal enrichment of a user, the method implemented using a computing device having one or more processors, the method comprising:

receiving, by the one or more processors, a selection of at least one personal enrichment goal, wherein the at least one personal enrichment goal is achieved as a function of an amount of a personal commodity dedicated to the at least one personal enrichment goal;

monitoring a routine of the user, wherein the routine includes an activity, and wherein a first amount of the personal commodity is used when the user performs the activity;

determining, by the one or more processors, an average amount of the personal commodity used to perform the activity in the past;

determining an excess amount of the personal commodity generated when the user deviates from the routine, wherein the excess amount of the personal commodity is determined as a function of the average amount and the first amount of the personal commodity; and

notifying, by the one or more processors, the user that the excess amount of the personal commodity may be applied to achievement of the at least one personal enrichment goal;

wherein determining an excess amount of the personal commodity generated comprises determining deviations from a planned activity using a machine-learning module of the one or more processors;

wherein the machine-learning module is configured to receive training data and optimization parameters that define a type of value within the training data and to generate an optimization model based upon iteratively reducing the training data to identify patterns in the training data associated with changes in the optimization parameters; and

wherein the one or more processors includes a data processing module configured to generate at least one event based upon determining that near-real time data satisfies at least one rule included in the optimization model.

2. The computer-implemented method in accordance with claim 1 , wherein determining an excess amount of the personal commodity comprises determining the excess amount based upon a difference between the average amount and the first amount of the personal commodity.

3. The computer-implemented method in accordance with claim 2 further comprising determining the difference between the average amount and the first amount of the personal commodity after completion of the activity.

4. The computer-implemented method in accordance with claim 1 , wherein a saved amount of the personal commodity, equal to the first amount, is defined when the user abstains from the activity, wherein determining an excess amount of the personal commodity comprises determining the excess amount of the personal commodity based upon a difference between the average amount and the saved amount of the personal commodity.

5. The computer-implemented method in accordance with claim 4 , wherein determining the excess amount of the personal commodity comprises comparing the average amount and the saved amount of the personal commodity at a predetermined interval.

6. The computer-implemented method in accordance with claim 1 further comprising providing means for achieving the at least one personal enrichment goal to the user using the excess amount of the personal commodity.

7. The computer-implemented method in accordance with claim 6 , wherein providing means for achieving the at least one personal enrichment goal comprises launching, on a user device, an application configured to facilitate achievement of the at least one personal enrichment goal by the user.

8. The computer-implemented method in accordance with claim 7 , wherein launching an application comprises launching the application configured to provide educational material to the user.

9. The computer-implemented method in accordance with claim 7 , wherein the personal commodity is money, said method further comprising:

causing to be displayed, in the application, an excess amount of money generated by deviating from the activity, wherein the application is linked to a bank account and a deposit account associated with the user; and

receiving, via the application, authorization to transfer the excess amount of money from the bank account to the deposit account.

10. The computer-implemented method in accordance with claim 7 further comprising:

causing to be, in the application, a user-selectable link to a third party site configured to offer at least one of a product and a service associated with the at least one personal enrichment goal; and

receiving a selection of the user-selectable link such that the user is redirected to the third party site.

11. The computer-implemented method in accordance with claim 7 further comprising utilizing machine-learning technology to offer, in the application, at least one of a product and a service tailored to the user and associated with the at least one personal enrichment goal.

12. The computer-implemented method in accordance with claim 1 , wherein notifying the user comprises displaying, on a user device by the one or more processors, a graphical representation of the excess amount of the personal commodity.

13. The computer-implemented method in accordance with claim 1 , wherein monitoring a routine of the user comprises monitoring a commute of the user from a first destination to a second destination, wherein the personal commodity is time.

14. The computer-implemented method in accordance with claim 13 , wherein monitoring a commute of the user comprises accessing global positioning system (GPS) data associated with a user device.

15. The computer-implemented method in accordance with claim 1 , wherein monitoring a routine of the user comprises monitoring spending habits of the user, wherein the personal commodity is money.

16. The computer-implemented method in accordance with claim 15 , wherein monitoring spending habits of the user comprises accessing financial transaction data from at least one of a bank account and a credit card account associated with the user.

17. The computer-implemented method in accordance with claim 1 , wherein the at least one personal enrichment goal includes at least one of gaining knowledge and saving money.

18. A computer system for utilizing opportunity gain for personal enrichment of a user, the computer system comprising:

a processor; and

a non-transitory, tangible, computer-readable storage medium having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations comprising:

receiving a selection of at least one personal enrichment goal, wherein the at least one personal enrichment goal is achieved as a function of an amount of a personal commodity dedicated to the at least one personal enrichment goal;

monitoring a routine of the user, wherein the routine includes an activity, and wherein a first amount of the personal commodity is used when the user performs the activity;

determining an average amount of the personal commodity used to perform the activity in the past;

determining an excess amount of the personal commodity generated when the user deviates from the routine, wherein the excess amount of the personal commodity is determined as a function of the average amount and the first amount of the personal commodity; and

notifying the user that the excess amount of the personal commodity may be applied to achievement of the at least one personal enrichment goal;

wherein determining an excess amount of the personal commodity generated comprises determining deviations from a planned activity using a machine-learning module of the processor;

wherein the machine-learning module is configured to receive training data and optimization parameters that define a type of value within the training data and to generate an optimization model based upon iteratively reducing the training data to identify patterns in the training data associated with changes in the optimization parameters; and

wherein the processor includes a data processing module configured to generate at least one event based upon determining that near-real time data satisfies at least one rule included in the optimization model.