SYSTEM AND METHOD FOR AIDING PRODUCT DESIGN AND QUANTIFYING ACCEPTANCE

• US 20070067212A1
• Filed: 09/21/2006
• Published: 03/22/2007
• Est. Priority Date: 09/21/2005
• Status: Active Grant

• Current Assignee: Icosystem Corp.
• Original Assignee: Icosystem Corp.
• Inventors: Bonabeau, Eric
• Granted Patent: US 8,423,323 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/00 Commerce
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

Methods and systems for designing new products may comprise having rated a first plurality of existing products in a product field according to a second plurality of product attributes; assigning each of the said first plurality of existing products a location in a space having the said second plurality of dimensions, according to the said existing product'"'"'s ratings with respect to the said second plurality of product attributes; locating the said first plurality of existing products in a space of a third plurality of dimensions; choosing a fourth plurality of reference products which sample the said space of the said third plurality of dimensions, from among the said first plurality of existing products; having conducted a consumer survey of the said fourth plurality of reference products; creating a map of consumer responses in the said space of the said third plurality of dimensions; and designing the said new product based upon the said map of consumer responses. In some embodiments, the product rated may be perfume.

Claims

1. A method for designing a new product, comprising:

a) having rated a first plurality of existing products in a product field according to a second plurality of attributes;

b) assigning each of the said first plurality of existing products a location in a space having the said second plurality of dimensions, according to the said existing product'"'"'s ratings with respect to the said second plurality of attributes of the said existing product;

c) locating the said first plurality of existing products in a space of a third plurality of dimensions;

d) choosing a fourth plurality of reference products which sample the said space of the said third plurality of dimensions, from among the said first plurality of existing products;

e) having conducted a consumer survey concerning the said fourth plurality of reference products;

f) creating a map of consumer responses in the said space of the said third plurality of dimensions; and

g) designing the said new product based upon the said map of consumer responses.

2. The method of claim 1, wherein the product field is perfumes.

3. The method of claim 1, wherein each rating of one of the said first plurality of existing products according to one of the said second plurality of attributes of the said existing product is a number from 0 to 1.

4. The method of claim 1, wherein the said second plurality of ratings of each of the said first plurality of existing products according to the said second plurality of attributes of the said existing product are normalized.

5. The method of claim 1, wherein the said first plurality of products is located in the said space of the said third plurality of dimensions using multidimensional scaling.

6. The method of claim 5, wherein the said first plurality of products is located in the said space of the said third plurality of dimensions using multidimensional scaling and interactive evolutionary computing.

7. The method of claim 1, wherein the said fourth plurality of reference products which sample the said space of the said third plurality of dimensions is chosen from among the said first plurality of existing products using bootstrapping.

8. The method of claim 1, wherein the said consumer survey comprises obtaining information about at least one demographic variable of a plurality of surveyed consumers.

9. The method of claim 1, wherein the said map of consumer responses comprises a map of consumer ratings of one characteristic of the said fourth plurality of reference products.

10. The method of claim 9, wherein the said one characteristic is consumer acceptance of the said product.

11. The method of claim 9, wherein the said map of consumer ratings of one characteristic of the said fourth plurality of reference products is created by using a graphical method.

12. The method of claim 11, wherein the graphical method is a neural network.

13. The method of claim 9, wherein the said map of consumer ratings of one characteristic of the said fourth plurality of reference products is created by using a genetic algorithm.

14. The method of claim 9, further comprising designing the said new product based upon the said map by

a) locating an unpopulated region in the said space of the said third plurality of dimensions, wherein a consumer rating of the said one characteristic is predicted by the said map to be high, and

b) locating a corresponding region in the said space having the said second plurality of dimensions.

15. The method of claim 14, further comprising locating the said corresponding region in the said space having the said second plurality of dimensions by using multidimensional scaling.

16. The method of claim 15, further comprising locating the said corresponding region in the said space having the said second plurality of dimensions by using multidimensional scaling and a genetic algorithm.

17. The method of claim 9, further comprising designing the said new product based upon the said map by

a) selecting an initial design comprising values for the said new product with respect to each of the said second plurality of product attributes;

b) locating the said initial design in the said space of the said third plurality of dimensions;

c) finding a location in the said space of the said third plurality of dimensions which is in a vicinity of the said initial design and which is predicted by the said map to have a greater value of the said consumer rating of a desired product characteristic than the said initial design; and

d) finding a corresponding location in the said space having the said second plurality of dimensions.

18. The method of claim 17, further comprising locating the said initial design in the said space of the said third plurality of dimensions by means of multidimensional scaling.

19. The method of claim 17, further comprising locating the said initial design in the said space of the said third plurality of dimensions by means of interpolation.

20. The method of claim 17, further comprising finding the said location in the said space of the said third plurality of dimensions which is in the said vicinity of the said initial design and which is predicted by the said map to have the said greater value of the said consumer rating of the said desired product characteristic by means of a gradient ascending algorithm.

21. The method of claim 20, further comprising finding the said location in the said space of the said third plurality of dimensions which is in the said vicinity of the said initial design and which is predicted by the said map to have the said greater value of the said consumer rating of the said desired product characteristic by means of a genetic algorithm.

22. The method of claim 17, further comprising locating the said corresponding location in the said space having the said second plurality of dimensions by using multidimensional scaling.

23. The method of claim 22, further comprising locating the said corresponding location in the said space having the said second plurality of dimensions by using multidimensional scaling and a genetic algorithm.

