Content preference with ratings and suggestions system and methods

• US 8,965,780 B2
• Filed: 06/08/2012
• Issued: 02/24/2015
• Est. Priority Date: 06/10/2011
• Status: Active Grant

• Current Assignee: Ivalu8, Inc.
• Original Assignee: PrkL8 Inc
• Inventors: Carsanaro, Joseph Anthony, Mills, Christopher Lawrence
• Primary Examiner(s): DIXON, THOMAS A
• Application Number:
  US13/491,978
• Publication Number:
  US 20120316921A1
• Time in Patent Office: 991 Days
• Field of Search: None
• US Class Current: 705/7.29
• 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 client device, a server and addressable uniform resource indicators (URIs), the client device and server in communication over an electronic network and a URI content retrievable over the network;

the client device having selectable feedback controls for obtaining feedback ratings and moods from a user while viewing content in order to provide context specific suggestions;

wherein the feedback controls are dynamically assigned and adjusted by the server based on criteria available to the server;

wherein the ratings are user-specified and dependent upon the URI content;

wherein the moods are user-specified rater states independent of the URI content; and

wherein the moods denote a context-specific preference;

the server automatically receiving and storing the moods and the ratings, tracking the URIs, using the ratings to create a preference model for suggesting additional URIs;

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 the moods for creating subsets of the ratings based on the moods; and

wherein all ratings are available to the preference model.

2. 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.

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

a server obtaining ratings and moods generated by a rater while viewing content via a client device, the client device having selectable feedback controls for obtaining the ratings and the moods of the user;

wherein the feedback controls are dynamically assigned and adjusted by the server based on criteria available to the server;

wherein the ratings are user-specified and dependent upon content of uniform resource indicators (URIs);

wherein the moods are user-specified rater states independent of the URI content, and

wherein the moods denote a context-specific preference;

the server communicating with the client device over an electronic network and the server retrieving addressable URIs over the network;

the server automatically receiving and storing the ratings from the client device, tracking the URIs, using the ratings to create a preference model for the URIs, and using the preference model to suggest additional 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 including the moods for creating subsets of ratings; and

wherein all ratings are available to the preference model;

identifying, by the server, the multiple subsets of ratings for items to be used;

automatically combining, by the server, the multiple subsets of ratings to form a combined dataset;

automatically identifying, by the server, ratings collisions;

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

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

4. The method of claim 3, 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.

5. The method of claim 3, wherein at least one of the subsets of ratings is a subset of rated items.

6. The method of claim 3, wherein at least one of the subsets of ratings is a dataset of ratings for a rated item by at least one of the raters.

7. The method of claim 3, wherein the rater is a brand.

8. The method of claim 3, wherein the rater is selected from the group consisting of characters, actors, celebrities, politicians, and leaders.

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

a server obtaining ratings and moods generated by a plurality of raters while viewing content via a plurality of client devices, the plurality of client devices having selectable feedback controls for obtaining the ratings and the moods of the plurality of raters;

wherein the feedback controls are dynamically assigned and adjusted by the server based on criteria available to the server;

wherein the ratings are user-specified and dependent upon content of uniform resource indicators (URIs);

wherein the moods are user-specified rater states independent of the URI content, and

wherein the moods denote a context-specific preference;

the server communicating with the plurality of client devices over an electronic network and the server retrieving addressable URIs over the network;

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

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

wherein all ratings are available to the preference model;

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

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

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

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

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

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

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.

10. The method of claim 9, further including the step of automatically identifying users to receive the identified suggested items.

11. The method of claim 9, 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.

12. A method for providing motivational devices to raters, comprising:

a server obtaining ratings and moods generated by a plurality of raters while viewing content via a plurality of client devices, the plurality of client devices having selectable feedback controls for obtaining the ratings and the moods of the plurality of raters;

wherein the feedback controls are dynamically assigned and adjusted by the server based on criteria available to the server;

the server communicating with the plurality of client devices over an electronic network and the server retrieving addressable uniform resource indicators (URIs) over the network;

wherein the ratings are user-specified and dependent upon content of the URIs;

wherein the moods are user-specified rater states independent of the URI content; and

wherein the moods denote a context-specific preference;

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

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

wherein all ratings are available to the preference model;

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

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

identifying, by the server, non-colliding items;

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.

13. The method of claim 12, wherein the motivational device is a coupon.