METHOD AND APPRATUS FOR DETECTING PATTERNS OF BEHAVIOR

• US 20090240647A1
• Filed: 03/19/2009
• Published: 09/24/2009
• Est. Priority Date: 03/19/2008
• Status: Abandoned Application

• Current Assignee: Appleseed Networks Incorporated
• Original Assignee: Appleseed Networks Incorporated
• Inventors: Green, Omar, Gosby, Desiree
• Application Number:
  US12/407,515
• Publication Number:
  US 20090240647A1
• Time in Patent Office: Days
• US Class Current: 706/52
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

Systems, apparatus, techniques, and methods are disclosed for predictively adapting properties of devices as a function of a user'"'"'s historical behaviors (e.g., habits) as well as the specific context within which such behaviors are displayed. Such context can be virtually anything, such as day of the week, time of day, season, tide, temperature, weather, the user'"'"'s mood, the score of a particular sporting event from the previous day, the phase of the moon, the user'"'"'s location, etc. Based on observation by software, the user'"'"'s habits and the context within which those habits occur are observed and the device is customized based on the user'"'"'s behavioral patterns and the context thereof

Claims

1. A method of modifying the behavior of a device as a function of context of the device based on observations of the behavior of at least a user of the device as a function of context comprising:

tracking contextual information related to the device;

tracking behavioral information of a user of the device related to the device;

correlating the behavioral information with the contextual information to determine the context within which user behaviors related to the device are exhibited;

generating a predictive model of future behavior of a user of the device as a function of context related to the device based on the tracked contextual information and the tracked behavioral information; and

adjusting operation of the device as a function of a set of contextual information related to the device based on the predictive model.

2. The method of claim 1 wherein:

the tracking of contextual information comprises collecting instances of contextual information upon the occurrence of predetermined trigger events;

the tracking of behavioral information comprises collecting instances of user interaction with the device; and

the correlating comprises, for an instance of behavioral information, retrieving the most recent instance of contextual information preceding the collection of the instance of behavioral information.

3. The method of claim 1 wherein:

the tracking of behavioral information comprises detecting instances of user interaction with the device and storing an instance of behavioral information about the user interaction; and

the tracking of contextual information comprises collecting instances of contextual information responsive to the detection of instances of user interaction with the device.

4. The method of claim 2 wherein the method is performed in a network environment and wherein the device is a node on a network and further wherein the tracking of contextual information and the tracking of behavioral information is performed at the device and the generating is performed at a separate, server node of the network, further comprising;

transmitting the contextual information and the behavioral information via the network to the server node; and

wherein the adjusting comprises;

applying a set of data comprising an instance of contextual information about the device to the predictive model;

determining from the predictive model a predicted behavior of the user as a function of the set of data comprising an instance of contextual information; and

adjusting an operational parameter of the device based on the predicted behavior.

5. The method of claim 4 wherein the operational parameter of the device comprises a configuration of a display of an idle screen of the device.

6. The method of claim 4 wherein the predictive model is generated as a function of behavioral information and corresponding contextual information of other users of other devices on the network.

7. The method of claim 6 wherein the tracked behavioral information further comprises the absence of a behavior in a particular context.

8. The method of claim 4 wherein the device comprises a plurality of devices and wherein the adjusting may comprise adjusting operation of a first device based on information collected about a second device.

9. The method of claim 4 further comprising:

providing a plurality of modeling algorithms for generating the predictive model; and

selecting a one of the modeling algorithms as a function of the set of data comprising an instance of contextual information about the device.

10. The method of claim 4 further comprising:

providing a plurality of modeling algorithms for generating the predictive model; and

wherein the adjusting comprises;

applying a set of data comprising an instance of contextual information about the device to at least two of the plurality of predictive models;

determining from each of the at least two predictive models a predicted behavior of the user as a function of the set of data comprising an instance of contextual information;

selecting a one of the at least two models that provided a better predicted behavior; and

adjusting an operational parameter of the device based on the selected predicted behavior.

