Dynamic profile slice

• US 8,473,512 B2
• Filed: 11/08/2010
• Issued: 06/25/2013
• Est. Priority Date: 11/06/2009
• Status: Expired due to Fees

• Current Assignee: Concert Technology Corporation
• Original Assignee: Waldeck Technology, LLC (Concert Technology Corporation)
• Inventors: Petersen, Steven L., Katpelly, Ravi Reddy
• Primary Examiner(s): Saeed, Usmaan
• Assistant Examiner(s): Uddin, Md. I
• Application Number:
  US12/941,464
• Publication Number:
  US 20120066231A1
• Time in Patent Office: 960 Days
• Field of Search: 707/706, 707/709, 707/722, 707/736, 707/748, 707/733, 707/754, 707/758, 707/776
• US Class Current: 707/776
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04L 65/403 Arrangements for multi-party communication, e.g. for conferences data switching systems for conference H04L12/18; arrangements for connecting several subscribers to a common circuit, i.e. affording conference facilities H04M3/56; television conferencing systems H04N7/15
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04W 4/021 Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences

Abstract

Systems and methods are provided for maintaining a dynamic profile slice of a user profile of a user. In one embodiment, a real-time user-generated context of the user is monitored over time to accumulate keywords in the dynamic profile slice of the user that are representative of dynamic interests of the user. Weights are assigned to the keywords in the dynamic profile slice using a time and/or location weighting function.

Claims

1. A computer-implemented method comprising:

monitoring a real-time user-generated context of a user to accumulate a plurality of keywords in a dynamic profile slice of a user profile of the user that are representative of dynamic interests of the user and, for each keyword of the plurality of keywords, a timestamp that defines a most recent time at which the keyword was obtained from the real-time user-generated context of the user, wherein, for each keyword of the plurality of keywords, the dynamic profile slice further comprises a location stamp that defines a location at which the user was located at the most recent time at which the keyword was obtained from the real-time user-generated context of the user;

for each keyword of the plurality of keywords, assigning a weight to the keyword as a function of both an amount of time that has elapsed since the most recent time at which the keyword was obtained from the real-time user-generated context of the user as indicated by the timestamp for the keyword and a distance between a current location of the user and the location at which the user was located at the most recent time at which the keyword was obtained from the real-time user-generated context of the user as indicated by the location stamp for the keyword;

receiving a crowd request from a mobile device of the user;

in response to the crowd request, identifying one or more crowds of users currently located within a bounding region created for the crowd request;

for each crowd of the one or more crowds, obtaining crowd data for the crowd that comprises an aggregate profile of the crowd generated based on a comparison of at least the dynamic profile slice of the user profile of the user to user profiles of a plurality of users in the crowd; and

returning the crowd data for the one or more crowds to the mobile device of the user.

2. The method of claim 1 wherein the real-time user-generated context of the user comprises search terms entered by the user, and monitoring the real-time user-generated context comprises monitoring the search terms entered by the user to accumulate at least a portion of the plurality of keywords in the dynamic profile slice of the user.

3. The method of claim 1 wherein the real-time user-generated context of the user comprises search terms entered by the user to search for crowds of users having user profiles that match the search terms, and monitoring the real-time user-generated context comprises monitoring the search terms entered by the user to accumulate at least a portion of the plurality of keywords in the dynamic profile slice of the user.

4. The method of claim 1 wherein, for each keyword of the plurality of keywords, assigning the weight to the keyword comprises assigning the weight to the keyword in response to receiving the crowd request from the mobile device of the user.

5. The method of claim 1 wherein, for each crowd of the one or more crowds, the aggregate profile of the crowd comprises an overall match strength computed based on the weights assigned to the plurality of keywords in the dynamic profile slice of the user profile of the user and a number of user matches between each keyword of the plurality of keywords in the dynamic profile slice of the user profile of the user and the user profiles of the plurality of users in the crowd.

6. The method of claim 1 wherein, for each crowd of the one or more crowds, the aggregate profile of the crowd comprises, for each keyword of the plurality of keywords in the dynamic profile slice, a match strength computed based on the weight assigned to the keyword and a number of user matches between the keyword and the user profiles of the plurality of users in the crowd.

