WO2009138879A2 - Système et procédé pour prédiction d'adaptation et recommandation de chaussure et de vêtement - Google Patents

Système et procédé pour prédiction d'adaptation et recommandation de chaussure et de vêtement Download PDF

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WO2009138879A2
WO2009138879A2 PCT/IB2009/006037 IB2009006037W WO2009138879A2 WO 2009138879 A2 WO2009138879 A2 WO 2009138879A2 IB 2009006037 W IB2009006037 W IB 2009006037W WO 2009138879 A2 WO2009138879 A2 WO 2009138879A2
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customer
customers
shape similarity
fit
item
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WO2009138879A3 (fr
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Wei Shi
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce

Definitions

  • the present invention relates to fit prediction of footwear or clothing to a customer and recommending footwear or clothing to a customer, using only the purchase / return or fitting records, without physically trying on the item and without having to take measurements of the foot or body, or having to use measurements of the footwear or clothing item.
  • the traditional sizing scheme divide the product size into predefined incremental changes, such as the US footwear men's sizes of 8, 8.5, 9, 9.5, 10, 10.5, 1 1 , 1 1.5, 12, 12.5, and 13.
  • the problems with the traditional sizing scheme is evident, and many consumer can tell from their own personal experiences that they fit different sizes of footwear and clothing from different brands and across different product lines, functions and styles.
  • the root of this problem may lie in that different countries may adopt different sizing schemes and different brand manufacturers use different design standards. Designing footwear and clothing is based on experimental data and experiences accumulated over decades. And different brands and design houses adopt different standards.
  • shoe last is usually used to represent the internal shape of the particular shoe.
  • Shoe last 3D information is usually available as shoe lasts are increasingly designed using 3D CAD tools. While 3D scanning is a step forward compared with traditional sizing scheme, it has not seen wide-spread application because the direct comparison of foot or body shape measurements with footwear or clothing is inherently expensive to implement.
  • Fit quality may be ultimately subjective and depends on individual preferences. Some may prefer a shoe loose at toe while others may prefer the shoe to be tight at heel, etc. As such, any fit prediction method relying solely on hard, objective, and direct comparison of geometry and measurements between footwear/clothing and foot/body may have certain limitations.
  • the present invention aims to solve one or more of the problems or disadvantages associated with the prior art
  • Wearable items refer to products a person would wear to the body, such as clothing, footwear, hats, gloves, eyeglasses, and so on.
  • a category of wearable item can be footwear, jacket, pants, sweater, shirt, underwear, underpants, hat, glove, or eyeglasses, etc.
  • the present invention exploit past purchase/return or fitting records of a group of customers to a library of wearable items to make fit prediction for a customer to a wearable item not yet tried.
  • Wearable items are designed for Specified Body Shapes.
  • each model of shoe in a particular band, style, and size is designed to fit a particular foot shape and set of measurements. These measurements are used to design a shoe last which defines the internal shape of the shoe.
  • fabric is cut to fit certain size body perfectly.
  • Specified Foot Shape is the foot shape and set of foot measurements on which the shoe is made to fit perfectly.
  • Specified Body Shape is the body shape and set of body measurements on which the clothing is made to fit perfectly. How these specified sizes match actual customer body sizes will not only determine fit comfort of customers, but also affect health.
  • the concepts of "Specified Body Shape” and “Specified Foot Shape” are introduced because an item of footwear, clothing, or other categories of wearable items are not rigid and do not have an innate shape of their own.
  • the “Specified Body Shape” of the wearable item is the corresponding shape of the body part the wearable item will fit perfectly.
  • the “Specified Body Shape” represents the fitting characteristics of a wearable item.
  • the present invention is based on these assumptions: that the body shape or foot shape of an adult customer remains relatively stable over time; that similarities in body shape or foot shape exist between the customers; and that similarities in Specified Foot Shape of footwear or Specified Body Shape of clothing exist between two different styles of shoes or two different styles of clothing, respectively. Based on these assumptions, customers' purchase/ return or fitting records can be used to predict fitting to footwear or clothing not yet tried.
  • purchase/return or fitting records can be used to determine similarities in wearable items. Clothing or footwear items fit to the same customer will have similarities in their Specified Body Shape or Specified Foot Shape and are interchangeable to some extent; For example, if a customer fit well to two different pairs of shoes, then there must be similarity between the specified foot shapes of these two pairs. If another customer also find one of the two pairs a good fit, it is very likely that this other customer will also find the other pair to be a good fit.
