SYSTEM AND METHOD FOR PROVIDING CUSTOMERS WITH PERSONALIZED INFORMATION ABOUT PRODUCTS

• US 20110099122A1
• Filed: 10/21/2010
• Published: 04/28/2011
• Est. Priority Date: 10/23/2009
• Status: Active Grant

• Current Assignee: True Fit Corp.
• Original Assignee: True Fit Corp.
• Inventors: Bright, Douglas R., Murphy, Jessica Arredondo, Evans, Joseph Romney
• Granted Patent: US 8,762,292 B2
• Time in Patent Office: Days
• US Class Current: 705/348
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

Embodiments of the invention may provide a personalized fit prediction to a particular consumer about a particular product. Personalization of the fit prediction may be accomplished by processing various types of data, including data about consumers, products, and previous purchases by consumers. For example, this data may be used to generate a model (e.g., set of rules), which can then be applied to predict whether and/or how well a particular product will suit a particular consumer.

Claims

1. A method for predicting whether a particular item will suit a particular consumer, the method comprising:

(A) receiving data about a population of consumers and a population of items;

(B) using the data received in (A), creating a model for use in predicting whether certain of the population of items will suit certain of the population of consumers; and

(C) using the model created in (B), predicting whether the particular item will suit the particular consumer.

2. The method of claim 1, wherein (C) comprises predicting whether the particular item is an appropriate size and/or shape for the particular consumer.

3. The method of claim 1, wherein the particular item is an item of apparel or footwear.

4. The method of claim 1, wherein the population of consumers comprises the particular consumer.

5. The method of claim 1, wherein the population of items comprises the particular item.

6. The method of claim 1, wherein the data received about the population of consumers in (A) comprises any of data useful for inferring body measurements of one or more of the population of consumers, data concerning style preferences of one or more of the population of consumers, web browsing history of one or more of the population of consumers, information on brands, styles, apparel or footwear that fit of one or more of the population of consumers well, demographics of one or more of the population of consumers, and gender of one or more of the population of consumers.

7. The method of claim 1, wherein the data received about the population of items in (A) comprises any of data useful for inferring measurements of one or more of the items and data concerning one or more of the items in stretched and relaxed states.

8. The method of claim 1, wherein (B) comprises creating a decision tree, neural network, support vector machine or regression model.

9. The method of claim 1, wherein (C) comprises predicting how well the particular to item will suit the particular consumer.

10. The method of claim 1, further comprising:

(D) causing a result of the predicting to be displayed to the particular consumer.

11. The method of claim 10, wherein (D) comprises causing a numeric fit score to be displayed to the particular consumer.

12. The method of claim 1, further comprising:

(E) receiving feedback from the particular consumer regarding the particular item;

(F) using the data received in (E), recreating the model;

(G) using the model recreated in (F), predicting whether a certain item will suit the particular consumer.

13. The method of claim 1, wherein (A) comprises producing one or more metrics relating data about certain of the population of consumers to certain of the population of items.

14. The method of claim 1, wherein (A) does not comprise receiving data regarding body measurements of the population of consumers.

15. A tangible computer-readable storage medium having recorded thereon instructions which, when executed, perform a method for predicting whether a particular item will suit a particular consumer, the method comprising:

(A) receiving data about a population of consumers and a population of items;

(B) using the data received in (A), creating a model for use in predicting whether certain of the population of items will suit certain of the population of consumers; and

(C) using the model created in (B), predicting whether the particular item will suit the particular consumer.

16. The tangible computer-readable storage medium of claim 15, wherein (C) comprises predicting whether the particular item is an appropriate size and/or shape for the particular consumer.

17. The tangible computer-readable storage medium of claim 15, wherein the particular item is an item of apparel or footwear.

18. The tangible computer-readable storage medium of claim 15, wherein the population of consumers comprises the particular consumer.

19. The tangible computer-readable storage medium of claim 15, wherein the population of items comprises the particular item.

20. The tangible computer-readable storage medium of claim 15, wherein the data received about the population of consumers in (A) comprises any of data useful for inferring body measurements of one or more of the population of consumers, data concerning style preferences of one or more of the population of consumers, web browsing history of one or more of the population of consumers, information on brands, styles, apparel or footwear that fit of one or more of the population of consumers well, demographics of one or more of the population of consumers, and gender of one or more of the population of consumers.