11. A method of modifying the behavior of a device as a function of context of the device based on observations of the behavior of at least a user of the device as a function of context comprising:

tracking contextual information related to the device;

tracking behavioral information of a user of the device related to the device;

generating and storing behavior atoms, the behavior atoms comprising a knowledge entity combined with the instance of contextual information to which it corresponds;

generating a predictive model of future behavior of a user of the device as a function of context related to the device by applying a modeling algorithm to the behavior atoms to create a model comprising an organized set of data points;

generating an empty data point, the empty data point comprising a set of contextual information without behavioral information;

generating a predicted behavior of a user of the device by applying the empty data point to the model; and

adjusting operation of the device as a function of the predicted behavior.

12. The method of claim 11 wherein the generating a predicted behavior is performed responsive to a trigger event further comprising:

detecting the trigger event; and

generating an inference query in response to the trigger event, the inference query comprising an instance of contextual information and at least one behavior type to be predicted;

wherein the generating of the predicted behavior of the behavior type identified in the inference query is performed responsive to the inference query.

13. The method of claim 12 further comprising:

maintaining a store of inference queries and responses thereto;

prior to generating the predicted behavior responsive to an inference query, checking the store of inference queries and replies thereto to determine if a similar inference query has been serviced previously; and

if a similar inference query has been previously serviced, using the corresponding inference query reply.

14. The method of claim 12 wherein the inference query further comprises an identity of a predictive model to use to generate a reply to the inference query and wherein the predictive model identified in the inference query is used to generate the inference query reply.

15. A method of modifying the behavior of a device as a function of context of the device based on observations of the behavior of at least a user of the device as a function of context comprising:

collecting behavioral information comprising a plurality of instances of use of the device;

collecting contextual information comprising a plurality of instances of context of the device, each instance of context corresponding to one of the instances of use of the device and hereinafter termed a context object;

generating behavior-context duples comprising the instances of use of the device with the corresponding context object;

extracting from each behavior-context duple a behavior factor;

transforming each behavior factor into at least one knowledge entity;

combining each knowledge entity with the context object of the behavior-context duple from which the knowledge entity was derived, the combination hereinafter termed a behavior atom;

creating a predictive model of use behavior with respect to the device from the behavior atoms, the predictive model comprising a plurality of data points derived from the behavior atoms;

generating an inference query comprising a context object;

generating an empty data point comprising the context object from the inference query;

applying the empty data point to the predictive model to generate an inference query reply comprising a predicted behavior of a user of the device as a function of the context object; and

modifying an operation of the device as a function of the inference query reply.

16. The method of claim 15 further comprising:

storing the inference queries and corresponding inference query replies;

responsive to generation of an inference query, determining if an inference query reply to a similar inference query has previously been stored; and

if a similar inference query has previously been stored, using the inference query reply corresponding to the previously stored inference query to generate an inference query reply to the instant inference query.

17. The method of claim 15 further comprising:

collecting context objects irrespective of an accompanying use of the device for purposes of tracking the absence of behaviors with respect to the device as a function of context, hereinafter termed empty context objects; and

processing the empty context objects similarly to the behavior-context duples to create additional data points for the model.

18. The method of claim 15 wherein the inference query providing a plurality of predictive models at the server;

and

selecting a one of the predictive models as a function of the corresponding inference query.

19. The method of claim 18 wherein the inference query further comprises an identity of a one of the predictive models to use in connection with that inference query.

20. The method of claim 15 wherein the device comprises a plurality of devices and wherein the modifying comprises modifying operation of a first device based on information collected about a second device.

21. A method of predicting a context within which a voluntary behavior will be exhibited based on observations of the behavior of at least a user of a device as a function of context comprising:

tracking contextual information related to the device;

tracking behavioral information of a user of the device related to the device;

correlating the behavioral information with the contextual information to determine the context within which user behaviors related to the device are exhibited;

generating a predictive model of future behavior of a user of the device as a function of context related to the device based on the tracked contextual information and the tracked behavioral information; and

predicting a context of a user of the device as a function of a behavior related to the device based on the predictive model.

22. The method of claim 21 wherein:

the tracking of contextual information comprises collecting instances of contextual information;

the tracking of behavioral information comprises collecting instances of user interaction with the device;

the correlating comprises, for an instance of behavioral information, retrieving a temporally corresponding instance of contextual information; and

wherein the predicting comprises;

applying to the predictive model a set of data comprising an instance of behavioral information related to the device; and

determining from the predictive model a predicted set of contextual information of the device as a function of the set of data comprising an instance of behavioral information.