METHOD AND SYSTEM FOR CREATING EVENTS AND MATCHING USERS VIA BLENDED PROFILES

• US 20120296973A1
• Filed: 05/21/2012
• Published: 11/22/2012
• Est. Priority Date: 05/20/2011
• Status: Active Grant

• Current Assignee: Blendabout, Inc.
• Original Assignee: Blendabout, Inc.
• Inventors: Spivak, Katharine D., Winters, Jon H.
• Granted Patent: US 8,793,314 B2
• Time in Patent Office: Days
• US Class Current: 709/204
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 10/02 Reservations, e.g. for tickets, services or events
• CPC Class Codes: G06Q 10/063112 Skill-based matching of a person or a group to a task
• CPC Class Codes: G06Q 10/06312 Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

Certain embodiments teach a method and system for pairing users, whom do not know each other for an event, on a social networking platform. The social dining system matches people with common interests and organizes an offline event, such as a meal at a restaurant, based on user profiles, availability, and other attributes. For example, the social dining system can schedule a group meal for a user, and five to seven other people that share an interest with the user. In cases where a user prefers to be attend events with at least one known user, a single profile for the user and the known user is created as a mutual or a blended profile. The system then creates an event by matching the mutual profile with other members using a blending algorithm.

Claims

1. A method of scheduling a social dining event by matching a blended profile with one or more entities:

receiving profile parameters which enable a blended profile to be created,

wherein the blended profile is an abstract data structure including information relating to at least two or more members,

wherein the at least two or more members are acquainted with each other, and

wherein the profile parameters include attributes of each member of the at least two or more members;

receiving event parameters which enable the social dining event to be scheduled,

wherein the social dining event arranges an appointment between the at least two or more members and the one or more entities at a restaurant, and

wherein the event parameters includes availability attributes of each member of the at least two or more members;

identifying a first blended profile, including information relating to the at least two or more members of the first blended profile,

wherein the information includes, at least, a first set of attributes for a first member and a second set of attributes for a second member, and

wherein the first member and the second member are acquainted with each other;

determining a set of mutual attributes, wherein the set of mutual attributes include the common attributes between the first set of attributes and the second set of attributes;

applying a weight to each mutual attribute in the set of mutual attributes;

using a blending algorithm to match the blended profile with one or more entities based, at least in part, on the set of mutual attributes,

wherein the one or more entities is either a member not including the first or second member, or a blended profile not including the first blended profile; and

arranging a first social dining event between the first member and the second member and the one or more entities matched by the blending algorithm.

2. The method of claim 1, wherein the set of mutual attributes includes one of the following:

preferred geographical locations, willingness to travel, favorite cuisines, activities, interests.

3. The method of claim 1,

wherein identifying a first blended profile further includes identifying a first user wish list for the first member and identifying a second user wish list for the second member;

and

wherein the set of mutual attributes further includes the common users between the first user wish list and the second user wish list.

4. The method of claim 1, wherein the blending algorithm utilizes a quality-thresholding clustering.

5. The method of claim 1,

wherein identifying a first blended profile includes identifying a first restaurant wish list for the first member and identifying a second restaurant wish list for the second member;

and

wherein the set of mutual attributes further includes the common restaurants between the first restaurant wish list and the second restaurant wish fist.

6. The method of claim 1, wherein using the blending algorithm further includes:

matching the first blended profile with a second blended profile and matching the first blended profile with a third blended profile.

