Using machine learning system to dynamically modify device parameters
Systems for dynamically modifying one or more parameters of an event processing device are provided. In some examples, a system may receive data, such as data from a mobile device of a user. The data may include current location information of the mobile device. In some examples, additional data, may also be received. In some examples, one or more machine learning datasets may be used to determine whether a parameter of the event processing device should be modified. If so, an instruction to modify the parameter of the event processing device may be generated and executed. After modifying the parameter, additional data may be received and analyzed to determine whether a triggering event has occurred. If not, the parameter may remain in the modified state. If a triggering event has occurred, the parameter may be further modified.
1. A dynamic device parameter modification computing platform, comprising:
at least one processor;
a communication interface communicatively coupled to the at least one processor; and
memory storing computer-readable instructions that, when executed by the at least one processor, cause the dynamic device parameter modification computing platform to:
receive data from a computing device of a user;
determine, based on one or more machine learning datasets and the received data, a modification to a parameter of a device;
execute, based on the determined modification, an instruction to modify the parameter based on the determined modification, modifying the parameter including modifying the parameter from a first condition to a second condition;
receive, in real-time, subsequent data from the computing device of the user;
analyze, in real-time, the subsequent data;
determine, based on the analyzed subsequent data, whether a triggering event has occurred;
responsive to determining that the triggering event has occurred, executing an instruction to modify the parameter from the second condition to the first condition; and
responsive to determining that a triggering event has not occurred:
maintaining the second condition of the parameter;
receive second subsequent data from the computing device of the user;
determine, based on the second subsequent data and the one or more machine learning datasets, a second modification of the parameter from the second condition to a third condition different from the first condition and the second condition; and
execute an instruction to modify the parameter from the second condition to the third condition.
2. The dynamic device parameter modification computing platform of claim 1 , wherein the computing device of the user is a mobile computing device.
3. The dynamic device parameter modification computing platform of claim 2 , wherein the received data from the mobile computing device of the user is current location data received from a global positioning system (GPS) in the mobile computing device of the user.
4. The dynamic device parameter modification computing platform of claim 1 , wherein determining whether the triggering event has occurred includes determining whether a predetermined amount of time has elapsed.
5. The dynamic device parameter modification computing platform of claim 1 , further including instructions that, when executed, cause the dynamic device parameter modification computing platform to:
receive data from an internal data source; and
wherein determining, based on one or more machine learning datasets and the received data, a modification to a parameter of a device is further based on the received data from the internal data source.
6. The dynamic device parameter modification computing platform of claim 1 , further including instructions that, when executed, cause the dynamic device parameter modification computing platform to:
receive data from an external data source; and
wherein determining, based on one or more machine learning datasets and the received data, a modification to a parameter of a device is further based on the received data from the external data source.
7. A method, comprising:
at a computing platform comprising at least one processor, memory, and a communication interface:
receiving, by the at least one processor and via the communication interface, data from a computing device of a user;
determining, by the at least one processor and based on one or more machine learning datasets and the received data, a modification to a parameter of a device;
executing, by the at least one processor and based on the determined modification, an instruction to modify the parameter based on the determined modification, modifying the parameter including modifying the parameter from a first condition to a second condition;
receiving, by the at least one processor, in real-time and via the communication interface, subsequent data from the computing device of the user;
analyzing, by the at least one processor, in real-time, the subsequent data;
determining, by the at least one processor, based on the analyzed subsequent data, whether a triggering event has occurred;
responsive to determining that the triggering event has occurred, executing, by the at least one processor, an instruction to modify the parameter from the second condition to the first condition; and
responsive to determining that a triggering event has not occurred:
maintaining, by the at least one processor, the second condition of the parameter;
receiving, by the at least one processor and via the communication interface, second subsequent data from the computing device of the user;
determining, by the at least one processor and based on the second subsequent data and the one or more machine learning datasets, a second modification of the parameter from the second condition to a third condition different from the first condition and the second condition; and
executing, by the at least one processor, an instruction to modify the parameter from the second condition to the third condition.
8. The method of claim 7 , wherein the computing device of the user is a mobile computing device.
9. The method of claim 8 , wherein the received data from the mobile computing device of the user is current location data received from a global positioning system (GPS) in the mobile computing device of the user.
10. The method of claim 7 , wherein determining whether the triggering event has occurred includes determining whether a predetermined amount of time has elapsed.
11. The method of claim 7 , further including:
receiving, by the at least one processor and via the communication interface, data from an internal data source; and
wherein determining, based on one or more machine learning datasets and the received data, a modification to a parameter of a device is further based on the received data from the internal data source.
12. The method of claim 7 , further including:
receiving, by the at least one processor and via the communication interface, data from an external data source; and
wherein determining, based on one or more machine learning datasets and the received data, a modification to a parameter of a device is further based on the received data from the external data source.
13. One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, memory, and a communication interface, cause the computing platform to:
receive data from a computing device of a user;
determine, based on one or more machine learning datasets and the received data, a modification to a parameter of a device;
execute, based on the determined modification, an instruction to modify the parameter based on the determined modification, modifying the parameter including modifying the parameter from a first condition to a second condition;
receive, in real-time, subsequent data from the computing device of the user;
analyze, in real-time, the subsequent data;
determine, based on the analyzed subsequent data, whether a triggering event has occurred;
responsive to determining that the triggering event has occurred, executing an instruction to modify the parameter from the second condition to the first condition; and
responsive to determining that a triggering event has not occurred:
maintain the second condition of the parameter;
receive second subsequent data from the computing device of the user;
determine, based on the second subsequent data and the one or more machine learning datasets, a second modification of the parameter from the second condition to a third condition different from the first condition and the second condition; and
execute an instruction to modify the parameter from the second condition to the third condition.
14. The one or more non-transitory computer-readable media of claim 13 , wherein the computing device of the user is a mobile computing device.
15. The one or more non-transitory computer-readable media of claim 14 , wherein the received data from the mobile computing device of the user is current location data received from a global positioning system (GPS) in the mobile computing device of the user.
16. The one or more non-transitory computer-readable media of claim 13 , wherein determining whether the triggering event has occurred includes determining whether a predetermined amount of time has elapsed.
17. The one or more non-transitory computer-readable media of claim 13 , further including instructions that, when executed, cause the computing platform to:
receive data from an internal data source; and
wherein determining, based on one or more machine learning datasets and the received data, a modification to a parameter of a device is further based on the received data from the internal data source.
18. The one or more non-transitory computer-readable media of claim 13 , further including instructions that, when executed, cause the computing platform to:
receive data from an external data source; and
wherein determining, based on one or more machine learning datasets and the received data, a modification to a parameter of a device is further based on the received data from the external data source.