View source for PAYPAL, INC. (20240232981). EDGE COMPUTING STORAGE NODES BASED ON LOCATION AND ACTIVITIES FOR USER DATA SEPARATE FROM CLOUD COMPUTING ENVIRONMENTS simplified abstract

=EDGE COMPUTING STORAGE NODES BASED ON LOCATION AND ACTIVITIES FOR USER DATA SEPARATE FROM CLOUD COMPUTING ENVIRONMENTS=


==Organization Name==

[[:Category:PAYPAL, INC.|PAYPAL, INC.]]


[[Category:PAYPAL, INC.]]

==Inventor(s)==

[[:Category:Michael Charles Todasco of Santa Clara CA (US)|Michael Charles Todasco of Santa Clara CA (US)]][[Category:Michael Charles Todasco of Santa Clara CA (US)]]

[[:Category:Patrick Babcock of Sturbridge MA (US)|Patrick Babcock of Sturbridge MA (US)]][[Category:Patrick Babcock of Sturbridge MA (US)]]

[[:Category:Avik Chatterjee of Phoenix AZ (US)|Avik Chatterjee of Phoenix AZ (US)]][[Category:Avik Chatterjee of Phoenix AZ (US)]]

==EDGE COMPUTING STORAGE NODES BASED ON LOCATION AND ACTIVITIES FOR USER DATA SEPARATE FROM CLOUD COMPUTING ENVIRONMENTS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240232981 titled 'EDGE COMPUTING STORAGE NODES BASED ON LOCATION AND ACTIVITIES FOR USER DATA SEPARATE FROM CLOUD COMPUTING ENVIRONMENTS

The abstract describes systems and methods for edge computing storage nodes based on location and activities for user data separate from cloud computing environments. A service provider may offer additional services to users via edge computing systems and storage nodes, predicting data needs based on user profiles, activities, and machine learning models.

* Predictive loading of data to edge computing storage nodes based on user location
* Data served quicker and with less network resource consumption compared to cloud storage
* Prediction of user data needs using user profiles and machine learning models
* Service provider offering additional services through edge computing systems
* Separation of user data from cloud computing environments

Potential Applications:
- Online transaction processing
- Predictive data loading for improved user experience
- Location-based data services
- Edge computing for faster data access

Problems Solved:
- Slow data retrieval from remote cloud storage
- High network resource consumption
- Predicting user data needs accurately
- Providing additional services based on user activities

Benefits:
- Faster data access
- Improved user experience
- Reduced network resource consumption
- Enhanced predictive data loading

Commercial Applications:
Title: Edge Computing Storage Nodes for Predictive Data Loading
Description: This technology can be used in online transaction processing, location-based services, and predictive data loading applications. It has implications for improving user experience and reducing network resource consumption.

Questions about Edge Computing Storage Nodes for Predictive Data Loading:
1. How does predictive data loading to edge computing storage nodes benefit users?
Predictive data loading to edge computing storage nodes benefits users by providing faster access to data and reducing network resource consumption.

2. What are the key features of edge computing storage nodes that differentiate them from traditional cloud storage solutions?
Edge computing storage nodes are differentiated by their ability to predict user data needs based on location and activities, serving data quicker and with less network resource consumption.


==Original Abstract Submitted==

there are provided systems and methods for edge computing storage nodes based on location and activities for user data separate from cloud computing environments. a service provider, such as an online transaction processor, may provide additional services for to users via edge computing systems and edge computing storage nodes. the service may be for data that may be predictively loaded to the edge computing storage node for a particular location, where the edge computing storage node may reside more locally to the location on a network so that data may be served quicker and with less network resource consumption than providing data from a remote cloud computing storage. the data may be predicted to be needed or useful to the user at the location using a user profile for the user, monitored user activities, and/or one or more machine learning models that predict user behaviors at the location.



[[Category:G06Q30/0601]]
[[Category:G06F16/27]]
[[Category:G06F16/9035]]
[[Category:G06N20/00]]
[[Category:G06Q30/0202]]
[[Category:G06Q30/0207]]
[[Category:CPC_G06Q30/0631]]