WO2001052140A1

WO2001052140A1 - System for predicting or determining garment fit

Info

Publication number: WO2001052140A1
Application number: PCT/US2000/034685
Authority: WO
Inventors: Jeff Silverman
Original assignee: Jeff Silverman
Priority date: 1999-12-21
Filing date: 2000-12-21
Publication date: 2001-07-19
Also published as: AU2444401A

Abstract

A method and/or apparatus for determining whether a garment meets a fit requirement of a customer, comprising: searching a database (104) for at least one customer record (112) including at least 3-D coordinates of a fit model corresponding to the fit requirement of the customer (108b); searching the database (104) for at least one brand record (113) for the garment, each brand record including at least 3-D coordinates of a fit model used in producing the garment; comparing the 3-D coordinates of the fit model corresponding to the fit requirement of the customer with the 3-D coordinates of the fit model producing the garment; and determining whether the garment will meet the fit requirement of the customer (108b) based on the comparison.

Description

SYSTEM FOR PREDICTING OR DETERMINING GARMENT FIT

Technical Field

The present invention relates to a system and method for predicting or determining the way a garment will fit a particular customer and, more particularly, the present invention relates to an electronically accessible database containing a plurality of searchable records, the records having information on customer fit characteristics and the fit characteristics of a plurality of brand name garments, where the records are searched to obtain a garment that the customer would wish to purchase. Background Art

Designers /manufacturers of garments and/or shoes (referred to collectively herein as "apparel") understand that in addition to styling and color, the fit of the apparel is paramount in a customer's decision to make a purchase. Indeed, no matter how striking a piece of clothing may be on display, a customer will not purchase the garment unless it meets the customer's personal (i.e., subjective) standards for fit.

Designers /manufacturers of garments utilize a so- called fit model in their design process in an attempt to obtain a pattern for a garment which will meet the fit requirements for a group of potential customers of a certain size. The theory is that if the fit model is chosen properly, the resulting garment will fit most of the potential customers in an appealing way and they will purchase the garment.

The fit model is usually a person selected for their physical build, the build having been determined to represent a particular customer base (i.e., customers of corresponding physical characteristics). The design (e.g., dimensions) of a "mother" garment design is adjusted to the proportions of the fit model such that the mother garment fits the model in a desired way, preferably in a way which matches the expectations of the customer base. A pattern is produced from the mother garment for use in mass producing similar garments for delivery to wholesalers and retailers. The garment pattern is two dimensional (2-D).

The garment pattern is "graded, " a process by which the 2-D pattern is modified to obtain 2-D patterns for various other sizes of interest. For example, if the 2-D pattern corresponding to the fit model produces a pair of trousers for a male, size 32 waist, 34 length, the grading process will produce 2-D patterns for: (i) waist size 34, length 34; (ii) waist size 30, length 32, etc. Those skilled in the art understand that the grading process is somewhat more complicated than simply scaling all dimensions of the 2-D pattern (indeed, belt loops, pocket sizes, button holes, etc. typically do not change from size to size) . In theory, the fit model could be graded such that the designer/manufacturer employs a plurality of fit models used in the design process. In practice, however, this would be highly unusual inasmuch as fit models are quite expensive.

A similar process is utilized in designing shoes, except that a fit model is not used. Rather, a prosthesis called a "last" is developed for the customer base and a pattern is produced from the last.

The use of fit models and/or lasts in mass producing apparel presents a problem for designers/manufacturers because even customers of similar physical build may have differing subjective fit requirements and, thus, prefer to wear different size apparel. Thus, even so-called "standard" sizes fail to attract the number of customers for which the sizes were designed. Retailers are also concerned with this problem inasmuch as they are left with residual inventory if they fail to accurately predict the quantities for each standard size of garment that will be purchased in a given season.

Mail order retailers, electronically networked retailers (e-retailers) and their customers are particularly concerned with this problem because any garment that does not properly fit the purchaser requires substantial effort and cost to return and/or replace.

Moreover, customers are interested in reducing the number of times that they must try on clothing (or shoes) to achieve their personal desires in terms of fit. In order to save time, customers are increasingly demanding the ability to shop from home using mail order services, the internet, etc. Retailers wish to maximize profits by reducing inventories and returned items. Designers/manufacturers are likewise interested in maximizing their profits by producing apparel which will be widely accepted and purchased by customers.

