fig1 illustrates the method of the present invention as applied to a shoe store setting offering both in store and on - line shopping . the skilled person will appreciate however that the present invention is applicable to all types of footwear or clothing sales and rental and indeed to any articles which need correct fitting to users . also there will be other retail solutions which utilise the method of the present invention . an in store customer wishing to purchase some shoes would browse for shoes they like and decide which shoes they would like to try . having identified shoes they would like to try a foot data file for that customer would be obtained . if there were no pre - existing foot data file for that customer this would involve having their feet scanned 2 by a three dimensional foot scanner 4 . the foot scanner 4 comprises at least one optical scanning device such as described in wo2004 / 044525 . for each scanning device a spot projector projects an array of spots of light onto the customer &# 39 ; s foot . a camera is arranged relative to the spot projector such that the position of the spots in the scene captured by the camera can be used to determine the range to that spot . the entire foot is illuminated and imaged , either by scanning the device relative to the foot or by taking multiple scans of the foot in different orientations . both feet are scanned and a three dimensional point map of the surface of each foot is constructed . the skilled person will appreciate however that a variety of other scanning technologies could be employed , for instance the foot scanner described in u . s . pat . no . 5 , 164 , 793 and the invention is not limited to any particular form of foot scan . the foot scanner 4 may process the point map data to determine key dimensions of the foot , such as heel to toe length , width at widest part , heel width etc . and produce a customer foot data file or may simply maintain the 3d shape as the customer foot data file . the foot data file is stored in the foot scanner 4 for now along with a means of identifying the customer to which the data relates . this could be by allocation of a customer number , time of scan and / or addition of metadata supplied by the customer such the customer &# 39 ; s name . for any particular model of shoe the customer is interested in the foot data file is then compared to a shoe database 6 comprising information of reference foot size data of other people known to have bought the same model of shoe . a matching algorithm is applied to match the current customer &# 39 ; s foot size data with the reference foot size data in the shoe database 6 . if a match is identified the relevant shoe size associated with the reference foot size data is obtained . if no match is identified because the database does not contain sufficient data about the chosen model of shoe the fitting is instead performed by simply converting the shoe data file into length and width measurements and determining a notional shoe size from the manufacturer &# 39 ; s shoe sizing table . it should be noted that in some circumstances the shoe of interest to the customer is not available in a size that would fit and , if this is the case , this fact is communicated to the customer . also , the fit determined by the matching algorithm or look up table may need to be adjusted for factors specific to the user such as preferred sock thickness or preferred type of fit ( snug or roomy ). this could be applied by a scaling factor applied to the customer foot data file prior to matching or by applying a compensation algorithm after an initial match has been determined . a constant offset could also be applied . the fit recommendation is communicated to the customer , for instance by means of a printed recommendation slip . this slip may be provided with a barcode or other identifier which links to the customers foot data file . the customer would then try on 8 the size of shoe recommended . if the fit is correct and the customer is happy with the shoe and wishes to purchase the item they go on to purchase the shoes 10 as normal . whilst completing the transaction the barcode identifying the shoes is scanned by the shops electronic point of sale equipment ( epos ) 12 as is the barcode printed on the size recommendation provided to the customer . this therefore identifies the make and model of shoe bought with the customer &# 39 ; s foot data file . the fact that the customer is buying the shoes indicates that the shoes fit that customer and hence the shoe model and size information is collated 14 with the customer &# 39 ; s foot data file and communicated to the shoe data base 6 . the shoe database 6 may be in store but preferably a central database is used by a variety of shoe stores and accessed , for instance , via the internet 16 . in this way purchase from each of the stores automatically adds to the information in the shoe database which in turn improves the accuracy of the system . the customer &# 39 ; s foot data file may also be stored separately for ease of access for the customer in future use , especially via the internet . additionally or alternatively it may be written to a removable storage medium to be taken by the customer or sent wirelessly to a date storage device of the customer such as a suitable mobile phone or personal data assistant . the customer may then get to keep their own foot data file . the customer &# 39 ; s foot data may also be incorporated into the customer reference number and barcode , so that the printed sizing receipt becomes the stored data file . the retail epos system , when scanning the receipt , inputs and stores the customer &# 39 ; s size information . the customer , when undertaking a remote transaction for further shoes , enters the customer reference number from the receipt and in doing so provides the fitting system with their relevant measurements . in this situation there is no need to connect each foot scanner to a network or have other means to store and download data files . the sizing information may be encrypted