Application analytics reporting

• US 9,317,415 B2
• Filed: 06/03/2013
• Issued: 04/19/2016
• Est. Priority Date: 06/03/2013
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Chu, Maurice Kyojin
• Primary Examiner(s): Dao, Thuy
• Assistant Examiner(s): Rivera, Anibal
• Application Number:
  US13/908,811
• Publication Number:
  US 20140359584A1
• Time in Patent Office: 1,051 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 11/3072 where the reporting involves data filtering, e.g. pattern matching, time or event triggered, adaptive or policy-based reporting
• CPC Class Codes: G06F 11/3438 monitoring of user actions tracking the activity of the user H04L67/535
• CPC Class Codes: G06F 11/3608 using formal methods, e.g. model checking, abstract interpretation theorem proving G06N5/013
• CPC Class Codes: G06F 11/3616 using software metrics
• CPC Class Codes: G06F 11/3692 for test results analysis
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 8/60 Software deployment
• CPC Class Codes: G06F 8/70 Software maintenance or management
• CPC Class Codes: G06F 9/5072 Grid computing
• CPC Class Codes: G06Q 10/00 Administration; Management

Abstract

Systems and methods for application analytics reporting include comparing metrics regarding the use of the application to a usage model for the application. The usage model indicates an expected set of states of the application and transitions between the states during execution of the application. A determined difference between the metrics and the expected states and transitions indicated by the usage model that exceeds a predetermined threshold is provided as analytics data.

Claims

1. A method of providing analytics data for an application comprising:

generating, by a processor, metrics regarding use of the application on a device, the metrics including data indicative of a plurality of states of the application and transitions between the states during execution of the application;

comparing the metrics to a usage model for the application that indicates an expected set of states of the application and transitions between the states during execution of the application;

determining a first portion of the metrics for which a difference between the first portion of the metrics and the expected states and transitions indicated by the usage model exceeds a predetermined threshold and a second portion of the metrics for which a difference between the second portion of the metrics and the expected states and transitions indicated by the usage model is within the predetermined threshold;

choosing the first portion of the metrics to be reported to an external device responsive to determining the difference between the first portion of the metrics and the expected states and transitions indicated by the usage model exceeds the predetermined threshold; and

transmitting to the external device the first portion of the metrics in response to a determination that the generated metrics correspond to at least a predetermined length of time.

2. The method of claim 1, further comprising:

constructing the model using a history of states and state transitions for the application.

3. The method of claim 2, wherein constructing the model comprises generating a Markov chain using the history of states and state transitions for the application.

4. The method of claim 2, wherein the history of states and state transitions for the application comprise data generated by a plurality of devices executing the application.

5. The method of claim 1, further comprising:

transmitting the first portion of the generated metrics in response to a determination that the device has established a network connection.

6. The method of claim 1, wherein the transitions between the states of the application correspond to operations performed by the application in response to input from a user interface device.

7. The method of claim 1, wherein determining a difference between the metrics and the expected states and transitions indicated by the usage model comprises:

determining whether the metrics exceed one or more probability thresholds associated with the expected states and transitions.

8. A system for providing analytics data for an application comprising one or more processors configured to:

generate metrics regarding use of the application on a device, the metrics including data indicative of a plurality of states of the application and transitions between the states during execution of the application;

compare the metrics to a usage model for the application that indicates an expected set of states of the application and transitions between the states during execution of the application;

determine a first portion of the metrics for which a difference between the first portion of the metrics and the expected states and transitions indicated by the usage model exceeds a predetermined threshold and a second portion of the metrics for which a difference between the second portion of the metrics and the expected states and transitions indicated by the usage model is within the predetermined threshold;

choose the first portion of the metrics to be reported to an external device responsive to determining the difference between the first portion of the metrics and the expected states and transitions indicated by the usage model exceeds the predetermined threshold; and

transmit to the external device the first portion of the metrics in response to a determination that the generated metrics correspond to at least a predetermined length of time.

9. The system of claim 8, wherein the one or more processors are configured to:

construct the model using a history of states and state transitions for the application.

10. The system of claim 9, wherein constructing the model comprises generating a Markov chain using the history of states and state transitions for the application.

11. The system of claim 9, wherein the history of states and state transitions for the application comprise data generated by a plurality of devices executing the application.

12. The system of claim 8, wherein the one or more processors are configured to:

transmitting the first portion of the generated metrics in response to a determination that the device has established a network connection.

13. The system of claim 8, wherein the transitions between the states of the application correspond to operations performed by the application in response to input from a user interface device.

14. The system of claim 8, wherein the one or more processors are configured to determine a difference between the metrics and the expected states and transitions indicated by the usage model by determining whether the metrics exceed one or more probability thresholds associated with the expected states and transitions.

15. A non-transitory computer-readable storage medium having machine instructions stored therein, the instructions being executable by one or more processors to cause the one or more processors to perform operations comprising:

generating metrics regarding use of the application on a device, the metrics including data indicative of a plurality of states of the application and transitions between the states during execution of the application;

comparing the metrics to a usage model for the application that indicates an expected set of states of the application and transitions between the states during execution of the application;

determining a first portion of the metrics for which a difference between the first portion of the metrics and the expected states and transitions indicated by the usage model exceeds a predetermined threshold and a second portion of the metrics for which a difference between the second portion of the metrics and the expected states and transitions indicated by the usage model is within the predetermined threshold;

choosing the first portion of the metrics to be reported to an external device responsive to determining the difference between the first portion of the metrics and the expected states and transitions indicated by the usage model exceeds the predetermined threshold; and

transmitting to the external device the first portion of the metrics in response to a determination that the generated metrics correspond to at least a predetermined length of time.

16. The non-transitory computer-readable storage medium of claim 15, wherein the operations further comprise:

constructing the model using a history of states and state transitions for the application.

17. The non-transitory computer-readable storage medium of claim 15, wherein the operations further comprise:

transmitting the first portion of the generated metrics in response to a determination that the device has established a network connection.

18. The non-transitory computer-readable storage medium of claim 16, wherein constructing the model comprises generating a Markov chain using the history of states and state transitions for the application.