Unfortunately, the inherent disadvantages of the prior art methods of designing and manufacturing garments prevent those methods from satisfying the desires of customers, retailers and designers/manufacturers .

Accordingly, there is a need in the art for a new system and method for influencing the design process of apparel and, additionally, predicting which of the available sizes and/or brands of apparel will satisfy a particular customer's fit requirements.

Summary of the Invention

In order to overcome the disadvantages of the prior art, according to one aspect of the invention an apparatus for determining whether a garment meets a fit requirement of a customer is provided. The apparatus includes: a database including customer records and brand records, each customer record including at least 3-D coordinates of a fit model corresponding to the fit requirement of the customer, and each brand record including at least at least 3-D coordinates of a fit model used in producing the garment; a processing unit operable to execute instructions in accordance with a program; and a memory coupled to the processing unit and operable to store the program.

The instructions of the program cause the processor to perform the following functions: (i) searching the database for at least one customer record for the customer;

(ii) obtaining the 3-D coordinates of the fit model corresponding to the fit requirement of the customer from the customer record;

(iii) searching the database for at least one brand record for the garment;

(iv) obtaining the 3-D coordinates of the fit model used in producing the garment from the brand record; (v) comparing the 3-D coordinates of the fit model corresponding to the fit requirement of the customer with the 3-D coordinates of the fit model used in producing the garment; and

(vi) determining whether the garment will meet the fit requirement of the customer based on the comparison.

According to another aspect of the invention, an apparatus for determining whether a shoe meets a fit requirement of a customer is provided which includes: a database including customer records and brand records, each customer record including at least 3-D coordinates of a last corresponding to the fit requirement of the customer, and each brand record including at least at least 3-D coordinates of a last used in producing the shoe; a processing unit operable to execute instructions in accordance with a program; and a memory coupled to the processing unit and operable to store the program.

The instructions of the program cause the microprocessor to perform the following functions:

(i) searching the database for at least one customer record for the customer;

(ii) obtaining the 3-D coordinates of the last corresponding to the fit requirement of the customer from the customer record;

(iii) searching the database for at least one brand record for the shoe; (iv) obtaining the 3-D coordinates of the last used in producing the shoe from the brand record;

(v) comparing the 3-D coordinates of the last corresponding to the fit requirement of the customer with the 3-D coordinates of the last used in producing the shoe; and

(vi) determining whether the shoe will meet the fit requirement of the customer based on the comparison.

The objects, features and advantages of the present invention will become apparent to those skilled in the art in light of the description herein taken in conjunction with the accompanying drawing. Brief Description of the Drawing

For the purposes of illustrating the invention, there is shown in the drawing a form which is presently preferred, it being understood, however, that the present invention is not limited by the precise arrangement and/or instrumentality shown.

FIG. 1 is a block diagram of a system suitable for carrying out a preferred embodiment of the present invention. Best Mode of Carrying Out Invention

Reference is now made to Fig. 1 , which illustrates a high-level block diagram of an apparel analysis system 10 suitable for use in the present invention. The system 10 includes a processing unit 100, database server 102, database 104 and interface (or network server) 106. The apparel analysis system 10 preferably provides direct services to users 108, including retail sales persons 108A, buyers (retail/wholesale) 108B and designers/manufacturers 108C.

Customers 110, e . g. , the purchasing public, may indirectly communicate with the apparel analysis system 10 inasmuch as they interact with at least some of the users 108, although they may also directly communicate with the system 10.

Any of the known commercially available computer and/or computer network hardware may be utilized in implementing the processing unit 100, database server 102, database 104 and/or interface/network server 106. Those skilled in the art will appreciate that multiple processing units, database servers, databases and interfaces may be utilized in implementing the invention. Equivalently, a single processing unit 100, database server 102, database 104 and interface 106 may be employed without departing the scope of the invention.

Preferably, the database 104 includes two basic sets of records, namely, customer records 112 and brand records 113. The customer records 112 concern the subjective fit requirements of particular customers, while the brand records 113 concern the fit characteristics of commercially available apparel (e.g., garments and/or shoes).

