Systems And Methods for an Adaptive Application Recommender

• US 20140279787A1
• Filed: 03/15/2013
• Published: 09/18/2014
• Est. Priority Date: 03/15/2013
• Status: Abandoned Application

• Current Assignee: Ximplar Limited
• Original Assignee: Ximplar Limited
• Inventors: Cheng, Ka Kui, Lee, Pui Tong Paul
• Application Number:
  US13/836,588
• Publication Number:
  US 20140279787A1
• Time in Patent Office: Days
• US Class Current: 706/46
• CPC Class Codes: G06F 11/30 Monitoring
• CPC Class Codes: G06F 11/3438 monitoring of user actions tracking the activity of the user H04L67/535
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 11/3476 Data logging G06F11/14, G06F11/2205 take precedence
• CPC Class Codes: G06F 2201/865 Monitoring of software
• CPC Class Codes: G06F 8/00 Arrangements for software engineering testing or debugging G06F11/36; administrative, planning or organisation aspects of software project management G06Q10/06
• CPC Class Codes: G06F 8/60 Software deployment

Abstract

With the growing number of downloaded applications on devices, especially on ones with limited screen real estate, users need a quick and pain-free way to locate applications. In accordance with one or more embodiments of the present invention, a system and methods are provided for generating an application selection recommendation.

Claims

1. A method comprising:

in a processing device;

gathering behavioral data resulting from a user'"'"'s interaction with an application, wherein the behavioral data comprises temporal data associated with the application, and locality data associated with the application;

analyzing the behavioral data to identify a usage pattern in response to a predetermined selection factor; and

providing an application selection recommendation to the user in response to the usage pattern and a real time selection factor.

2. The method of claim 1, wherein the behavioral data further comprises:

a history of application launches.

3. The method of claim 1 further comprising:

storing the usage pattern.

4. The method of claim 1, wherein analyzing the behavioral data to identify the usage pattern in response to the predetermined selection factor further comprises:

analyzing the behavioral data to identify the usage pattern in response to an application based selection factor, wherein the usage pattern specifies a plurality of applications.

5. The method of claim 1, wherein analyzing the behavioral data to identify the usage pattern in response to the predetermined selection factor further comprises:

analyzing the behavioral data to identify the usage pattern in response to an location based selection factor, wherein the usage pattern specifies a plurality of applications clustered by a plurality of locations.

6. The method of claim 1, wherein analyzing the behavioral data to identify the usage pattern in response to the predetermined selection factor further comprises:

analyzing the behavioral data to identify the usage pattern in response to a time based selection factor, wherein the usage pattern specifies a plurality of applications clustered by a plurality of predetermined time slots.

7. The method of claim 1, wherein gathering the behavioral data resulting from the user'"'"'s interaction with the application further comprises:

monitoring an application request;

extracting an application identifier from the application request;

determining temporal data associated with the application identifier;

determining locality data associated with the application identifier.

8. The method of claim 1, wherein the real time selection factor comprises:

an application in use.

9. The method of claim 1, wherein the real time selection factor comprises:

a location of the processing device during the user'"'"'s interaction with the application.

10. The method of claim 1, wherein the real time selection factor comprises:

a time of day corresponding to the user'"'"'s interaction with the application.

11. The method of claim 1, wherein the real time selection factor comprises:

a day of week corresponding to the user'"'"'s interaction with the application.

12. The method of claim 1, wherein providing the application selection recommendation to the user in response to the usage pattern and the real time selection factor further comprises:

querying from the usage pattern an availability of an application in response to the real time selection factor;

acquiring the application from the usage pattern;

calculating a launch probability of the application in response to the real time selection factor and the usage pattern;

ranking the application in response to the launch probability of the application;

adding the application to the application selection recommendation;

receiving an application selection request; and

displaying the application as the application selection recommendation.

13. The method of claim 12, wherein the usage pattern is acquired from a cloud storage facility in response to the real time selection factor.

14. The method of claim 12, wherein a second usage pattern is acquired from a cloud storage facility in response to the real time selection factor, and wherein calculating the launch probability is performed in response to the real time selection factor, the usage pattern, and the second usage pattern.

15. The method of claim 12 further comprising:

assigning a weight to the real time selection factor;

calculating a weighted launch probability for the application in response to the weight and the launch probability of the application;

aggregating the weighted launch probability of the application; and

ranking the application in response to the weighted launch probability of the application.

16. The method of claim 3, wherein storing the usage pattern further comprises:

storing the usage pattern to a cloud storage facility.

17. The method of claim 16, wherein storing the usage pattern to the cloud storage facility further comprises:

saving the usage pattern to a local storage device;

queuing the usage pattern for copying to the cloud storage facility; and

communicating, through a network connection, the usage pattern from the local storage device to the cloud storage facility.

18. The method of claim 16 further comprising:

stripping personal data from the usage pattern.

21. The system of claim 18 further comprising:

a network adaptor configured to;

store the usage pattern to a cloud storage facility.

19. A system comprising:

a processor;

a memory, coupled to the processor, containing a program, which when executed by the processor is configured to;

gather behavioral data resulting from a user'"'"'s interaction with an application, wherein the behavioral data comprises temporal data associated with the application, and locality data associated with the application;

analyze the behavioral data to identify a usage pattern in response to a predetermined selection factor;

provide an application selection recommendation to the user in response to the usage pattern and a real time selection factor; and

a data storage configured to;

store the usage pattern.

20. The system of claim 19, wherein the memory is further configured to:

gather the behavioral data resulting from the user'"'"'s interaction with the application, wherein the behavioral data comprises a history of application launches.

22. A tangible non-transitory computer readable medium comprising computer readable code, that when executed by a processing device, cause the processing device to perform operations comprising:

gathering behavioral data resulting from a user'"'"'s interaction with an application, wherein the behavioral data comprises temporal data associated with the application, and locality data associated with the application;

analyzing the behavioral data to identify a usage pattern in response to a predetermined selection factor;

storing the usage pattern; and

providing an application selection recommendation to the user in response to the usage pattern and a real time selection factor.