21. The tangible computer-readable storage medium of claim 15, wherein the data received about the population of items in (A) comprises any of data useful for inferring measurements of one or more of the items and data concerning one or more of the items in stretched and relaxed states.

22. The tangible computer-readable storage medium of claim 15, wherein (B) comprises creating a decision tree, neural network, support vector machine or regression model.

23. The tangible computer-readable storage medium of claim 15, wherein (C) comprises predicting how well the particular item will suit the particular consumer.

24. The tangible computer-readable storage medium of claim 15, further comprising:

(D) causing a result of the predicting to be displayed to the particular consumer.

25. The tangible computer-readable storage medium of claim 24, wherein (D) comprises causing a numeric fit score to be displayed to the particular consumer.

26. The tangible computer-readable storage medium of claim 15, further comprising:

(E) receiving feedback from the particular consumer regarding the particular item;

(F) using the data received in (E), recreating the model;

(G) using the model recreated in (F), predicting whether a certain item will suit the particular consumer.

27. The tangible computer-readable storage medium of claim 15, wherein (A) comprises producing one or more metrics relating data about certain of the population of consumers to certain of the population of items.

28. The tangible computer-readable storage medium of claim 15, wherein (A) does not comprise receiving data regarding body measurements of the population of consumers.

29. A system for predicting whether a particular item will suit a particular consumer, the system comprising at least one microprocessor programmed to:

(A) receive data about a population of consumers and a population of items;

(B) using the data received in (A), create a model for use in predicting whether certain of the population of items will suit certain of the population of consumers; and

(C) using the model created in (B), predict whether the particular item will suit the particular consumer.

30. The system of claim 29, wherein (C) comprises predicting whether the particular item is an appropriate size and/or shape for the particular consumer.

31. The system of claim 29, wherein the particular item is an item of apparel or footwear.

32. The system of claim 29, wherein the population of consumers comprises the particular consumer.

33. The system of claim 29, wherein the population of items comprises the particular item.

34. The system of claim 29, wherein the data received about the population of consumers in (A) comprises any of data useful for inferring body measurements of one to or more of the population of consumers, data concerning style preferences of one or more of the population of consumers, web browsing history of one or more of the population of consumers, information on brands, styles, apparel or footwear that fit of one or more of the population of consumers well, demographics of one or more of the population of consumers, and gender of one or more of the population of consumers.

35. The system of claim 29, wherein the data received about the population of items in (A) comprises any of data useful for inferring measurements of one or more of the items and data concerning one or more of the items in stretched and relaxed states.

36. The system of claim 29, wherein (B) comprises creating a decision tree, neural network, support vector machine or regression model.

37. The system of claim 29, wherein (C) comprises predicting how well the particular item will suit the particular consumer.

38. The system of claim 29, further comprising:

(D) causing a result of the predicting to be displayed to the particular consumer.

39. The system of claim 38, wherein (D) comprises causing a numeric fit score to be displayed to the particular consumer.

40. The system of claim 29, further comprising:

(E) receiving feedback from the particular consumer regarding the particular item;

(F) using the data received in (E), recreating the model;

(G) using the model recreated in (F), predicting whether a certain item will suit the particular consumer.

41. The system of claim 29, wherein (A) comprises producing one or more metrics relating data about certain of the population of consumers to certain of the population of items.

42. The system of claim 29, wherein (A) does not comprise receiving data regarding body measurements of the population of consumers.

43. A method of predicting whether a particular item will suit a particular consumer, the method comprising:

(A) receiving a plurality of heuristics, each heuristic describing a characteristic of the particular consumer or a characteristic of the particular item;

(B) using the plurality of heuristics received in (A), generating a plurality of fit scores, each fit score predicting whether the particular item will fit the particular consumer; and

(C) combining the plurality of fit scores into an overall fit score predicting whether the particular item will suit the particular consumer.

44. The method of claim 43, wherein (B) comprises comparing a first heuristic describing a characteristic of the particular consumer with a second heuristic describing a characteristic of the particular item.

45. The method of claim 44, wherein the first heuristic relates to a size of a body region of the particular consumer.

46. The method of claim 44, wherein the second heuristic relates to a size of a region to of the particular item.

47. The method of claim 44, wherein the first heuristic relates to a preference of the particular consumer with respect to whether an item fits loosely or tightly, and the second heuristic relates to a how the particular item is intended to fit.

