Survey Administration System and Methods

• US 20120316921A1
• Filed: 06/08/2012
• Published: 12/13/2012
• Est. Priority Date: 06/10/2011
• Status: Active Grant

• Current Assignee: Ivalu8, Inc.
• Original Assignee: PrkL8 Inc
• Inventors: Carsanaro, Joe Anthony, Mills, Christopher Lawrence
• Granted Patent: US 8,965,780 B2
• Time in Patent Office: Days
• US Class Current: 705/7.32
• CPC Class Codes: G06F 3/0481 based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• CPC Class Codes: G06F 3/04842 Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06F 3/04847 Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls
• CPC Class Codes: G06Q 30/0207 Discounts or incentives, e.g. coupons or rebates
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

A system for determining preference, including a client device with feedback controls, a server and addressable URIs; the device and server in communication over an electronic network and the URIs'"'"' content retrievable over the network; the server automatically receiving and storing ratings, tracking URIs, using ratings to create a preference model for URIs, and using the preference model to suggest URIs; the stored ratings include a record having a rated item URI, a rater having a unique identification, a rating value provided by the rater, and at least one metadatum for creating subsets of ratings. Also, a method for automatically creating a chimeric preference vector, the method steps including identifying a multiplicity of datasets of rated items; automatically combining the datasets to form a combined dataset; automatically identifying ratings collisions; treating ratings collisions to form a data subset; and generating a chimeric preference vector based on the data subset.

Claims

1. A system for determining preference, the system comprising:

a. a client device, a server and addressable URIs;

the client device and server in communication over an electronic network and the URIs'"'"' content retrievable over the network;

b. the client device having selectable feedback controls for providing feedback from the user;

c. the server automatically receiving and storing ratings, tracking URIs, using ratings to create a preference model for URIs, and using the preference model to suggest URIs;

d. wherein the stored ratings include at least one record with a rated item URI, a rater having a unique identification, at least one rating value provided by the rater, and at least one metadatum including mood for creating subsets of ratings;

e. and wherein all ratings are available to the preference model.

2. The system of claim 1, wherein the feedback controls are independent of displayed URIs.

3. The system of claim 1, wherein the feedback controls are dynamically assignable by the server based on criteria available to the server.

4. The system of claim 2, wherein the feedback controls are dynamically assignable by the server based on criteria available to the server.

5. The system of claim 1, wherein the feedback controls are dynamically assignable by the server based on a survey creator'"'"'s selected feedback control set for an item.

6. The system of claim 2, wherein the feedback controls are dynamically assignable by the server based on a survey creator'"'"'s selected feedback control set for an item.

7. A method for automatically creating a preference vector calculated from ratings of a plurality of raters, the method steps comprising:

a. obtaining, by a server, rating for item from a plurality of raters and their moods;

the server communicates with a client device over an electronic network and the server can retrieve addressable URIs over the network;

the server automatically receiving and storing ratings from the client device, tracking URIs, using ratings to create a preference model for URIs, and using the preference model to suggest URIs;

wherein the stored ratings include at least one record having a rated item URI, a rater having a unique identification, at least one rating value provided by the rater, and at least one metadatum for creating subsets of ratings; and

wherein all ratings are available to the preference model;

b. identifying, by the server, multiple sets of rating for items to be used;

c. automatically combining, by the server, the multiple sets of rating to form a combined dataset;

d. automatically identifying, by the server, ratings collisions;

e. treating, by the server, ratings collisions automatically to generate a treated data subset of the combined dataset; and

f. generating, by the server, a preference vector based on the treated data subset.

8. The method of claim 7, wherein the step of treating the ratings collisions is selected from the group of treatments consisting of:

keeping the rating from a favored set and discarding any colliding ratings from other sets, keeping the median rating of the colliding ratings, eliminating all colliding ratings, averaging the colliding rating values to come up with a mean rating, negatively weighting items with colliding ratings that disagree, and combinations thereof.

9. The method of claim 7, wherein at least one dataset is a subset of a dataset of rated items.

10. The method of claim 7, wherein at least one set is for a rater and at least one set is a set of ratings for a rated item.

11. The method of claim 7, wherein the rater is a brand.

12. The method of claim 7, wherein the rater is selected from the group consisting of characters, actors, celebrities, politicians, and leaders.

13. A method for identifying content for a new market position, the method steps comprising:

a. obtaining, by a server, rating for item from a plurality of raters and their moods;

the server communicates with a client device over an electronic network and the server can retrieve addressable URIs over the network;

the server automatically receiving and storing ratings from the client device, tracking URIs, using ratings to create a preference model for URIs, and using the preference model to suggest URIs;

wherein the stored ratings include at least one record having a rated item URI, a rater having a unique identification, at least one rating value provided by the rater, and at least one metadatum for creating subsets of ratings; and

wherein all ratings are available to the preference model;

b. indicating, by the server, a set of ratings of rated items for a current market position;

c. indicating, by the server, a set of ratings of rated items for a target market position;

d. automatically combining, by the server, the sets of ratings to form a combined datasets;

e. identifying, by the server, the items where there are ratings collisions;

f. treating, by the server, ratings collisions automatically to generate a treated data subset of the combined dataset;

g. automatically creating, by the server, a new market position vector from the treated data set;

h. automatically identifying, by the server, items to suggest to users during the transition to transit them from the existing market position to the new market position.

14. The method of claim 13, further including the step of automatically identifying users to receive the identified suggested items.

15. The method of claim 13, wherein the step of treating the ratings collisions is selected from the group of treatments consisting of:

keeping the rating from a favored set and discarding any colliding ratings from other sets, keeping the median rating of the colliding ratings, eliminating all colliding ratings, averaging the colliding rating values to come up with a mean rating, negatively weighting items with colliding ratings that disagreeing, and combinations thereof.

16. A method for providing motivational devices to a rater, comprising:

a. obtaining, by a server, rating for item from a plurality of raters and their moods;

the server communicates with a client device over an electronic network and the server can retrieve addressable URIs over the network;

the server automatically receiving and storing ratings from the client device, tracking URIs, using ratings to create a preference model for URIs, and using the preference model to suggest URIs;

wherein the stored ratings include at least one record having a rated item URI, a rater having a unique identification, at least one rating value provided by the rater, and at least one metadatum for creating subsets of ratings; and

wherein all ratings are available to the preference model;

b. identifying, by the server, a first rater with a preference vector;

c. comparing, by the server, the preference vector of the rater with the preference vectors of at least one items with a motivation device;

d. identifying, by the server, non-colliding items;

e. pushing, by the server, at least one of the motivational devices for the non-colliding items to the rater;

thereby providing a method for pushing motivational devices to raters.

17. The method of claim 16, wherein the motivational device is a coupon.