METHOD AND APPARATUS FOR INTERACTIVE EVOLUTIONARY OPTIMIZATION OF CONCEPTS

• US 20140344013A1
• Filed: 03/14/2014
• Published: 11/20/2014
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: Nielsen Consumer LLC (Nielsen Holdings plc)
• Original Assignee: Affinnova, Inc. (RR Donnelley & Sons Company)
• Inventors: Karty, Kevin D., Lamoureux, Steven H., Lee, Jenkin Espinoza, Flynn, Mark Gerard, Chen, Xinyan
• Granted Patent: US 9,799,041 B2
• Time in Patent Office: Days
• US Class Current: 705/7.29
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

A method includes determining a plurality of data points, determining a distance between each data point and each of the other plurality of data points, choosing a first one of the plurality of data points, identifying all of the other plurality of data points within a maximum distance of the chosen data point, repeating steps the previous steps to choose a different one of the plurality of data points until all of the data points have been chosen, identifying one or more clusters each having a predefined minimum number, K, of data points within a predefined search radius and analyzing the one or more clusters with respect to K linkages.

Claims

1. A method comprising:

(i) determining a plurality of data points;

(ii) determining a distance between each data point and each of the other of the plurality of data points;

(iii) choosing a first one of the plurality of data points;

(iv) identifying all of the other plurality of data points within a maximum distance of the chosen data point;

(v) repeating steps (iii)-(iv) choosing a different one of the plurality of data points until all of the data points have been chosen;

(vi) identifying one or more clusters each having a predefined minimum number, K, of data points within a predefined search radius; and

(vii) analyzing the one or more clusters with respect to K linkages.

2. The method of claim 1 wherein analyzing the one or more clusters comprises extending each cluster.

3. The method of claim 2 wherein analyzing the one or more clusters comprises extending each cluster to include any data points within a predetermined maximum span of at least m data points forming the cluster wherein m is less than a total number of all of the plurality of data points.

4. The method of claim 2 wherein analyzing the one or more clusters comprises extending each cluster to include any data point that is within a maximum span proportional to a number of data points in the cluster.

5. The method of claim 2 wherein analyzing the one or more clusters comprises extending each cluster to include any data point that is within a predetermined maximum average distance to a nearest n data points in the cluster.

6. The method of claim 1 wherein each of the plurality of data points corresponds to a click location on a graphical representation of a product.

7. The method of claim 6 further comprising utilizing information associating a location of each of the plurality of data points to an attribute of the graphical representation to identify the one or more clusters.

8. The method of claim 7 wherein the attribute of the graphical representation may be selected from the group consisting of a color, a color region, a shape, a text region and a pre-identified region.

9. A computer program product residing on a computer readable medium the computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations comprising:

(i) determining a plurality of data points;

(ii) determining a distance between each data point and each of the other of the plurality of data points;

(iii) choosing a first one of the plurality of data points;

(iv) identifying all of the other plurality of data points within a maximum distance of the chosen data point;

(v) repeating steps (iii)-(iv) choosing a different one of the plurality of data points until all of the data points have been chosen;

(vi) identifying one or more clusters each having a predefined minimum number, K, of data points within a predefined search radius; and

(vii) analyzing the one or more clusters with respect to K linkages.

10. The computer program of claim 9 wherein analyzing the one or more clusters comprises extending each cluster.

11. The computer program of claim 10 wherein analyzing the one or more clusters comprises extending each cluster to include any data points within a predetermined maximum span of at least m data points forming the cluster wherein m is less than a total number of all of the plurality of data points.

12. The computer program of claim 10 wherein analyzing the one or more clusters comprises extending each cluster to include any data point that is within a maximum span proportional to a number of data points in the cluster.

13. The computer program of claim 10 wherein analyzing the one or more clusters comprises extending each cluster to include any data point that is within a predetermined maximum average distance to a nearest n data points in the cluster.

14. The computer program of claim 9 wherein each of the plurality of data points corresponds to a click location on a graphical representation of a product.

15. The computer program of claim 14 further comprising utilizing information associating a location of each of the plurality of data points to an attribute of the graphical representation to identify the one or more clusters.

16. The computer program of claim 15 wherein the attribute of the graphical representation may be selected from the group consisting of a color, a color region, a text region and a pre-identified region.