DISTRIBUTED SYSTEM AND METHODS FOR MODELING POPULATION-CENTRIC ACTIVITIES

• US 20130254152A1
• Filed: 03/23/2012
• Published: 09/26/2013
• Est. Priority Date: 03/23/2012
• Status: Abandoned Application

• Current Assignee: Palo Alto Research Center, Inc. (Xerox Holdings Corp.)
• Original Assignee: Palo Alto Research Center, Inc. (Xerox Holdings Corp.)
• Inventors: Zhang, Rui, Partridge, Kurt E., Begole, James M. A., Chu, Maurice Kyojin
• Application Number:
  US13/429,139
• Publication Number:
  US 20130254152A1
• Time in Patent Office: Days
• US Class Current: 706/52
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

A client device can receive information about a population to which a user belongs. During operation, the client device determines information about a user, determines a group identifier for the user, and communicates the determined information about the local user and the group identifier to a group-modeling server. The client device then receives a group-activity model that corresponds to the group identifier, and generates a user-activity model for the local user based on the group-activity model and the determined information about the local user. The client device uses the user-activity model to compute an activity probability for a corresponding target activity. The group-modeling server receives user information from a plurality of client devices of a group, and generates a group-activity model for the group based on the user information. The server then sends the group-activity model to users of the identified group.

Claims

1. A computer-implemented method, comprising:

determining, by a computing device, information about a local user;

determining a group identifier for the local user, wherein the group identifier identifies a group of users to which the local user belongs;

communicating the determined information about the local user and the group identifier to a group-modeling server;

receiving a group-activity model;

generating a user-activity model for the local user based on the group-activity model and the determined information about the local user.

2. The method of claim 1, wherein the user-activity model facilitates computing a probability that the local user is performing a corresponding target activity;

and

wherein the method further comprises computing the probability that the local user is performing the target activity based on the user-activity model and the local user'"'"'s contextual information.

3. The method of claim 1, wherein the group-activity model includes a set of group-parameter distributions for the identified group:

N₀(A,σ

₀),N₁(B₁,σ

₁), . . . N_n(B_n,σ

_n);

wherein N_i corresponds to a normal distribution for a corresponding curve-fitting parameter for users of the identified group, wherein A and B_i correspond to average values for the corresponding curve-fitting parameters, and wherein σ

_i corresponds to a standard deviation for a corresponding curve-fitting parameter.

4. The method of claim 3, wherein generating the user-activity model involves:

determining a first set of curve-fitting parameters for the local user based on the determined information about the local user;

determining a second set of curve-fitting parameters for the local user based on the first set of curve-fitting parameters and the set of group-parameter distributions; and

generating a function for the user-activity model based on the second set of curve-fitting parameters, wherein the function has the form;

p=logit(a+b₁*x₁+b₂*x₂+ . . . +b_n*x_n);

wherein p is a probability that the determined information about the local user corresponds to a target activity, wherein a and b_i correspond to curve-fitting parameters, and wherein x_i corresponds to a context feature value.

5. The method of claim 1, wherein the group identifier corresponds to at least one of:

a group of people in the local user'"'"'s online social network;

a group of people in the local user'"'"'s contact list stored in the computing device;

a group of people whose historical activities are determined to be similar to the local user'"'"'s historical activities;

the local user'"'"'s postal area code;

the local user'"'"'s current geographic location; and

an organization to which the local user is affiliated with.

6. The method of claim 1, wherein determining the information about the local user involves selecting at least one of:

a subset of contextual information of the local user;

a subset of profile information about the local user;

a subset of historical activities performed by the local user; and

a stored user-activity model for the local user.

7. The method of claim 6, wherein the contextual information includes at least one of:

a geographic location;

a motion trajectory;

a date range;

a logical name associated with a geographic location;

a logical name associated with an activity description;

a list of participants of the historical activity; and

a set of keywords associated with the historical activity.

8. A computer-implemented method, comprising:

receiving a group identifier and information about a user from a client device, wherein the group identifier identifies a group of similar users;

generating a group-activity model for the identified group based on a plurality of user-activity models from users in the identified group; and

communicating the group-activity model to one or more client devices that correspond to users of the identified group.