within the customer reference number for commercial and data security reasons . a customer who had previously had a foot scan could then use such data file in assuring fit in on - line shopping 18 . the customer would enter their unique customer number or the metadata taken in the shop to locate their foot data file . alternatively they could download it onto an appropriate computer . the customer could then browse on - line for shoes that they are interested in . once they have chosen the shoes they are interested in the foot data file could be compared to the shoe database 6 exactly as described previously and obtain a size recommendation . if the customer is happy to proceed the order could be placed on this basis . if subsequent the customer did not return the shoes within the allowed time for returns this could be taken as an indication they were happy with the fit and this data could be added to the shoe database . as an alternative or in addition the system could recommend shoes to the customer that they have not selected but which are known would fit the customer &# 39 ; s feet . when recommending shoes the system may use shoes that the customer is interested in as a guide . that is if a customer is browsing for a particular style of shoes the system may recommend other shoes of a similar style . the system may also use information regarding the customer such as gender , age , shoe style preference etc . which may for instance be supplied by and customer and / or stored in a customer profile , to make sure the recommendations are appropriate . the present invention therefore offers several advantages , over conventional shoe fitting techniques . the scanner fitting performance is continuously refined and improved based on what people find comfortable when they purchase shoes in shops . where expert fitters in the shop help customers to select the correct shoe size , this information will also be automatically accumulated and integrated into the fit recommendation . the system will automatically incorporate and optimise fit recommendation for any brand and style of shoe without needing access to shoe lasts , measuring shoes , or doing controlled fitting trials for each brand or style of shoe . the scanner and database will quickly and automatically learn the fitting performance of new shoe styles which are introduced into the marketplace . the scanners and database will also automatically learn what are the correct ‘ comfort ’ factors for different types of shoes e . g . children &# 39 ; s , running etc . foot size and shoe sale data can be automatically analysed and provided back to the retailer to optimise business efficiency , trend analysis , stock control etc . software can analyse the effect of the person having two differently sized feet and the resultant preferred shoe size , and optimise fitting algorithms for these situations . following the launch of a new range of shoes in shops and on - line , there will be a short period when the scanner system initially applies an estimated or default sizing information to determine best fit . once data on the fit performance of this new shoe range starts to be received by the system database , a judgement on the fit accuracy can be determined and new ‘ comfort ’ compensation factors applied and distributed back to the foot scanners . this process will be iterated until the fit prediction consistently matches the size of shoe purchased . the fit prediction can then become available for on - line shoe sales . this optimisation process may take around 100 shoe sales for a particular style and so is likely to stabilise very quickly , particularly if there are a number of shoe shops connected to the system . the foot shape can be established using 3d feet shape data or a set of published or undisclosed measurement parameters . each of the parameters can have a unique ‘ comfort ’ factor applied for each style or brand of shoe . it is likely these parameters will adopt standard foot metrics used by feet specialists , and so make this foot information valuable to shoe designers and biometric surveys . comfort compensation factors can be developed and refined for different customer types through a customer survey / data input e . g . gender , intended use of footwear , ethnic origin etc . each of these parameters could alter the type of fit compensation applied to the shoe and therefore further improve quality of fit . customers who don &# 39 ; t wish to participate would receive a more generic fit recommendation . fit performance could be ultimately tailored to individuals e . g . customer x prefers tighter fitting shoes than the average . this could be established by monitoring the size mapping of that customer compared to the average . analysis of individual fit parameters could indicate e . g . that the customer has a narrow foot or prefers more volume around the toe . the fit mapping would be automatically tuned for specialist sports footwear e . g . running , golf , hiking etc . input of sock type when the foot was scanned could be used to build up accurate compensation for different sock types .
the invention relates to an improved method of providing customers with indications of whether certain articles , for instance footwear or clothing , will fit that customer and to making recommendations for articles that will fit . the method takes body size data regarding the size / shape of the appropriate body part of the customer and compares the data against a reference body size / shape associated with an article . the reference body size is obtained from the recorded body sizes of previous customers who are known to fit the particular article . a match between the current customer &# 39 ; s body size and the reference body size for an article is an indication that such article will fit that customer . the present invention thus avoids any need to obtain any size information about the article itself and can generate the reference body size data through normal retail practices . the invention is particularly applicable to the fitting of shoes .