Method and apparatus for evolutionary design

• US 20060080268A1
• Filed: 09/16/2005
• Published: 04/13/2006
• Est. Priority Date: 11/10/2000
• Status: Active Grant

• Current Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Original Assignee: Affinnova, Inc. (RR Donnelley & Sons Company)
• Inventors: Afeyan, Noubar B., Bufton, Nigel J., Ficici, Sevan G., Malek, Kamal M., Austin, Howard A., McClellan, Honor E., Austin, Larry J.
• Granted Patent: US 7,610,249 B2
• Time in Patent Office: Days
• US Class Current: 706/13
• CPC Class Codes: G06F 2111/06 Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
• CPC Class Codes: G06F 2111/08 Probabilistic or stochastic CAD
• CPC Class Codes: G06F 30/00 Computer-aided design [CAD]
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/101 Collaborative creation, e.g. joint development of products or services
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

The invention involves generating and presenting, typically electronically, a number of design alternatives to persons who are participating in the design, selection, or market research exercise. The participants (referred to as “selectors”) transmit data indicative of their preferences among or between the presented design alternatives, and that data is used to derive a new generation of design alternatives or proposals. The new designs are generated through the use of a computer program exploiting a genetic or evolutionary computational technique. The process is repeated, typically for many iterations or cycles.

Claims

1. A method of selecting a preferred one or a preferred group of decision objects having a plurality of combinations of attributes from a larger population of possible decision objects, the method comprising the steps of:

(a) presenting, over an electronic network, to a plurality of selectors, one or more groups of decision objects having a plurality of combinations of attributes;

(b) obtaining information from a selector expressing a selector preference among the presented decision objects;

(c) using the information to evolve or to select a derived group of decision objects comprising one or more different combinations of attributes;

(d) iterating steps (a) through (c), using a derived group from step (c); and

(e) upon achieving a stopping criterion, selecting one or a group of preferred decision objects for further development, manufacture, use, or sale.

2. The method of claim 1 wherein the stopping criterion is achieved when the derived group of decision objects ceases to change significantly after an iteration.

3. The method of claim 1 wherein step (b) comprises obtaining data indicative of which decision objects within the presented group are preferred by said selector.

4. The method of claim 1 wherein step (b) comprises obtaining data indicative of which decision objects within the presented group are not preferred by said selector.

5. The method of claim 1 comprising the additional step of identifying a subset of selectors having similar preferences.

6. The method of claim 1, wherein step (b) comprises obtaining data indicative of relative preference of a selector from among presented decision objects.

7. The method of claim 1 wherein the data indicative of a preference includes data indicative of the confidence of a selector in its preference expression.

8. The method of claim 1, wherein step (b) comprises obtaining data indicative of a rating assigned to at least some of the presented decision objects by at least one selector.

9. The method of claim 1, wherein step (b) comprises obtaining data indicative of a preference as between a presented decision object and a previously presented decision object.

10. The method of claim 1, wherein different selectors are presented with different groups of decision objects.

11. The method of claim 1, wherein each selector comprises one or more of:

(a) a person;

(b) a group of persons;

(c) a proxy for a person such as a machine learning system;

(d) a neural net, statistical model, or expert system;

or

(e) a combination thereof.

12. The method of claim 1 comprising using a genetic or evolutionary computational algorithm to evolve or select some or all of the derived decision objects.

13. The method of claim 1 wherein the decision objects comprise advertising material.

14. The method of claim 1 wherein the decision objects comprise packaging material.

15. The method of claim 1 wherein the decision objects comprise manufactured consumer goods.

16. The method of claim 1 wherein, for each iteration, each selector is presented with a group of decision objects substantially different from the groups presented to the other selectors.

17. An electronic network comprising a plurality of computers for design of one or a group of decision object comprising a plurality of attributes, the network being configured to:

(a) present one or more groups of decision objects having a plurality of combinations of attributes to a plurality of selectors;

(b) obtain data from a selector indicative of a selector preference from among the presented decision objects;

(c) use the data to evolve or to select a derived group of decision objects, one or more of which comprise different combinations of attributes;

(d) iterate steps (a) through (c), using a derived group from step (c); and

(e) upon achieving a stopping criterion, output data informing a decision to select one or a group of preferred decision objects for manufacture, use, or sale.

18. The network of claim 17 wherein the stopping criterion comprises convergence of a group of decision objects.

19. The network of claim 17 further configured to identify a subset of the plurality of selectors having similarity among expressed decision object preferences.

20. The network of claim 17 configured to obtain data indicative of relative preference of a selector from among presented decision objects.

21. The network of claim 17 configured to obtain data indicative of the confidence in an expressed selector preference.

22. The network of claim 17 configured to obtain data indicative of a rating assigned to at least some of the presented decision objects.

23. The network of claim 17 configured to obtain data from a selector indicative of the selector'"'"'s preference as between a presented decision object and a previously presented decision object.

24. The network of claim 17 configured to present each selector with a different group of decision objects.

25. The network of claim 17 configured to use a genetic or evolutionary computational algorithm to evolve some or all of the decision objects.

26. The network of claim 17 configured to enrich a generation of decision objects with objects comprising at least one preferred attribute.

27. The network of claim 17 configured to reduce the incidence of decision objects comprising at least one disfavored attribute.

28. The network of claim 17 configured to present groups of decision objects comprising advertising material, packaging material, or manufactured consumer goods.

29. The network of claim 17 wherein, for each iteration, each selector is presented with a group of decision objects substantially different from the groups presented to the other selectors