7. A system for creating a social event by matching a mutual profile with one or more entities, comprising:

a processor;

a mutual profile module instantiated on the system which operates to;

receive profile parameters which enable a mutual profile to be created,

wherein the mutual profile is an abstract data structure including information relating to at least two or more members,

wherein the at least two or more members are acquainted with each other, and

wherein the profile parameters include attributes of each member of the at least two or more members; and

an event scheduling module, coupled to the mutual profile module, instantiated on the system which operates to;

receive event parameters which enable a social event to be scheduled,

wherein the social event arranges a meeting between the at least two or more members and the one or more entities at a restaurant, and

wherein the event parameters includes availability attributes of each member of the at least two or more members and each entity of the one or more entities;

identify a first mutual profile, including information relating to the at least two or more members of the first mutual profile,

wherein the information includes, at least, a first set of attributes for a first member and a second set of attributes for a second member, and

wherein the first member and the second member are acquainted with each other;

determine a set of mutual attributes, wherein the set of mutual attributes include common attributes between the first set of attributes and the second set of attributes;

apply a weight to each mutual attribute in the set of mutual attributes;

use a blending algorithm to match the mutual profile with one or more entities based, at least in part, on the set of mutual attributes,

wherein the one or more entities is either a member not including the first or second member, or a mutual profile not including the first mutual profile; and

arrange a first social event between the first member and the second member and the one or more entities matched by the blending algorithm.

8. The system of claim 7, wherein the set of mutual attributes includes one of the following:

preferred geographical locations, willingness to travel, favorite cuisines, activities, interests.

9. The system of claim 7, wherein the system further comprises a friends module, coupled to the mutual profile module, instantiated on the system which operates to identify a first user wish list for the first member and to identify a second user wish list for the second member;

and wherein the set of mutual attributes further includes the common users between the first user wish list and the second user wish list.

10. The system of claim 7, wherein the system further comprises a restaurants module, coupled to the mutual profile module, instantiated on the system which operates to identify a first restaurant wish list for the first member and to identify a second restaurant wish list for the second member;

and wherein the set of mutual attributes further includes the common restaurants between the first restaurant wish list and the second restaurant wish list.

11. The system of claim 7, wherein using a blending algorithm further includes;

matching the first mutual profile with a second mutual profile and matching the first mutual profile with a third mutual profile.

12. The system of claim 7, wherein the blending algorithm utilizes a quality-thresholding clustering.

13. A social dining method of arranging an event by matching a blended profile with at least two other blended profiles:

receiving profile parameters which enable a blended profile to be created,

wherein the blended profile is a single merged entity including information on two members,

wherein the two members are acquainted with each other, and

wherein the profile parameters include attributes of each member of the two members;

receiving event parameters which enable a social event to be scheduled,

wherein the social event arranges a gathering between the two members and at least two other blended profiles at a restaurant, and

wherein the event parameters includes availability attributes of each member of the two member and each member of the at least two other blended profiles;

identifying a first blended profile with a first member and a second member,

wherein identifying includes determining a first set of attributes for the first member and a second set of attributes for the second member, and

wherein the first member and the second member are acquainted with each other;

determining a set of mutual attributes, wherein the set of mutual attributes include the common attributes between the first set of attributes and the second set of attributes;

using a blending algorithm to match the blended profile with at least two other blended profiles based, at least in part, on the set of mutual attributes,

wherein the at least two other blended profiles do not include the first member and do not include the second member; and

arranging a first social event between the first member and the second member and the at least two other blended profiles matched by the blending algorithm.

14. The social dining method of claim 13, wherein the set of mutual attributes includes one of the following:

preferred geographical locations, willingness to travel, favorite cuisines, activities, interests.

15. The social dining method of claim 13,

wherein identifying a first blended profile further includes identifying a first user wish list for the first member and identifying a second user wish list for the second member;

and

wherein the set of mutual attributes further includes the common users between the first user wish list and the second user wish list.

16. The social dining method of claim 13, further comprises applying a weight to each mutual attribute in the set of mutual attributes.

17. The social dining method of claim 13, wherein the blending algorithm utilizes a quality-thresholding clustering.

18. The social dining method of claim 13,

wherein identifying a first blended profile includes identifying a first restaurant wish list for the first member and identifying a second restaurant wish list for the second member;

and

wherein the set of mutual attributes further includes the common restaurants between the first restaurant wish list and the second restaurant wish list.

19. The social dining method of claim 13, wherein using the blending algorithm further includes:

matching the first blended profile with a second blended profile and matching the first blended profile with a third blended profile.