In particular, a customer record 112 is preferably organized in terms of apparel classifications . Apparel classifications as used herein refers to a type of garment, for example, tailored fit, relaxed fit, dress garments, casual garments, shirts (e.g., golf, dress), slacks (e.g., dress, jeans), jackets. This classification recognizes that a particular customer may subjectively require a different fit for a garment in one classification versus another. For example, a particular customer may require that a golf shirt have a looser fit than a dress shirt. Similarly, the customer may require tightly fitting penny loafers versus more loosely fitting sneakers. Each classification of apparel in the customer record

112 preferably includes at least one criteria representing the subjective requirements of that customer concerning fit. It is most preferred that each classification include at least one of: (i) the 3-D coordinates of a fit model (graded or ungraded) corresponding to the subjective requirements of the customer in that classification;

(ii) material characteristics ( e. g. , stretchability: no stretch, minor stretch, high stretch; softness; texture; etc.) representing the customer's requirements in that classification;

(iii) the 2-D pattern characteristics ( e. g. , tightness: neutral, tight, loose; sleeves: sleeveless, long-sleeve, short-sleeve, etc.); and

(iv) the 3-D coordinates of fit models corresponding to the classified garment when the customer has not historically been able to find an acceptable fit.

Those skilled in the art will appreciate from the disclosure herein that the 3-D coordinates of a fit model (whether graded or ungraded) represents a mathematical set of data describing the physical build of the fit model. Preferably, simple Cartesian coordinates, polar coordinates or other known mathematical spatial systems are utilized in obtaining the 3-D coordinates, it being understood that other systems may be utilized without departing from the scope of the invention.

The customer records 112 may also contain classifications of shoes, although the criteria for each classification of shoes preferably includes the 3-D coordinates of the corresponding last (either graded or ungraded) rather than the fit model for a garment.

Preferably, the brand records 113 are organized in terms of particular brand names, manufacturers, etc., which would be recognizable by the customer. The brand records 113 are preferably further organized in terms of classification of apparel in a substantially similar fashion as with the customer records 112. For example, the brand records 113 preferably include a plurality of classifications, such as dress shirts, golf shirts, shorts, slacks, dress slacks, casual slacks, dresses, skirts, blouses, etc. For each classification, a plurality of brand names/manufacturers represent sub-classifications of each classification. For example, under the classification dress shirts, several brand name subclasses may be defined, namely, Ralph Lauren, Eddie Bauer, Arrow, etc. Further, sub-sub- classifications may also be employed, for example, dress shirts, Ralph Lauren, Polo, may specify a particular dress shirt within the Ralph Lauren brand. For each classification and/or sub-classification, the 3-D coordinates of the fit model (graded or ungraded) is preferably recorded, it being understood that the 3-D coordinates of a corresponding last is recorded for shoes.

The operation of a preferred embodiment of the invention will now be described by way of example: A customer wishing to purchase a dress shirt will review the inventory of an apparel shop, for example, a retail clothing store. It is understood that the retail clothing store may represent a physical, brick and mortar storefront, a mail order catalog, an Internet website or the like. When the customer selects a garment (or pair of shoes) that he or she wishes to purchase, the customer presents the garment to a sales clerk (or in the case of mail order/internet sales, identifies the garment by way of telephone, computer instruction, order form, etc.).

For example, if the customer is in a brick and mortar storefront, he may select a Ralph Lauren, Polo, dress shirt of desirable styling and color and present the same to the sales clerk. The customer need not try on the dress shirt or spend any time searching for the correct size. Indeed, the retail sales clerk may access the apparel analysis system 10, for example, by way of network connection through the network server 106 using any of the known techniques. Those skilled in the art will appreciate that the apparel analysis system 10 may be accessed in many other ways, for example, via telephone, via e-mail, via snail mail, etc. The processing unit 100 preferably requests that the retail clerk 108 provide certain information regarding the particular customer, namely, the customer name (or number) and the identification of the garment at issue ( e . g. , by brand name, etc.) . Those skilled in the art will appreciate that the interaction/communication between the apparel analysis system 10 and retail sales clerk 108A may be achieved in many ways, for example, browser-based electronic screens having drop-down menus, icons, input dialog boxes, etc .

