User Behavior Modeling for Intelligent Mobile Companions

• US 20140100835A1
• Filed: 10/04/2013
• Published: 04/10/2014
• Est. Priority Date: 10/04/2012
• Status: Abandoned Application

• Current Assignee: Futurewei Technologies Incorporated (Huawei Investment & Holding Co., Ltd.)
• Original Assignee: Futurewei Technologies Incorporated (Huawei Investment & Holding Co., Ltd.)
• Inventors: Waclawsky, John, Vanecek, George, Majumdar, Ishita, Bedford, Chris, Tran, Tim, Namasivayam, Gayathri
• Application Number:
  US14/046,770
• Publication Number:
  US 20140100835A1
• Time in Patent Office: Days
• US Class Current: 703/11
• CPC Class Codes: G06N 3/00 Computing arrangements based on biological models
• CPC Class Codes: G06Q 10/02 Reservations, e.g. for tickets, services or events
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/047 Optimisation of routes or paths, e.g. travelling salesman problem
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/0224 based on user history
• CPC Class Codes: G06Q 30/0252 based on events or environment, e.g. weather or festivals
• CPC Class Codes: G06Q 30/0261 based on user location
• CPC Class Codes: G06Q 30/0267 Wireless devices
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 52/0258 controlling an operation mode according to history or models of usage information, e.g. activity schedule or time of day
• CPC Class Codes: Y02D 30/70 in wireless communication networks

Abstract

An apparatus for modeling user behavior comprising at least one sensor for sensing a parameter, a memory, a processor coupled to the sensor and the memory, wherein the memory contains instructions that when executed by the processor cause the apparatus to collect a first data from the sensor, fuse the sensor data with a time element to obtain a context-feature, determine a first state based on the context-feature, record the first state in a state repository, wherein the state repository is configured to store a plurality of states such that the repository enables time-based pattern identification, and wherein each state corresponds to a user activity, incorporate information stored in the state repository into a behavior model, and predict an expected behavior based on the behavior model.

Claims

1. A mobile device for modeling user behavior comprising:

at least one sensor for sensing a parameter;

a memory;

a processor coupled to the sensor and the memory, wherein the memory contains instructions that when executed by the processor cause the apparatus to;

collect data from the sensor;

fuse the data with a time element to obtain a context-feature;

determine a first state based on the context-feature;

record the first state in a state repository, wherein the state repository is configured to store a plurality of states such that the state repository enables time-based pattern identification, and wherein each state corresponds to a user activity;

incorporate time-based pattern identification information into a behavior model; and

predict an expected user behavior based on the behavior model.

2. The mobile device of claim 1, wherein the sensor is a sensor for sensing geographic location, a sensor for sensing physical motion, or a sensor for sensing light, sound, or temperature.

3. The mobile device of claim 1, wherein fusing the data comprises utilizing a Kalman Filter approach, a Bayesian algorithm, or a Correlation regression.

4. The mobile device of claim 1, wherein incorporating time-based pattern identification information into a behavior model comprises utilizing a k-mean algorithm, a Hidden Markov model, or a conditional random field, and wherein recording the first state in the state repository comprises updating a state transition model using a state transition algorithm or a harmonic search.

5. The mobile device of claim 1, wherein the sensor is a sensor for sensing performance of a plurality of software applications on the apparatus with respect to at least one of the following metrics:

frequency of use, power consumption, processor demand, random access memory (RAM) demand, background usage duration, and foreground usage time.

6. The mobile device of claim 1, wherein the context-feature is selected from a group consisting of:

location, software applications in use, travel mode, activity data, and environment.

7. The mobile device of claim 1, wherein execution of the instructions further causes the apparatus to execute an action based on the expected behavior.

8. The mobile device of claim 7, wherein the action is selected from a group consisting of:

offering personalized services, suggesting traffic-managed alternate routes, sending a communication to a contact from a contact list, sending a communication to an emergency service, sending an instruction to a remote device, and running a context-aware power management routine, and wherein the personalized services include services selected from a group consisting of;

offering coupons, making reservations, and providing directions to a commercial establishment.

9. The apparatus of claim 1, wherein predicting the expected user behavior comprises selecting the expected user behavior from a preference correlation data set developed using a plurality of other users'"'"' behaviors.

10. The apparatus of claim 1, wherein incorporating time-based pattern identification information into the behavior model comprises performing a pattern recognition analysis to identify sequential patterns for predictive analysis.

11. The apparatus of claim 1, wherein predicting the expected user behavior comprises performing a first behavior vector analysis to extract implied information regarding user preferences and incorporating the implied information into a second behavior vector analysis.