9. The method of claim 8, wherein the method further comprises:

receiving information about the user from the client device;

storing the received information in association with the identified group;

selecting information from a set of users of the identified group; and

generating the group-activity model based on the selected information, wherein generating the group-activity model involves generating a set of group-parameter distributions;

N₀(A,σ

₀),N₁(B₁,σ

₁), . . . N_n(B_n,σ

_n);

wherein N_i corresponds to a normal distribution for a corresponding curve-fitting parameter for users of the identified group, wherein A and B_i correspond to average values for the corresponding curve-fitting parameters, and wherein σ

_i corresponds to a standard deviation for a corresponding curve-fitting parameter.

10. The method of claim 8, wherein communicating the group-activity model involves:

determining that the computed group-activity model is sufficiently different from a previous group-activity model.

11. An apparatus, comprising:

an information-gathering module to determine information about a local user;

a group-identifying module to determine a group identifier for the local user, wherein the group identifier identifies a group of users to which the local user belongs;

a communication module to;

communicate the determined information about the local user and the group identifier to a group-modeling server; and

receive a group-activity model; and

an activity-modeling module to generate a user-activity model for the local user based on the group-activity model and the determined information about the local user.

12. The apparatus of claim 11, wherein the user-activity model facilitates computing a probability that the local user is performing a corresponding target activity;

and

wherein the apparatus further comprises a computing module to compute the probability that the local user is performing the target activity based on the user-activity model and the local user'"'"'s contextual information.

13. The apparatus of claim 11, wherein the group-activity model includes a set of group-parameter distributions for the identified group:

N₀(A,σ

₀),N₁(B₁,σ

₁), . . . N_n(B_n,σ

_n);

wherein N_i corresponds to a normal distribution for a corresponding curve-fitting parameter for users of the identified group, wherein A and B_i correspond to average values for the corresponding curve-fitting parameters, and wherein σ

_i corresponds to a standard deviation for a corresponding curve-fitting parameter.

14. The apparatus of claim 13, wherein while generating the user-activity model, the activity-modeling module is further configured to:

determine a first set of curve-fitting parameters for the local user based on the determined information about the local user;

determine a second set of curve-fitting parameters for the local user based on the first set of curve-fitting parameters and the set of group-parameter distributions; and

generate a function for the user-activity model based on the second set of curve-fitting parameters, wherein the function has the form;

p=logit(a+b₁*x₁+b₂*x₂+ . . . +b_n*x_n);

wherein p is a probability that the determined information about the local user corresponds to a target activity, wherein a and b_i correspond to curve-fitting parameters, and wherein x_i corresponds to a context feature value.

15. The apparatus of claim 11, wherein the group identifier corresponds to at least one of:

a group of people in the local user'"'"'s online social network;

a group of people in the local user'"'"'s contact list stored in the computing device;

a group of people whose historical activities are determined to be similar to the local user'"'"'s historical activities;

the local user'"'"'s postal area code;

the local user'"'"'s current geographic location; and

an organization to which the local user is affiliated with.

16. The apparatus of claim 11, wherein while determining the information about the local user, the information-gathering module is further configured to select at least one of:

a subset of contextual information of the local user;

a subset of profile information about the local user;

a subset of historical activities performed by the local user; and

a stored user-activity model for the local user.

17. The apparatus of claim 16, wherein the contextual information includes at least one of:

a geographic location;

a motion trajectory;

a date range;

a logical name associated with a geographic location;

a logical name associated with an activity description;

a list of participants of the historical activity; and

a set of keywords associated with the historical activity.

18. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method, the method comprising:

receiving a group identifier and information about a user from a client device, wherein the group identifier identifies a group of similar users;

generating a group-activity model for the identified group based on a plurality of user-activity models from users in the identified group; and

communicating the group-activity model to one or more client devices that correspond to users of the identified group.

19. The storage medium of claim 18, wherein the method further comprises:

receiving information about the user from the client device;

storing the received information in association with the identified group;

selecting information from a set of users of the identified group; and

generating the group-activity model based on the selected information, wherein generating the group-activity model involves generating a set of group-parameter distributions;

N₀(A,σ

₀),N₁(B₁,σ

₁), . . . N_n(B_n,σ

_n);

wherein N_i corresponds to a normal distribution for a corresponding curve-fitting parameter for users of the identified group, wherein A and B_i correspond to average values for the corresponding curve-fitting parameters, and wherein σ

_i corresponds to a standard deviation for a corresponding curve-fitting parameter.

20. The storage medium of claim 18, wherein communicating the group-activity model involves:

determining that the computed group-activity model is sufficiently different from a previous group-activity model.