Once the customer 110 and the garment of interest have been identified, the processing unit 100 preferably formats the information for a database search of the customer records 112 and brand records 113. In particular, the customer identification ( e . g. , customer number) is utilized to search the database 104 to obtain the customer record 112 for that particular customer. The classification identifier ( e . g. , dress shirt) is utilized by the processing unit 100 to search the customer record 112 to obtain the one or more criteria under the dress shirt classification, namely, the 3-D coordinates of the fit model (graded or ungraded) corresponding with the fit requirements for that customer. As discussed above, other criteria may also be obtained from the customer record 112 for the dress shirt classification, such as material characteristics, 2-D pattern characteristics, etc.

The processing unit 100 accesses the database 104 utilizing the garment identification data to obtain a brand record 113 concerning that garment. The processing unit 100 compares the 3-D coordinates of the fit for the brand of garment (obtained from the brand record 113) with the 3-D coordinates of the fit model obtained from the customer record 112. When the 3-D coordinates of the fit model from the customer record 112 matches (e.g., corresponds to) one of the 3-D coordinates of the fit models obtained from the brand record 113, then the processing unit 100 maps the 3-D coordinates of the fit model from the brand record 113 with a size or other garment identifier recognizable by the retail sales clerk 108A and/or customer 110. Accordingly, the retail sales clerk 108A obtains the size of the Ralph Lauren, Polo, dress shirt having a fit which substantially meet the fit requirements for the customer 110. If the 3-D coordinates of the fit model taken from the customer record 112 cannot be matched with any of the 3-D coordinates of the fit models for the identified brand of dress shirt, then it is preferred that the processing unit 100 perform a broader search of other brands within the classification to obtain potential matches with other brands of dress shirts similar to the identified dress shirt provided by the customer. In searching for other brands of dress shirt, the processing unit 110 may utilize other criteria within the classification, such as material characteristics, 2-D pattern characteristics, etc. Accordingly, the retail sales clerk 108A may suggest to the customer 110 that it is unlikely that he will find a Ralph Lauren, Polo, dress shirt which will fit to his liking and recommend another brand which will likely meet the customer's fit requirements.

Alternatively, buyers 108B of apparel may access the apparel analysis system 10 to obtain access to the fit criteria contained within the customer records 112. This criteria may be organized by the processing unit 100 in a way which provides useful information to the buyer 108B. For example, information as to the most popular 3-D coordinates of the fit model for a men's dress shirt may be obtained by the buyer 108B. This information may be categorized in terms of region, season, age, etc. if such variables effect the popularity of the fit criteria. Advantageously, the buyers 108B may utilize this information in determining what quantities and what sizes should be purchased to present to retail customers 110.

Still further, designers/manufacturers 108C, such as Ralph Lauren, may access the fit criteria contained within the customer records 112 to determine whether there are a substantial number of customers 110 who do not currently purchase Ralph Lauren dress shirts because they cannot find a size which meets their fit requirements. The designer/manufacturer 108C could advantageously utilize this information in determining whether a new dress shirt size should be introduced into the marketplace to expand their market share .

Those skilled in the art will appreciate that the advantages of the preferred embodiment of the apparel analysis system 10 is a function of the completeness of the customer records 112 and brand records 113. It is desirable, therefore, to obtain as much fit criteria information from the customer 110 as possible and to update this data on an ongoing basis. This information may be obtained in any number of ways. For example, when a customer 110 purchases a piece of apparel from a mail order establishment or via e-commerce and the customer does not return the piece of apparel, a feedback path for customer data preferably exists directly from the customer 110 or from the retail establishment 108A to the apparel analysis system 10. The customer data preferably includes the customer identification ( e . g. , customer number), classification of apparel, brand name, subclassifications (if any) , material characteristics, 2-D pattern characteristics, and 3-D coordinates of the corresponding fit model or last. This customer data is then preferably stored in the database 104 in the appropriate customer record 112.

Similarly, if the customer 110 returns a piece of apparel, a questionnaire (verbal, via hard copy, via computer, etc . ) is preferably obtained from the customer to determine why the piece of apparel was returned. For example, if the customer 110 states that he or she did not like the type of material, he or she did not like the fit, and/or he or she could not locate any size in which the piece of apparel was available, the customer data is fed into the apparel analysis system 10 and ultimately stored in the database 104 within an appropriate customer record 112.