7. The method of claim 1 further comprising maintaining, for each user of a plurality of users comprising the user, a user profile that comprises a dynamic profile slice, wherein for each user of the plurality of users, maintaining the user profile of the user that comprises the dynamic profile slice comprises:

monitoring a real-time user-generated context of the user to accumulate a plurality of keywords in the dynamic profile slice of the user profile of the user that are representative of dynamic interests of the user and, for each keyword of the plurality of keywords, a timestamp that defines a most recent time at which the keyword was obtained from the real-time user-generated context of the user; and

for each keyword of the plurality of keywords, assigning a weight to the keyword as a function of an amount of time that has elapsed since the most recent time at which the keyword was obtained from the real-time user-generated context of the user as indicated by the timestamp for the keyword.

8. The method of claim 7 further comprising periodically updating the weights assigned to the plurality of keywords in the dynamic profile slice of each of the plurality of users.

9. The method of claim 1 further comprising updating the weights assigned to the plurality of keywords over time.

10. The method of claim 9 further comprising removing a keyword of the plurality of keywords from the dynamic profile slice once the weight assigned to the keyword falls below a predefined threshold.

11. A server comprising:

a communication interface communicatively coupling the server to a device of a user via a network; and

a processor associated with the communication interface and adapted to;

monitor a real-time user-generated context of the user at the device to accumulate a plurality of keywords in a dynamic profile slice of a user profile of the user that are representative of dynamic interests of the user and, for each keyword of the plurality of keywords, a timestamp that defines a most recent time at which the keyword was obtained from the real-time user-generated context of the user, wherein, for each keyword of the plurality of keywords, the dynamic profile slice further comprises a location stamp that defines a location at which the user was located at the most recent time at which the keyword was obtained from the real-time user-generated context of the user;

for each keyword of the plurality of keywords, assigning a weight to the keyword as a function of both an amount of time that has elapsed since the most recent time at which the keyword was obtained from the real-time user-generated context of the user as indicated by the timestamp for the keyword and a distance between a current location of the user and the location at which the user was located at the most recent time at which the keyword was obtained from the real-time user-generated context of the user as indicated by the location stamp for the keyword;

receive a crowd request from a mobile device of the user;

in response to the crowd request, identify one or more crowds of users currently located within a bounding region created for the crowd request;

for each crowd of the one or more crowds, obtain crowd data for the crowd that comprises an aggregate profile of the crowd generated based on a comparison of at least the dynamic profile slice of the user profile of the user to user profiles of a plurality of users in the crowd; and

return the crowd data for the one or more crowds to the mobile device of the user.

12. A non-transitory computer readable medium storing software for instructing a controller of a computing device to:

monitor a real-time user-generated context of a user to accumulate a plurality of keywords in a dynamic profile slice of a user profile of the user that are representative of dynamic interests of the user and, for each keyword of the plurality of keywords, a timestamp that defines a most recent time at which the keyword was obtained from the real-time user-generated context of the user, wherein, for each keyword of the plurality of keywords, the dynamic profile slice further comprises a location stamp that defines a location at which the user was located at the most recent time at which the keyword was obtained from the real-time user-generated context of the user;

for each keyword of the plurality of keywords, assign a weight to the keyword as a function of both an amount of time that has elapsed since the most recent time at which the keyword was obtained from the real-time user-generated context of the user as indicated by the

timestamp for the keyword and a distance between a current location of the user and the location at which the user was located at the most recent time at which the keyword was obtained from the real-time user-generated context of the user as indicated by the location stamp for the keyword;

receive a crowd request from a mobile device of the user;

in response to the crowd request, identify one or more crowds of users currently located within a bounding region created for the crowd request;

for each crowd of the one or more crowds, obtain crowd data for the crowd that comprises an aggregate profile of the crowd generated based on a comparison of at least the dynamic profile slice of the user profile of the user to user profiles of a plurality of users in the crowd; and

return the crowd data for the one or more crowds to the mobile device of the user.