HIERARCHICAL BEHAVIORAL PROFILE

• US 20130166605A1
• Filed: 12/22/2011
• Published: 06/27/2013
• Est. Priority Date: 12/22/2011
• Status: Active Grant

• Current Assignee: Google Technology Holdings LLC (Alphabet Inc.)
• Original Assignee: General Instrument Corporation (CommScope Holding Company, Inc.)
• Inventors: Li, Jianguo, Hao, Guohua, Davis, Paul C.
• Granted Patent: US 9,110,998 B2
• Time in Patent Office: Days
• US Class Current: 707/803
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

In a hierarchical profile, each node represents at least one feature of behavioral data collected about an entity profiled, with the topmost node selected as the “statistically most informative” feature of the data. A profile can cover numerous domains and be predictively very powerful in each domain. A number of observations can be “aggregated” together into a single datapoint. In use, the structure of the profile is compared against current information associated with the entity to produce a recommendation or prediction. If the profile represents at least some data aggregation, then new observations are folded into the profile based on statistical weights of the aggregations. Because of the way the profile is created and updated, its hierarchical structure maps the collected observations. Therefore, as new observations are incorporated, if the new observations change the profile'"'"'s structure significantly, then it can be hypothesized that something “interesting” has happened to the entity.

Claims

1. A method for creating a hierarchical profile for an entity, the method comprising:

collecting, by a first computing device, behavioral data associated with the entity;

identifying, by the first computing device, a plurality of features of the collected data;

based, at least in part, on an analysis of at least a portion of the collected behavioral data, selecting, by the first computing device, a first feature of the identified features, wherein the first feature is selected as the statistically most informative feature of the analyzed behavioral data;

based, at least in part, on an analysis of at least a portion of the collected behavioral data, selecting, by the first computing device, a plurality of values for the selected first feature; and

creating, by the first computing device, a first hierarchical preference level in the profile, the first level based, at least in part, on the selected first feature and the selected values for the selected first feature.

2. The method of claim 1 wherein the first computing device is selected from the group consisting of:

a compute server, a coordinated group of compute servers, and a personal electronic device.

3. The method of claim 1 wherein the entity is selected from the group consisting of:

a person, a plurality of persons, a corporate entity, a social network of persons, a group of persons defined by a shared contextual attribute, and an entity defined by a collection rule.

4. The method of claim 1 wherein the behavioral data comprise an element selected from the group consisting of:

a preference explicitly stated by the entity, a preference explicitly stated by something other than the entity, passive usage data, passive contextual data, and a statistical aggregation of behavioral data.

5. The method of claim 1 wherein the behavioral data are filtered by a contextual attribute.

6. The method of claim 1 wherein the plurality of values are selected as the statistically most informative values of the selected first feature of the analyzed behavioral data.

7. The method of claim 1 wherein at least one node in the profile is further based, at least in part, on an application of a rule.

8. The method of claim 1 wherein at least one node in the profile comprises a statistical aggregation of at least a portion of the collected behavioral data.

9. The method of claim 1 wherein at least one node in the profile comprises a plurality of features.

10. The method of claim 1 further comprising:

defining a coverage criterion for the profile;

based, at least in part, on an analysis of at least a portion of the collected behavioral data, selecting, by the first computing device, a second feature of the identified features, the second feature distinct from the first feature;

based, at least in part, on an analysis of at least a portion of the collected behavioral data, selecting, by the first computing device, a plurality of values for the selected second feature;

creating, by the first computing device, a second hierarchical preference level in the profile, the second level based, at least in part, on the selected second feature and the selected values for the selected second feature; and

repeating the selecting and creating steps with distinct features until the coverage criterion is satisfied.

11. The method of claim 10 wherein the coverage criterion is selected from the group consisting of:

an amount of the collected behavioral data covered by the profile and a number of hierarchical preference levels of the profile.

12. The method of claim 10 wherein the second feature is selected as the statistically most informative feature, after the first selected feature, of the analyzed behavioral data.

13. The method of claim 1 further comprising:

based, at least in part, on an analysis of at least a portion of the collected behavioral data, selecting, by the first computing device, the entity to profile.

14. The method of claim 1 further comprising:

presenting, by the first computing device, at least a portion of the profile to a user;

receiving, by the first computing device, a profile modification from the user; and

modifying, by the first computing device, the profile, the modifying based, at least in part, on the received profile modification.