Alternatively, customers may be asked to inventory the clothes that they already own so that information as to classification and fit criteria may be stored in the database 104 for that customer. When the customer 110 purchases a piece of apparel, for example, at a brick and mortar storefront, the retail sales clerk 108 may scan a UPC code which identifies the piece of apparel and provides all necessary fit criteria to the apparel analysis system 10 by way of electronic interconnection with the interface 106. Thus, the act of purchasing the piece of apparel may automatically trigger the updating of the database 104 for the customer 110.

Those skilled in the art will appreciate many other systems and methods for obtaining fit criteria from customers 110 and updating the database 104 may be utilized without departing from the scope of the invention.

In some instances, a customer 110 may wish to purchase a piece of apparel and present the same to, for example, a retail sales clerk 108 at a brick and mortar storefront. In accordance with the invention, the retail sales clerk 108 enters the customer identification and apparel identification into the apparel analysis system as described above, when the processing unit 100 obtains the customer record 112 for that customer 110, it is possible that very little data is available as to the fit criteria for the classification of apparel presented. For example, the customer 110 may have presented a dress shirt to the retail sales person 108A and asked what size should be purchased. If the customer record 112 for that customer 110 contains little or no fit criteria for the dress shirt classification, then it is preferred that the processing unit 100 attempt to search for a so-called "subjective clone" of the customer 110. The subjective clone may be another customer within the database 104 having similar fit criteria (albeit in other classifications) as the instant customer. The subjective clone's fit criteria for the classification: dress shirt may thus be used to service the instant customer. Advantageously, the retail sales clerk 108A may suggest a size dress shirt for the customer 110 even though little or no fit criteria is contained in the customer record 112 under that classification. Alternatively, marketing tests may be executed in which a plurality of apparel items are sent to test customers to determine which of the articles the test customers would prefer to purchase. The fit criteria obtained from the test marketing trials may be utilized in building the database 104. Industrial Applicability Although the invention herein has been described with reference to a particular embodiment, it is to be understood that the embodiment is merely illustrative of the principles and application of the present invention. It is therefore to be understood that numerous modifications and variations may be made to the embodiment and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the claims .

Claims

1. An apparatus for determining whether a garment meets a fit requirement of a customer, comprising: a database including customer records and brand records, each customer record including at least 3-D coordinates of a fit model corresponding to the fit requirement of the customer, and each brand record including at least at least 3-D coordinates of a fit model used in producing the garment; a processing unit operable to execute instructions in accordance with a program; and a memory coupled to the processing unit and operable to store the program, the instructions of the program causing the processor to perform the following functions : (i) searching the database for at least one customer record for the customer;

(ii) obtaining the 3-D coordinates of the fit model corresponding to the fit requirement of the customer from the customer record; (iii) searching the database for at least one brand record for the garment;

(iv) obtaining the 3-D coordinates of the fit model used in producing the garment from the brand record;

(v) comparing the 3-D coordinates of the fit model corresponding to the fit requirement of the customer with the 3-D coordinates of the fit model used in producing the garment; and

(vi) determining whether the garment will meet the fit requirement of the customer based oiϊ the comparison.

2. The apparatus of claim 1, wherein the customer records are organized in terms of apparel classifications, the apparel classifications being a type of garment.

3. The apparatus of claim 2, wherein the type of garment includes tailored fit, relaxed fit, dress garments, casual garments, shirts, golf shirts, dress shirts, slacks, dress slacks, jeans, jackets.

4. The apparatus of claim 2 , wherein each classification includes at least one criteria representing the fit requirement of the customer.

5. The apparatus of claim 4, wherein the at least one criteria includes at least one of: (i) the 3-D coordinates of a fit model corresponding to the fit requirement in the given classification; (ii) the 3-D coordinates of a fit model that are graded and that correspond to the fit requirement in the given classification; (iii) stretchability, including at least one of no stretch, minor stretch, and high stretch; (iv) texture; (v) softness; (vi) color; (vii) pattern; (viii) 2-D pattern characteristics; (ix) tightness, including at least one of neutral, tight, loose; (x) sleeve characteristics, including at least one of sleeveless, long-sleeve, short-sleeve; and (xi) 3-D coordinates of at least one fit model corresponding to the given classification when the customer has not previously been able to find an acceptable fit.