48. The method of claim 43, wherein (A) further comprises receiving results of a measurement fit algorithm employing at least one body measurement of the particular consumer and at least one measurement of the particular item, and (B) comprises generating one or more of the plurality of fit scores by modifying the results of the measurement fit algorithm.

49. The method of claim 43, wherein (C) comprises computing an average of the plurality of fit scores, computing a mean of the plurality of fit scores, identifying a lowest of the plurality of fit scores, and identifying a highest of the plurality of fit scores.

50. The method of claim 43, further comprising:

(D) causing the overall fit score to be displayed to the particular consumer.

51. The method of claim 43, wherein the particular item is an item of apparel or footwear.

52. A tangible computer-readable storage medium having instructions recorded thereon which, when executed, perform a method of predicting whether a particular item will suit a particular consumer, the method comprising:

(A) receiving a plurality of heuristics, each heuristic describing a characteristic of the particular consumer or a characteristic of the particular item;

(B) using the plurality of heuristics received in (A), generating a plurality of fit scores, each fit score predicting whether the particular item will fit the particular consumer; and

(C) combining the plurality of fit scores into an overall fit score predicting whether the particular item will suit the particular consumer.

53. The tangible computer-readable storage medium of claim 52, wherein (B) comprises comparing a first heuristic describing a characteristic of the particular consumer with a second heuristic describing a characteristic of the particular item.

54. The tangible computer-readable storage medium of claim 53, wherein the first heuristic relates to a size of a body region of the particular consumer.

55. The tangible computer-readable storage medium of claim 53, wherein the second heuristic relates to a size of a region of the particular item.

56. The tangible computer-readable storage medium of claim 53, wherein the first heuristic relates to a preference of the particular consumer with respect to whether an item fits loosely or tightly, and the second heuristic relates to a how the particular item is intended to fit.

57. The tangible computer-readable storage medium of claim 52, wherein (A) further comprises receiving results of a measurement fit algorithm employing at least one body measurement of the particular consumer and at least one measurement of the particular item, and (B) comprises generating one or more of the plurality of fit scores by modifying the results of the measurement fit algorithm.

58. The tangible computer-readable storage medium of claim 52, wherein (C) comprises computing an average of the plurality of fit scores, computing a mean of the plurality of fit scores, identifying a lowest of the plurality of fit scores, and identifying a highest of the plurality of fit scores.

59. The tangible computer-readable storage medium of claim 52, further comprising:

(D) causing the overall fit score to be displayed to the particular consumer.

60. The tangible computer-readable storage medium of claim 52, wherein the particular item is an item of apparel or footwear.

61. A system for predicting whether a particular item will suit a particular consumer, the system comprising at least one microprocessor programmed to:

(A) receive a plurality of heuristics, each heuristic describing a characteristic of the particular consumer or a characteristic of the particular item;

(B) using the plurality of heuristics received in (A), generate a plurality of fit scores, each fit score predicting whether the particular item will fit the particular consumer; and

(C) combining the plurality of fit scores generated in (B) into an overall fit score predicting whether the particular item will suit the particular consumer.

62. The system of claim 61, wherein (B) comprises comparing a first heuristic describing a characteristic of the particular consumer with a second heuristic describing a characteristic of the particular item.

63. The system of claim 62, wherein the first heuristic relates to a size of a body region of the particular consumer.

64. The system of claim 62, wherein the second heuristic relates to a size of a region of the particular item.

65. The system of claim 62, wherein the first heuristic relates to a preference of the particular consumer with respect to whether an item fits loosely or tightly, and the second heuristic relates to a how the particular item is intended to fit.

66. The system of claim 61, wherein (A) further comprises receiving results of a measurement fit algorithm employing at least one body measurement of the particular consumer and at least one measurement of the particular item, and (B) comprises generating one or more of the plurality of fit scores by modifying the results of the measurement fit algorithm.

67. The system of claim 61, wherein (C) comprises computing an average of the plurality of fit scores, computing a mean of the plurality of fit scores, identifying a lowest of the plurality of fit scores, and identifying a highest of the plurality of fit scores.

68. The system of claim 61, further comprising:

(D) causing the overall fit score to be displayed to the particular consumer.

69. The system of claim 61, wherein the particular item is an item of apparel or footwear.