Dynamic caching based on a user's temporal and geographical location
A system for dynamically caching information includes a memory for storing location information and user information and a processor configured to periodically receive geolocation data associated with a first external device and store that geolocation data, along with a time stamp, as location information in the memory. After a predetermined period of time, the processor retrieves from the memory the location information corresponding to the predetermined time period. Machine learning is used to determine the probability of a first action. When the probability is higher than the first predetermined amount, the processor is configured to retrieve information needed to perform a first action and send the retrieved information to a second external device to store the information as cached information. This cached information is then used to perform a first action using the cached information on the second external device.
1. A system for dynamically caching information, the system comprising:
a memory configured to store location information and user information; and
a processor operably coupled to the memory and configured to:
receive, periodically, geolocation data associated with a first external device;
store the received geolocation data with a time stamp to the location information in the memory;
retrieve, after a predetermined period of time from the memory, the location information corresponding to the predetermined time period;
determine, using a machine learning operation based on the location information, a probability for a first action to be performed by a user of the first external device;
retrieve from the memory, user information needed for performing the first action, when the probability of the first action being performed is greater than a predetermined amount;
send the retrieved user information to a second external device to store as cached information; and
perform the first action using the cached information.
2. The system of claim 1 , wherein the machine learning operation uses quantum generative artificial intelligence.
3. The system of claim 1 , wherein the user information comprises an account number and one or more of: rewards, incentives, limitations, or permissions.
4. The system of claim 1 , wherein the second external device is an edge server.
5. The system of claim 1 , wherein the second external device is a point-of-sale device.
6. The system of claim 1 , wherein the machine learning operation determines the probability for the first action based on both location and time.
7. The system of claim 1 , wherein the first external device is a user's portable personal device.
8. The system of claim 1 , wherein the processor is further configured to:
determine, using the machine learning operation a second probability for a second action to be performed by a user of the first external device;
retrieve from the memory, second user information needed for performing the second action, when the probability of the second action being performed is greater than the predetermined amount; and
send the retrieved second user information to a third external device to store as cached information.
9. The system of claim 8 , wherein the second action and the first action are actions that are performed at different times at a same location.
10. The system of claim 8 , wherein the second action and the first action are actions that are performed at different locations.
11. A method for dynamically caching information, comprising:
receiving, periodically, geolocation data associated with a first external device;
storing the received geolocation data with a time stamp to location information in a memory;
retrieving, after a predetermined period of time from the memory, the location information corresponding to the predetermined time period;
determining, using a machine learning operation based on the location information, a probability for a first action to be performed by a user of the first external device;
retrieving from the memory, user information needed for performing the first action, when the probability of the first action being performed is greater than a predetermined amount;
sending the retrieved user information to a second external device to store as cached information; and
performing the first action using the cached information.
12. The method of claim 11 , wherein the machine learning operation uses quantum generative artificial intelligence.
13. The method of claim 11 , wherein the user information comprises an account number and one or more of: rewards, incentives, limitations, or permissions.
14. The method of claim 11 , further comprising:
determining, using the machine learning operation a second probability for a second action to be performed by a user of the first external device;
retrieving from the memory, second user information needed for performing the second action, when the probability of the second action being performed is greater than the predetermined amount; and
sending the retrieved second user information to a third external device to store as cached information.
15. The method of claim 14 , wherein the second action and the first action are actions that are performed at different times at a same location.
16. The method of claim 14 , wherein the second action and the first action are actions that are performed at different locations.
17. A non-transitory computer-readable medium storing instructions that when executed by a processor cause the processor to:
receive, periodically, geolocation data associated with a first external device;
store the received geolocation data with a time stamp as location information in a memory;
retrieve, after a predetermined period of time from the memory, the location information corresponding to the predetermined time period;
determine, using a machine learning operation based on the location information, a probability for a first action to be performed by a user of the first external device;
retrieve from the memory, user information needed for performing the first action, when the probability of the first action being performed is greater than a predetermined amount;
send the retrieved user information to a second external device to store as cached information; and
perform the first action using the cached information.
18. The non-transitory computer-readable medium of claim 17 , wherein the machine learning operation uses quantum generative artificial intelligence.
19. The non-transitory computer-readable medium of claim 17 , wherein the user information comprises an account number and one or more of: rewards, incentives, limitations, or permissions.
20. The non-transitory computer-readable medium of claim 17 , wherein the machine learning operation determines the probability for the first action based on both location and time.