12. A method of modeling user behavior for a platform on a mobile device, comprising:

collecting a time-based data from a plurality of sensors;

analyzing the data to determine a plurality of states, wherein each state corresponds to a real-world activity being performed by a user;

recording the plurality of states in a state repository;

incorporating information about the plurality of states into a behavior model, wherein building the behavior model comprises applying one or more behavior algorithms to the state repository in order to identify one or more behavior patterns;

predicting an expected user behavior based on the behavior model; and

sending instructions to perform an action to at least one hardware component, software application, or both based on the expected behavior.

13. The method of claim 12, wherein the sensors include two or more sensors selected from a group consisting of:

geographic position sensors, physical motion sensors, acoustic sensors, optical sensors, and temperature sensors.

14. The method of claim 12, wherein determining at least one state requires utilizing context-features, and wherein the context-features are selected from a group consisting of:

location, software applications in use, travel mode, activity data, and environment.

15. The method of claim 12, wherein applying the one or more behavior algorithms comprises utilizing one or more techniques selected from a group consisting of:

vector quantization algorithms, Hidden Markov Models (HMM), Bayes filtering, naï

ve Bayes classifiers, expectation-maximization for learning travel patterns from geographic location sensors, k-Nearest Neighbor (k-NN), support vector machines (SVM), and decision trees or decision tables for classifying the activity of a user based on accelerometer readings.

16. The method of claim 12, wherein the instructions inform the at least one hardware component, software component, or both to perform one or more of the following actions:

disabling, closing, deactivating, and powering-down.

17. The method of claim 12, wherein predicting the expected user behavior comprises selecting the expected user behavior from a preference correlation data set developed using a plurality of other users'"'"' behaviors.

18. The method of claim 12, wherein incorporating information about the plurality of states into a behavior model comprises performing a pattern recognition analysis to identify sequential patterns for predictive analysis.

19. The method of claim 12, wherein predicting the expected user behavior comprises performing a first behavior vector analysis to extract implied information regarding user preferences and incorporating the implied information into a second behavior vector analysis.

20. A computer program product for modeling user behavior comprising computer executable instructions stored on a non-transitory medium that when executed by a processor cause the processor to:

collect data from a mobile device over a time interval, wherein the data comprises low-level, mid-level, and high-level data;

fuse the data with time information to create a plurality of context-features;

utilize the plurality of context-features to determine a plurality of states, wherein each state corresponds to a real-world activity being performed by a user;

record the plurality of states in a state repository;

incorporate information stored in the state repository into a behavior model, wherein building the behavior model comprises applying one or more behavior algorithms to the state repository in order to identify one or more behavior patterns; and

identify an action to be taken by the mobile device based on an expected state, wherein the expected state is based on the behavior model.

21. The computer program product of claim 20, wherein the instructions further cause the processor to perform the action based on sensing a current state not matching the expected state.

22. The computer program product of claim 20, wherein the instructions further cause the processor to perform the action, and wherein the action is selected from a group consisting of:

offering personalized services, suggesting traffic-managed alternate routes, sending a communication to a contact from a contact list, sending a communication to an emergency service, sending an instruction to a remote device, and running a context-aware power management routine, and wherein the personalized services include services selected from a group consisting of;

offering coupons, making reservations, and providing directions to a commercial establishment.

23. The computer program product of claim 20, wherein the low-level data comprises data selected from a group consisting of:

global positioning system (GPS) data, accelerometer data, microphone data, camera data, wireless fidelity (WiFi) data, e-mail client data, short message service (SMS) client data, Bluetooth data, heart rate monitor data, and light sensor data, wherein the mid-level data comprises data selected from a group consisting of;

SMS software application data, email software application data, telephone software application data, and calendar software application data, and wherein the high-level data comprises data selected from a group consisting of;

search engine usage data, web browser usage data, social media usage data, music service data, and mobile commerce (M-Commerce) data.

24. The computer program product of claim 20, wherein applying the one or more behavior algorithms comprises utilizing one or more techniques selected from a group consisting of:

vector quantization algorithms, Hidden Markov Models (HMM), Bayes filtering, naï

ve Bayes classifiers, expectation-maximization for learning travel patterns from geographic location sensors, k-Nearest Neighbor (k-NN), support vector machines (SVM), and decision trees or decision tables for classifying the activity of a user based on accelerometer readings.

25. The computer program product of claim 20, wherein collecting the plurality of data comprises receiving the plurality of data from a remote location.

26. The computer program product of claim 20, wherein the action comprises instructing at least one hardware component, software component, or both to perform one or more of the following actions:

disabling, closing, deactivating, and powering-down.

27. The computer program product of claim 20, wherein the expected state is selected based on a preference correlation data set developed using a plurality of other users'"'"' behaviors.

28. The computer program product of claim 20, wherein incorporating information stored in the state repository comprises performing a pattern recognition analysis on the plurality of states to identify sequential patterns for predictive analysis.

29. The computer program product of claim 20, wherein identifying the one or more behavior patterns comprises performing a first behavior vector analysis to extract implied information regarding user preferences and incorporating the implied information into a second behavior vector analysis.