6. The apparatus of claim 5, wherein the 3-D coordinates of a fit model corresponds to a mathematical set of data defining a physical build of the fit model.

7. The apparatus of claim 6, wherein the mathematical set of data is expressed in at least one of

Cartesian coordinates and polar coordinates

8. The apparatus of claim 1, wherein the brand records are organized in terms of apparel classifications, the apparel classifications being a type of garment.

9. The apparatus of claim 8, wherein the type of garment includes at least one of tailored fit, relaxed fit, dress garments, casual garments, shirts, golf shirts, dress shirts, slacks, dress slacks, casual slacks, jeans, jackets, dresses, skirts, and blouses.

10. The apparatus of claim 9, wherein the brand records are further organized in terms of at least one of brands and manufacturers .

11. The apparatus of claim 10, wherein the brand records include a 3-D coordinates of a fit model corresponding the fit model used to produce the apparel of the given classification.

12. The apparatus of claim 11, wherein the 3-D coordinates of the fit model corresponds to a mathematical set of data defining a physical build of the fit model.

13. The apparatus of claim 12, wherein the mathematical set of data is expressed in at least one of Cartesian coordinates and polar coordinates

14. A method for determining whether a garment meets a fit requirement of a customer, comprising: receiving information from the customer identifying the garment; searching a database including customer records and brand records, each customer record including at least 3-D coordinates of a fit model corresponding to the fit requirement of the customer, and each brand record including at least at least 3-D coordinates of a fit model used in producing the garment; obtaining the 3-D coordinates of the fit model corresponding to the fit requirement of the customer from at least one of the customer record; obtaining the 3-D coordinates of the fit model used in producing the garment from at least one of the brand record; comparing the 3-D coordinates of the fit model corresponding to the fit requirement of the customer with the 3-D coordinates of the fit model used in producing the garment; and determining whether the garment will meet the fit requirement of the customer based on the comparison.

15. The method of claim 14, wherein the customer records are organized in terms of apparel classifications, the apparel classifications being a type of garment.

16. The method of claim 15, wherein the type of garment includes tailored fit, relaxed fit, dress garments, casual garments, shirts, golf shirts, dress shirts, slacks, dress slacks, jeans, jackets.

17. The method of claim 15, wherein each classification includes at least one criteria representing the fit requirement of the customer.

18. The method of claim 17, wherein the at least one criteria includes at least one of: (i) the 3-D coordinates of a fit model corresponding to the fit requirement in the given classification; (ii) the 3-D coordinates of a fit model that are graded and that correspond to the fit requirement in the given classification; (iii) stretchability, including at least one of no stretch, minor stretch, and high stretch; (iv) texture; (v) softness; (vi) color; (vii) pattern; (viii) 2-D pattern characteristics; (ix) tightness, including at least one of neutral, tight, loose; (x) sleeve characteristics, including at least one of sleeveless, long-sleeve, short-sleeve; and (xi) 3-D coordinates of at least one fit model corresponding to the given classification when the customer has not previously been able to find an acceptable fit.

19. The method of claim 18, wherein the 3-D coordinates of a fit model corresponds to a mathematical set of data defining a physical build of the fit model .

20. The method of claim 19, wherein the mathematical set of data is expressed in at least one of Cartesian coordinates and polar coordinates

21. The method of claim 14, wherein the brand records are organized in terms of apparel classifications, the apparel classifications being a type of garment.

22. The method of claim 21, wherein the type of garment includes at least one of tailored fit, relaxed fit, dress garments, casual garments, shirts, golf shirts, dress shirts, slacks, dress slacks, casual slacks, jeans, jackets, dresses, skirts, and blouses.

23. The method of claim 22, wherein the brand records are further organized in terms of at least one of brands and manufacturers .

24. The method of claim 23, wherein the brand records include a 3-D coordinates of a fit model corresponding the fit model used to produce the apparel of the given classification.

