Method and apparatus for evolutionary design

Patent metadata

Field	Value
Publication number	US-RE46178-E
Application number	US-201113283495-A
Country	US
Kind code	E1
Filing date	Oct 27, 2011
Priority date	Nov 10, 2000
Publication date	Oct 11, 2016
Grant date	Oct 11, 2016

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.

First claim

What is claimed is:

1. A method of selecting a preferred one or a preferred group of forms of a product, each product form comprising a plurality of attributes, the method comprising the steps of:
(a) presenting, over an electronic network, to a plurality of selectors, one or more groups of product forms; (b) obtaining information from a selector about the selector's preference among the presented product forms; (c) using the obtained information to determine a derived group of product forms, each of at least some of the derived product forms comprising a combination of attributes different than the plurality of attributes of each of at least some of the presented product forms; (d) iterating steps (a) through (c), using a derived group from step (c), until a stopping criterion is achieved; and (e) upon achieving the stopping criterion, selecting one or a group of preferred product forms, wherein each of the attributes comprises a structural, functional, stylistic, or economic feature of the product.

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

3. The method of claim 1 wherein step (b) comprises obtaining information indicative of which presented product forms are preferred by the selector.

4. The method of claim 1 wherein step (b) comprises obtaining information indicative of which presented product forms are not preferred by the selector.

5. The method of claim 1 wherein step (b) comprises obtaining information indicative of relative preference of the selector from among the presented product forms.

6. The method of claim 1 wherein the obtained information from step (b) includes information indicative of the confidence of the selector in the selector's preference.

7. The method of claim 1 wherein step (b) comprises obtaining information indicative of a rating assigned to at least some of the presented product forms by at least one selector.

8. The method of claim 1 wherein step (b) comprises obtaining information indicative of a preference as between a presented product form and a previously presented product form.

9. The method of claim 1 wherein different selectors are presented with different groups of product forms.

10. The method of claim 1 wherein each selector comprises a person or a group of persons.

11. The method of claim 1 wherein step (c) includes determining the derived group of product forms by selecting the derived group of product forms.

12. The method of claim 1 wherein step (c) includes determining the derived group of product forms by selecting the derived group of product forms.

13. The method of claim 1 wherein step (c) includes determining the derived group of product forms by generating the derived group of product forms using a computational algorithm.

14. The method of claim 13 wherein the computational algorithm comprises an evolutionary algorithm.

15. The method of claim 13 wherein the computational algorithm comprises a genetic algorithm.

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

17. An electronic network comprising computers for use by selectors to express preferences for certain forms of a product, each product form comprising a plurality of attributes, the network being configured to:
(a) present one or more groups of product forms to a plurality of selectors; (b) obtain data from a selector indicative of the selector's preference from among the presented product forms; (c) use the obtained data to determine a derived group of product forms, each of at least some of the derived product forms comprising a combination of attributes different than the plurality of attributes of each of at least some of the presented product forms; (d) iterate steps (a) through (c), using a derived group from step (c), until a stopping criterion is achieved; and (e) upon achieving the stopping criterion, output data informing a decision to select one or a group of preferred product forms, wherein each of the attributes comprises a structural, functional, stylistic, or economic feature of the product.

18. The network of claim 17 wherein the stopping criterion comprises convergence of a group of product forms.

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

20. The network of claim 17 configured to obtain data indicative of relative preference of the selector from among presented product forms.

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 product forms.

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

24. The network of claim 17 configured to present different selectors with a different group of product forms.

25. The network of claim 17 configured to use a computational algorithm to generate the derived group of product forms.

26. The network of claim 17 configured to use selection to assemble the derived group of product forms.

27. The network of claim 17 configured to use an evolutionary algorithm to generate the derived group of product forms.

28. The network of claim 17 configured to use a genetic algorithm to generate the derived group of product forms.

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

30. The network of claim 17 further comprising at least one server for performing at least function (c).

31. A method of selecting one or more preferred forms of a product, the method comprising the steps of:
(a) presenting, over an electronic network, to a plurality of selectors, one or more groups of the product forms; (b) obtaining information from a selector about the selector's preference among the presented product forms; (c) using the obtained information to determine one or more derived product forms; (d) repeating steps (a) through (c), using at least some of the one or more derived product forms from step (c), until a stopping condition is met; and (e) upon achieving the stopping condition, selecting one or more of the remaining derived product forms as the one or more preferred product forms, wherein the product comprises a mass produced good, a consumer good, a manufactured good, a service, advertising material, or packaging material.

32. The method of claim 31 wherein the product comprises apparel, footwear, a computer, a telephone, a chair, a seat, an automobile, a bicycle, a home, a building, a boat hull, or a billboard.

33. The method of claim 31 wherein step (b) comprises obtaining information indicative of which product forms among the presented product forms are preferred by the selector.

34. The method of claim 31 wherein step (b) comp

Assignees

• Afeyan Noubar B
• Malek Kamal M
• Bufton Nigel J
• Ficici Steven G
• Austin Howard A
• Austin Larry J
• Mcclellan Honor E
• Nielsen Co Us Llc

Inventors

• Afeyan Noubar B
• Malek Kamal M
• Bufton Nigel J
• Ficici Steven G
• Austin Howard A

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06F2111/08

  Probabilistic or stochastic CAD · CPC title

• G06N3/126 Primary

  Evolutionary algorithms, e.g. genetic algorithms or genetic programming · CPC title

• G06F2111/06

  Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA] · CPC title

• G06Q10/101

  Collaborative creation, e.g. joint development of products or services · CPC title