Rule based data collection and management in a wireless communications network

• US 20060007901A1
• Filed: 07/05/2005
• Published: 01/12/2006
• Est. Priority Date: 07/08/2004
• Status: Active Grant

• Current Assignee: AT&T Mobility LLC (AT&T, Inc.)
• Original Assignee: Carrier Iq
• Inventors: Roskowski, Steve, Othmer, Konstantin, Ruf, Michael P., Kolm, David, Jaquet, James R.
• Granted Patent: US 7,551,922 B2
• Time in Patent Office: Days
• US Class Current: 370/338
• CPC Class Codes: H04W 24/00 Supervisory, monitoring or testing arrangements

Abstract

Systems and methods for defining and using a data collection profile in connection with a telecommunications network that includes multiple devices. Initially, a query is defined that concerns one or more aspects of the communications network. The query is then used as a basis for building a data collection profile. Next, a set of target devices is selected based upon criteria relating to the query. The data collection profile is then provided to the target devices which collect data in accordance with the data collection profile. Finally, the collected data is transmitted to a service platform for further processing.

Claims

1. A method for collecting data associated with a communications network that includes a plurality of devices, the method comprising:

obtaining a data collection profile that defines data that is to be collected and a condition under which the data is to be collected;

identifying a targeted subset of the plurality of devices in the communications network;

distributing the data collection profile to a targeted subset; and

receiving, from at least some of the targeted subset, the data that has been generated in response to the data collection profile.

2. The method as recited in claim 1, wherein identifying the targeted subset comprises accessing a target device database to select devices having characteristics that correspond to the data collection profile.

3. The method as recited in claim 2, wherein the characteristics that correspond to the data collection profile relate to a usage history of the individual devices .

4. The method as recited in claim 2, wherein the characteristics are determined utilizing data that has previously been collected from devices in the communication network using data collection profiles.

5. The method as recited in claim 2, wherein the characteristics that correspond to the data collection profile relate to at least one of:

environmental factors a device has encountered; and

a specific software configuration of a device.

6. The method as recited in claim 5, wherein:

the characteristics correspond to the specific software configuration; and

the software configuration includes at least one of;

downloaded software;

data entered by a user of the device;

user preferences;

downloaded ringtones;

or

other downloaded data.

7. The method as recited in claim 2, wherein the characteristics that correspond to the data collection profile relate to physical properties of the devices.

8. The method as recited in claim 2, wherein the characteristics that correspond to the data collection profile relate to at least one of:

area code;

prefix;

billing address; and

other data identifying the likely location of the device.

9. The method as recited in claim 2, wherein the characteristics that correspond to the data collection profile relate to at least one of:

services the user has subscribed; and

capabilities of the device.

10. The method as recited in claim 2, wherein the characteristics that correspond to the data collection profile relate to at least one of:

the ability of the device to collect metrics specified in the profile;

the ability of the device to respond to the triggers specified in the profile;

the RAM storage on the device available for metrics collection;

the persistent storage on the device available for storage of metrics packages;

the processing power of the device; and

the general ability of the device to execute the specified data collection profile.

11. The method as recited in claim 1, wherein the data has been generated in response to the detection of a triggering condition by individual devices of the targeted subset.

12. The method as recited in claim 11, wherein the triggering condition comprises one or more events in the communications network.

13. The method as recited in claim 11, wherein the triggering condition is defined as part of the data collection profile.

14. The method as recited in claim 1, wherein the data relates to services of the communications network that are provided to users through the devices.

15. The method as recited in claim 1, wherein the data relates to characteristics of a software stack of a device that communicates with various network layers associated with the communications network.

16. The method as recited in claim 15, wherein the software stack includes a radio layer that corresponds to at least one of a cellular radio protocol and a WLAN radio protocol.

17. The method as recited in claim 1, wherein the data collection profile defines a filter for the data by identifying data that is to be collected.

18. The method as recited in claim 17, wherein the data collection profile defines the filter for the data by specifying that either:

data associated with a software stack of a device that communicates with a particular layer of the communications network is to be collected;

or

data associated with a software stack of a device that communicates with a particular layer of the communications network is to be collected in response to another layer of the communications network being or not being in a particular state.

19. The method as recited in claim 1, further comprising using the received data to refine the data collection profile.

20. The method as recited in claim 1, wherein the data relates to communication by a software stack of a target device with at least one of:

an application layer of the communications network;

a transport layer of the communications network; and

a physical layer of the communications network.

21. The method as recited in claim 1, wherein the data relates to at least one of:

a usage history of the target device;

interaction with or physical manipulation of the device by the end user;

hardware associated with the target device; and

software associated with the target device.

22. The method as recited in claim 1, wherein identifying a targeted subset of the plurality of devices in the communications network is performed iteratively, such that the number of target devices falls within a selected range.