24. The method of claim 1, wherein the said map of consumer responses comprises a map of consumer ratings of a plurality of characteristics of the said fourth plurality of reference products.

25. The method of claim 24, further comprising generating the said map of consumer ratings of the said plurality of characteristics of the said fourth plurality of reference products in the said space of a second plurality of dimensions based upon the said map of consumer ratings of the said plurality of characteristics of the said fourth plurality of reference products in the said space of the said third plurality of dimensions.

26. A system, comprising at least one processor in communications with at least one display, the at least one processor having instructions for causing the at least one processor to:

a) assign each of a first plurality of existing products in a product field a location in a space having a second plurality of dimensions, according to the said existing product'"'"'s ratings with respect to the said second plurality of product attributes;

b) locate the said first plurality of existing products in a space of a third plurality of dimensions;

c) choose a fourth plurality of reference products which sample the said space of the said third plurality of dimensions, from among the said first plurality of existing products; and

d) create in the said space of the said third plurality of dimensions a map of consumer responses to a consumer survey concerning the said fourth plurality of reference products.

27. The system of claim 26, wherein the product field is perfumes.

28. The system of claim 26, wherein each rating of one of the said first plurality of existing products according to one of the said second plurality of product attributes is a number from 0 to 1.

29. The system of claim 26, wherein the said second plurality of ratings of each of the said first plurality of existing products according to the said second plurality of product attributes are normalized.

30. The system of claim 26, wherein the said first plurality of products is located in the said space of the said third plurality of dimensions using multidimensional scaling.

31. The system of claim 30, wherein the said first plurality of products is located in the said space of the said third plurality of dimensions using multidimensional scaling and interactive evolutionary computing.

32. The system of claim 26, wherein the said fourth plurality of reference products which sample the said space of the said third plurality of dimensions is chosen from among the said first plurality of existing products using bootstrapping.

33. The system of claim 26, wherein the said consumer survey comprises obtaining information about at least one demographic variable of a plurality of surveyed consumers.

34. The system of claim 26, wherein the said map of consumer responses comprises a map of consumer ratings of one characteristic of the said fourth plurality of reference products.

35. The system of claim 34, wherein the said one characteristic is consumer acceptance of the said product.

36. The system of claim 34, wherein the said map of consumer ratings of one characteristic of the said fourth plurality of reference products is created by using a graphical method.

37. The system of claim 36, wherein the graphical method is a neural network.

38. The system of claim 34, wherein the said map of consumer ratings of one characteristic of the said fourth plurality of reference products is created by using a genetic algorithm.

39. The system of claim 34, further comprising the at least one processor having instructions for causing the at least one processor to:

a) locate an unpopulated region in the said space of the said third plurality of dimensions, wherein a consumer rating of the said one characteristic is predicted by the said map to be high, and

b) locate a corresponding region in the said space having the said second plurality of dimensions.

40. The system of claim 39, further comprising the at least one processor having instructions for causing the at least one processor to locate the said corresponding region in the said space having the said second plurality of dimensions by using multidimensional scaling.

41. The system of claim 40, further comprising the at least one processor having instructions for causing the at least one processor to locate the said corresponding region in the said space having the said second plurality of dimensions by using multidimensional scaling and a genetic algorithm.

42. The system of claim 34, further comprising the at least one processor having instructions for causing the at least one processor to

a) receive as input an initial design for a new product comprising values for the said new product with respect to each of the said second plurality of product attributes;

b) locate the said initial design in the said space of the said third plurality of dimensions;

c) find a location in the said space of the said third plurality of dimensions which is in a vicinity of the said initial design and which is predicted by the said map to have a greater value of the said consumer rating of a desired product characteristic than the said initial design; and

d) find a corresponding location in the said space having the said second plurality of dimensions.

43. The system of claim 42, further comprising the at least one processor having instructions for causing the at least one processor to locate the said initial design in the said space of the said third plurality of dimensions by means of multidimensional scaling.

44. The system of claim 42, further comprising the at least one processor having instructions for causing the at least one processor to locate the said initial design in the said space of the said third plurality of dimensions by means of interpolation.

45. The system of claim 42, further comprising the at least one processor having instructions for causing the at least one processor to find the said location in the said space of the said third plurality of dimensions which is in the said vicinity of the said initial design and which is predicted by the said map to have the said greater value of the said consumer rating of the said desired product characteristic by means of a gradient ascending algorithm.

46. The system of claim 45, further comprising the at least one processor having instructions for causing the at least one processor to find the said location in the said space of the said third plurality of dimensions which is in the said vicinity of the said initial design and which is predicted by the said map to have the said greater value of the said consumer rating of the said desired product characteristic by means of a genetic algorithm.

47. The system of claim 42, further comprising the at least one processor having instructions for causing the at least one processor to locate the said corresponding location in the said space having the said second plurality of dimensions by using multidimensional scaling.

48. The system of claim 47, further comprising the at least one processor having instructions for causing the at least one processor to locate the said corresponding location in the said space having the said second plurality of dimensions by using multidimensional scaling and a genetic algorithm.

49. The system of claim 26, wherein the said map of consumer responses comprises a map of consumer ratings of a plurality of characteristics of the said fourth plurality of reference products.

50. The system of claim 49, further comprising the at least one processor having instructions for causing the at least one processor to generate the said map of consumer ratings of the said plurality of characteristics of the said fourth plurality of reference products in the said space of a second plurality of dimensions based upon the said map of consumer ratings of the said plurality of characteristics of the said fourth plurality of reference products in the said space of the said third plurality of dimensions.