25. The method of claim 24, wherein the 3-D coordinates of the fit model corresponds to a mathematical set of data defining a physical build of the fit model.

26. The method of claim 25, wherein the mathematical set of data is expressed in at least one of Cartesian coordinates and polar coordinates

27. The method of claim 14, further comprising comparing the 3-D coordinates of the fit model corresponding to the fit requirement of the customer with 3-D coordinates of at least one other fit model used in producing at least one substitute garment to determine whether the at least one substitute garment will meet the fit requirement of the customer .

28. The method of claim 27, wherein the garment is of a first brand and the at least one substitute garment but is of a second brand.

29. The method of claim 28, further comprising recommending that the customer purchase the substitute garment of the second brand rather than the garment of the first brand based on the comparison of the 3-D coordinates of the fit model corresponding to the fit requirement of the customer with 3-D coordinates of the at least one other fit model used in producing the at least one substitute garment.

30. The method of claim 14, further comprising: searching the database for at least one of the 3-D coordinates of the fit model corresponding to the fit requirement of the customer; and providing the 3-D coordinates of the fit model corresponding to the fit requirement of the customer to a buyer such that buyer may base its purchase thereon.

31. The method of claim 14, further comprising: searching the database for at least one of the 3-D coordinates of the fit model corresponding to the fit requirement of the customer; and providing the 3-D coordinates of the fit model corresponding to the fit requirement of the customer to a manufacturer such that manufacturer may base its manufacturing plan thereon.

32. A method for at least one of producing and maintaining a database, comprising: receiving data concerning a garment based on customer information; and storing the data in the database, the database including customer records and brand records, each customer record including at least 3-D coordinates of a fit model corresponding to a fit requirement of the customer, and each brand record including at least at least 3-D coordinates of a fit model used in producing the garment.

33. The method of claim 32, wherein the data is obtained at least one of (i) when the customer purchases the garment, (ii) when the customer returns the garment to a place of purchase, (iii) when the customer responds to a questionnaire, and (iv) when the customer responds to an inventory request .

34. The method of claim 33, wherein the customer records are organized in terms of apparel classifications, the apparel classifications being a type of garment.

35. The method of claim 34, wherein the type of garment includes tailored fit, relaxed fit, dress garments, casual garments, shirts, golf shirts, dress shirts, slacks, dress slacks, jeans, jackets.

36. The method of claim 34, wherein each classification includes at least one criteria representing the fit requirement of the customer.

37. The method of claim 36, wherein the at least one criteria includes at least one of: (i) the 3-D coordinates of a fit model corresponding to the fit requirement in the given classification; (ii) the 3-D coordinates of a fit model that are graded and that correspond to the fit requirement in the given classification; (iii) stretchability, including at least one of no stretch, minor stretch, and high stretch; (iv) texture; (v) softness; (vi) color; (vii) pattern; (viii) 2-D pattern characteristics; (ix) tightness, including at least one of neutral, tight, loose; (x) sleeve characteristics, including at least one of sleeveless, long-sleeve, short-sleeve; and (xi) 3-D coordinates of at least one fit model corresponding to the given classification when the customer has not previously been able to find an acceptable fit.

38. The method of claim 37, wherein the 3-D coordinates of a fit model corresponds to a mathematical set of data defining a physical build of the fit model .

39. The method of claim 38, wherein the mathematical set of data is expressed in at least one of Cartesian coordinates and polar coordinates

40. The method of claim 32, wherein the brand records are organized in terms of apparel classifications, the apparel classifications being a type of garment.

41. The method of claim 40, wherein the type of garment includes at least one of tailored fit, relaxed fit, dress garments, casual garments, shirts, golf shirts, dress shirts, slacks, dress slacks, casual slacks, jeans, jackets, dresses, skirts, and blouses.

42. The method of claim 41, wherein the brand records are further organized in terms of at least one of brands and manufacturers .

43. The method of claim 42, wherein the brand records include a 3-D coordinates of a fit model corresponding the fit model used to produce the apparel of the given classification.

44. The method of claim 43, wherein the 3-D coordinates of the fit model corresponds to a mathematical set of data defining a physical build of the fit model.

45. The method of claim 44, wherein the mathematical set of data is expressed in at least one of Cartesian coordinates and polar coordinates