15. The method of claim 14 wherein the received profile modification comprises a rule.

16. The method of claim 1 further comprising:

selecting a second and a third feature of the identified plurality of features, wherein the selected second and third features are comparable and wherein a degree of overlap of coverages of the second and third features exceeds a threshold; and

replacing the second and third features in an analysis of at least a portion of the collected behavioral data with a combination of the second and third features.

17. The method of claim 1 further comprising:

sending, by the first computing device to a second computing device distinct from the first computing device, the created hierarchical profile.

18. A first computing device configured for creating a hierarchical profile for an entity, the first computing device comprising:

a transceiver configured for collecting behavioral data associated with the entity; and

a processor operatively connected to the transceiver and configured for;

identifying a plurality of features of the collected data;

based, at least in part, on an analysis of at least a portion of the collected behavioral data, selecting a first feature of the identified features, wherein the first feature is selected as the statistically most informative feature of the analyzed behavioral data;

based, at least in part, on an analysis of at least a portion of the collected behavioral data, selecting a plurality of values for the selected first feature; and

creating a first hierarchical preference level in the profile, the first level based, at least in part, on the selected first feature and the selected values for the selected first feature.

19. The first computing device of claim 18 wherein the first computing device is selected from the group consisting of:

a compute server, a coordinated group of compute servers, and a personal electronic device.

20. The first computing device of claim 18 wherein the entity is selected from the group consisting of:

a person, a plurality of persons, a corporate entity, a social network of persons, a group of persons defined by a shared contextual attribute, and an entity defined by a collection rule.

21. The first computing device of claim 18 wherein the behavioral data comprise an element selected from the group consisting of:

a preference explicitly stated by the entity, a preference explicitly stated by something other than the entity, passive usage data, passive contextual data, and a statistical aggregation of behavioral data.

22. The first computing device of claim 18 wherein the behavioral data are filtered by a contextual attribute.

23. The first computing device of claim 18 wherein the plurality of values are selected as the statistically most informative values of the selected first feature of the analyzed behavioral data.

24. The first computing device of claim 18 wherein at least one node in the profile is further based, at least in part, on an application of a rule.

25. The first computing device of claim 18 wherein at least one node in the profile comprises a statistical aggregation of at least a portion of the collected behavioral data.

26. The first computing device of claim 18 wherein at least one node in the profile comprises a plurality of features.

27. The first computing device of claim 18 wherein the processor is further configured for:

defining a coverage criterion for the profile;

based, at least in part, on an analysis of at least a portion of the collected behavioral data, selecting a second feature of the identified features, the second feature distinct from the first feature;

based, at least in part, on an analysis of at least a portion of the collected behavioral data, selecting a plurality of values for the selected second feature;

creating a second hierarchical preference level in the profile, the second level based, at least in part, on the selected second feature and the selected values for the selected second feature; and

repeating the selecting and creating steps with distinct features until the coverage criterion is satisfied.

28. The first computing device of claim 27 wherein the coverage criterion is selected from the group consisting of:

an amount of the collected behavioral data covered by the profile and a number of hierarchical preference levels of the profile.

29. The first computing device of claim 27 wherein the second feature is selected as the statistically most informative feature, after the first selected feature, of the analyzed behavioral data.

30. The first computing device of claim 18 wherein the processor is further configured for:

based, at least in part, on an analysis of at least a portion of the collected behavioral data, selecting the entity to profile.

31. The first computing device of claim 18 wherein the processor is further configured for:

presenting at least a portion of the profile to a user;

receiving a profile modification from the user; and

modifying the profile, the modifying based, at least in part, on the received profile modification.

32. The first computing device of claim 31 wherein the received profile modification comprises a rule.

33. The first computing device of claim 18 wherein the processor is further configured for:

selecting a second and a third feature of the identified plurality of features, wherein the selected second and third features are comparable and wherein a degree of overlap of coverages of the second and third features exceeds a threshold; and

replacing the second and third features in an analysis of at least a portion of the collected behavioral data with a combination of the second and third features.

34. The first computing device of claim 18 wherein the processor is further configured for:

sending, via the transceiver to a second computing device distinct from the first computing device, the created hierarchical profile.

35. A method for performing an action, the performing based, at least in part, on a hierarchical profile for an entity, the method comprising:

comparing a current context with the hierarchical profile, the current context comprising a plurality of features with values; and

based, at least in part, on the comparing, performing an action;

wherein an organization of the profile is based, at least in part, on a hierarchy of statistically most informative features of collected behavioral data associated with the entity.

36. The method of claim 35 wherein the action is selected from the group consisting of:

providing a recommendation, choosing a message to send, providing a prediction, and mapping a behavioral pattern.