23. The method as recited in claim 1, wherein distributing the data collection profile to the targeted subset comprises one of:

pushing the data collection profile to the devices in the targeted subset;

sending messages to the devices in the targeted subset instructing the devices to retrieve the data collection profile from a server in the communications network; and

without sending a message to the devices in the targeted subset, preparing the data collection profile to be obtained by the devices when the devices next contact the server.

24. The method as recited in claim 1, wherein receiving, from at least some of the targeted subset the data that has been generated by the targeted subset in response to the data collection profile, comprises receiving the data in at least one metrics package and receiving an identifier that associates the metrics package with a specified transaction on the communications network.

25. The method as recited in claim 24, further comprising:

receiving metrics packages from multiple nodes in the communications network; and

based on the identifiers of the metrics packages, correlating a plurality of metrics packages received from different nodes of the communications network, wherein the plurality of metrics packages permit a sequence of events associated with the transaction in the communications network to be analyzed.

26. The method as recited in claim 1, wherein at least some of the target devices are wireless devices that operate in the communications network.

27. The method as recited in claim 24, wherein the data received from the wireless devices includes data that is observable only at the wireless device.

28. The method as recited in claim 1, wherein identifying the targeted subset comprises selecting devices having characteristics that correspond to device characteristics specified in or implied by the data collection profile.

29. The method as recited in claim 1, further comprising receiving, from at least some of the targeted subset, data that has been obtained from a collection buffer in response to a trigger defined by the data collection profile, wherein the data has been stored in the collection buffer prior to the trigger.

30. The method as recited in claim 1, wherein identifying the targeted subset of devices comprises considering any pre-existing collection task that a device may be associated with.

31. The method as recited in claim 1, wherein the target devices are capable of performing a local adjustment of their operations.

32. The method as recited in claim 31, wherein the local adjustment can be performed to:

minimize the impact on the communication network;

compensate for a prior failed attempt to perform a function;

respond to a battery status of the device;

minimize a negative impact on a user experience of a user of the device;

or

compensate for a perceived load of the communications network.

33. The method as recited in claim 32, wherein:

the local adjustment can be performed to compensate for a prior failed attempt to perform the function;

the function includes reporting of metrics packages; and

the local adjustment includes reporting metrics packages at a first time available after having failed to previously report.

34. The method as recited in claim 32, wherein the local adjustment can be performed to compensate for the perceived load of the communications network.

35. The method as recited in claim 32, wherein the target devices are capable of developing and maintaining an adaptive usage pattern for the user of the devices as a factor for performing a local adjustment.

36. The method as recited in claim 1, wherein at least some of the plurality of devices are end point devices.

37. The method as recited in claim 36, wherein the end point devices are distributed to external entities, including customers.

38. The method as recited in claim 36, wherein the endpoint devices are networked via an unreliable transport model.

39. The method as recited in claim 38, wherein the unreliable transport model is one of:

a wireless network; and

a cellular network.

40. The method a recited in claim 1, wherein the data received from at least some of the targeted subset has been:

archived locally as metric packages in metric package archives on the devices in the targeted subset; and

then

reported to a network server.

41. The method as recited in claim 40, wherein the archived data has been reported in response to an event defined in the data collection profile.

42. The method as recited in claim 41, wherein the event is a device-selected reporting time within a range of times defined within the data collection profile.

43. The method as recited in claim 42, wherein the selection of the reporting time by the device is based upon a unique number associated with the device, wherein the unique number is one of:

an electronic serial number associated with the device;

a mobile equipment identifier associated with the device; and

a phone number associated with the device.

44. The method as defined in claim 41, wherein:

the device has been unable to report within the a range of times defined by the data collection profile; and

in response to having been unable to respond in within the range of times, the device adopts another policy to report the data.

45. The method as recited in claim 44, wherein the other policy comprises reporting the data soon as possible after the range of times.

46. The method as recited in claim 41, wherein the event is a metric matching a specified set of conditions.

47. The method as recited in claim 41, wherein the reporting is delayed due to conditions measurable from the reporting device.

48. The method as recited in claim 47, wherein the measurable condition includes at least one of:

a battery status of the reporting device;

network connectivity associated with the reporting device; and

a measurable condition defined in the data collection profile.

49. The method as recited in claim 48, wherein the network connectivity is partially determined by RF coverage for the reporting device.

50. The method as recited in claim 40, wherein the metric package archives have a maximum size specified in the data collection profile.

51. The method as recited in claim 50, wherein:

the archived data has been reported in response to an event defined in the data collection profile; and

the event comprises at least one of;

the metric package archive of a reporting device reaching a specific percentage of the specified maximum size; and

the metric package archive reaching a specific size.

52. The method as recited in claim 50, wherein the reporting device includes logic that defines how the reporting device is to respond to the metric package archive becoming full.

53. The method as recited in claim 52, wherein the logic defines a prioritization scheme that:

determines if the total non-volatile memory required to store a new metrics package exceeds the amount of memory allotted to the metrics archive; and

if the total non-volatile memory required to store a new metrics package exceeds the amount of memory allotted to the metrics archive, replaces one or more archived metric packages at lower priority with metric packages of higher priority.