Predictive analysis of computing patterns for preloaded data to reduce processing downtime

• US 10,264,050 B2
• Filed: 10/03/2016
• Issued: 04/16/2019
• Est. Priority Date: 10/03/2016
• Status: Active Grant

• Current Assignee: PayPal, Inc. (PayPal Holdings, Inc.)
• Original Assignee: PayPal, Inc. (PayPal Holdings, Inc.)
• Inventors: Todasco, Michael Charles, Tian, Cheng
• Primary Examiner(s): Bengzon, Greg C
• Application Number:
  US15/284,333
• Publication Number:
  US 20180097905A1
• Time in Patent Office: 925 Days
• CPC Class Codes: H04L 43/0811 by checking connectivity
• CPC Class Codes: H04L 65/612 for unicast
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 67/5681 Pre-fetching or pre-delivering data based on network characteristics
• CPC Class Codes: H04W 4/21 for social networking applications

Abstract

There are provided systems and methods for predictive analysis of computing patterns for preloaded data to reduce processing downtime. A user may utilize a computing device to interact with a service provider over a network connection, for example, to utilize one or more services or processes of the service provider. Based on the user'"'"'s actions and/or historical data for the user, the service provider may determine potentially required data and/or processes of the user. The service provider may preload the data or processes, for example, by storing data to the communication device'"'"'s cache or by executing some of the processes. The service provider may preload data to the communication device while the communication device has available bandwidth or network connectivity so that the data is available in an offline environment. Processes may be executed before requirement by the user during times of low processing load.

Claims

1. A transaction processor system comprising:

a non-transitory memory; and

one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the transaction processor system to perform operations comprising;

receiving at least one user action by a user through a website or a mobile application using a device, wherein the at least one user action indicates an item of interest for purchase by the user;

determining a potential future request for a transaction checkout flow with the transaction processor system for the item based on user historical information for the user;

determining that a processing load on the transaction processor system to execute one or more computing processes is below a minimum threshold;

in response to determining that the processing load is below the minimum threshold, retrieving item data for the item and user data for the user required for processing a transaction for the item in the transaction checkout flow prior to incurring additional processing load on the transaction processor system;

generating dynamic data for the transaction checkout flow based on the potential future request, the item data, and the user data, wherein the dynamic data depends on input to the transaction checkout flow, and wherein the dynamic data is separate from static data with a content delivery network; and

storing the dynamic data for a subsequent request by the device of the user.

2. The transaction processor system of claim 1, wherein the user historical information comprises information for a plurality of users, and wherein the user historical information further comprises past user actions by the user and the plurality of users conducted through the website or the mobile application.

3. The transaction processor system of claim 2, wherein the potential future request is determined based on a subsequent action conducted by the plurality of users through the website or the mobile application after previously performing a matching action to the at least one user action.

4. The transaction processor system of claim 2, wherein determining the potential future request comprises:

determining one of the plurality of users previously performed a matching action to the at least one user action based on the past user actions; and

determining the potential future request based on a subsequent action conducted by the one of the plurality of users after previously performing the matching action.

5. The transaction processor system of claim 1, wherein the user historical information comprises at least one of a purchase history for the user, past actions on the website or the mobile application by the user, past items of interest of the user, a shopping list of the user, or a wish list of the user.

6. The transaction processor system of claim 1, wherein the transaction checkout flow for the transaction processor system is integrated with the website or the mobile application for processing a sale of the item of interest.

7. The transaction processor system of claim 6, wherein processes for providing the dynamic data in the transaction checkout flow are pre-executed, wherein the dynamic data is associated with a token provided to the device, and wherein the dynamic data is inserted to the transaction checkout flow with the static data on receipt of the token from the device.

8. The transaction processor system of claim 7, wherein generating the dynamic data comprises performing API calls between an item sales application of a merchant and the transaction checkout flow while the processing load is below the minimum threshold.

9. The transaction processor system of claim 7, wherein the operations further comprise:

receiving the token from the device; and

providing the dynamic data to the transaction checkout flow on a page of the website or an interface of the mobile application with the static data.

10. The transaction processor system of claim 7, wherein the dynamic data is stored on one of a cache of the transaction processor system or a cache server associated with the transaction processor system.

11. The transaction processor system of claim 7, wherein the dynamic data comprises at least one of user information for the user, financial information for the user, merchant information, or item information for the item of interest.

12. A method comprising:

receiving user information for a user using a device of the user, wherein the user information comprises at least a location of the user,

determining a predictive requirement for dynamic data required by a transaction checkout flow for processing a transaction for an item through an application executing on the device at a future time based on the location and past user history associated with the application, wherein the application of the device requires a network connection to process the dynamic data in the transaction checkout flow at the future time, wherein the dynamic data depends on input to the transaction checkout flow, and wherein the dynamic data is separate from static online content with a content delivery network;

determining a network connectivity rating for the network connection at the future time;

determining that the network connectivity rating at the future time is below a minimum required rating;

in response to determining that the network connectivity rating is below the minimum required rating, retrieving item data for the item and user data for the user required for processing the transaction through the transaction checkout flow;

generating the dynamic data for the transaction checkout flow based on the item data and the user data; and

loading, using the network connection, the dynamic data to a cache of the device prior to the future time.

13. The method of claim 12, wherein the network connectivity rating at the future time indicates no network connectivity for the network connection at the future time.

14. The method of claim 13, wherein the dynamic data is associated with static online content comprising one of map data for a mapping feature of the application, social networking data displayable using the application, microblogging data displayable using the application, audio data output using the application, video data output using the application, audiovisual data output using the application, or video game data processed using the application, and wherein the static online content is further loaded to the cache prior to the future time.

15. The method of claim 14, wherein the receiving the user information further comprises accessing historical information for network usage for a plurality of users at the location of a future location for the user associated with the future time, and wherein the determining that the network connectivity rating at the future time is below the minimum required rating further uses the historical information.

16. The method of claim 14, wherein the receiving the user information further comprises accessing carrier coverage data for the location of a future location for the user associated with the future time, and wherein the determining that the network connectivity rating at the future time is below the minimum required rating further uses the carrier coverage data.

17. A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:

receiving input to a checkout flow through one of a website or a mobile application using a device, wherein the input comprises an item for purchase through the one of the website or the mobile application, and wherein the checkout flow is provided by a transaction processor associated with the machine;

determining that a processing load of the transaction processor is below a minimum threshold;

determining account data for an account utilized by the device with the transaction processor, wherein the account data is required for processing the checkout flow for the item;

generating dynamic data for the checkout flow based on the input and the account data prior to incurring additional processing load on the transaction processor, wherein the dynamic data is variable based on at least one of the input or the account data, and wherein the dynamic data is separate from static data with a content delivery network; and

storing the dynamic data for the checkout flow.

18. The non-transitory machine-readable medium of claim 17, wherein the item comprises digital media for playback by the device, and wherein the digital media is provided by a content delivery network.

19. The non-transitory machine-readable medium of claim 18, wherein the digital media requires a purchase through the checkout flow for the playback of the digital media, wherein the digital media is protected from playback or copying without the purchase, and wherein the operations further comprise:

processing the checkout flow using the dynamic data to complete the purchase of the digital media;

in response to the determining that a processing load of the transaction processor is below a minimum threshold, storing the digital media on the device; and

unlocking the digital media for at least one of playback or copying on the device.

20. The non-transitory machine-readable medium of claim 19, wherein digital media content comprises one of movie data, music data, television show data, video game data, or virtual reality data.