  • the two pairs of shoes while can be of different brand, style and nominal size, are interchangeable in terms of fit characteristics. Based on this principle, fit quality of wearable items can be predicted.
  • this method may be regarded as "using body to measure clothing”, “using foot to measure shoes”. There exist no better measuring device than the wearer's body and foot which can sensitively and reliably measure the static and dynamic fit quality over short-term and long-term. [0021]
  • the present invention develops the aforementioned concepts and ideas into an integral system.
  • Figure 1 A shows a data structure of purchase and fitting rating database and example data entries, according to an embodiment.
  • Figure 1 B shows the same data entries as Figure 1 A, but arranged in a different manner.
  • Figure 2 shows a forward search, which looks for items which a customer has purchased; and a reverse search, which looks for customers who have purchased an item.
  • Figure 3 shows that a forward search beginning with a customer and followed by a reverse search to find other customers who purchased same ID items as the customer.
  • Figure 4 shows sub-sets discovered through an iterative forward and reverse search until convergence.
  • Figure 5 shows some of the customers resulting from an iterative forward and reverse search.
  • Figure 6 shows a customer set divided into sub-sets.
  • Figure 7 shows the item set divided into sub-sets.
  • Figure 8A shows Foot Shapes Similarity Degree (%) between two customers, under different ⁇ values and the number of pairs of same ID footwear purchased, n.
  • Figure 8B shows "Foot Shape Similarity Degree (%) between two customers” vs. "number of the same ID items purchased by two customers” is a kind of saturated curve.
  • Figure 9 shows indirect calculation of Foot Shape Similarity Degree based on its transitive attribute.
  • Figure 10 shows an alternative method to calculate Specified Foot Shape Similarity Degree between two footwear items.
  • Figure 1 1 shows a Foot Shape Similarity Network with directly and indirectly calculated Foot Shape Similarity Degrees.
  • Figure 12 shows a Foot Shape Similarity Network of customers and its subnetworks.
  • Figure 13 shows a Specified Foot Shape Similarity Network of footwear items and its sub-networks.
  • Figure 14 shows a clustering method to further divide customers in a Foot Shape Similarity Sub-Network into clusters based on a similarity degree threshold.
  • Figure 15A summarizes and clarifies the terms “Customer Set”, “Sub-set”, “Customer Network”, “Sub-networks” and “Customer clusters”.
  • Figure 15B summarizes and clarifies the terms “Item set”, “Item Sub-set”, “Item Network”, “Sub-networks” and “Item clusters”.
  • Figure 16 shows an example of customers' Fitting Rating and Fit Score form for footwear items.
  • Figure 17 shows a calculation of Minor Difference between foot shapes of customers and the correction in their Foot Shape Similarity Degrees based on customers' Fitting Ratings.
  • Figure 18 shows a calculation of Minor Difference between specified foot shape footwear items and the correction in their Specified Body Shape Similarity Degrees based on customers' fitting ratings.
  • Figure 19 shows an example of "virtual try on”.
  • Figure 20 shows one method of fit prediction of a footwear item to a customer, without customer Fitting Ratings.
  • Figure 21 shows another method of fit prediction of a footwear item to a customer, without customer Fitting Ratings.
  • Figure 22 shows one method of fit prediction of a footwear item to a customer, with customer Fitting Ratings.
  • Figure 23 shows another method of fit prediction of a footwear item to a customer, with customer Fitting Ratings.
  • Figure 24 is an example showing the relation between predicted customers' fitting rating and returning ratio of the item purchased.
  • Figure 25 provides an overall summary of some of the concepts and procedures disclosed, according to an embodiment.
  • FIG. 1A shows the data structure of a fitting record database, which consists of four components: Customer ID 1 10, Item ID 120, Date of Purchase 130 and customer Fitting Rating 140.
  • the present invention can work with or without fitting rating 140. Date of Purchase 130 is only used during testing, simulation, and fine-tuning of the present invention. It is evident in the present specification that Date of Purchase 130 is not necessary during actual use of the present invention.
  • Customer refers to the actual user of products, not necessarily the person who bought and paid for the product. Each customer will have a distinct customer ID, though the ID does not need to be related to the customer's real identity information.
  • Item refers to the products.
  • Item ID identifies different products. Take footwear as an example, a distinct item ID refers to all shoes of a particular brand, in a particular function, of a particular model/style line, in a particular size, but can be any color. For example, Nike walking shoes for adult women's in the Nike Walker V model/style line, in size 6, in any color would have a distinct item ID. In other words, all shoes of the same item ID are exact the same in terms of fit characteristics and have the same item ID.
  • GTIN Globalstar, GTIN, UPC, or EAN. And retailers usually have their own SKU code scheme to manage inventory.
  • lower-case letter i, j, k,...or number 1 , 2, 3,... represent unique customers
  • upper-case letter A, B, C,... represent distinct wearable items.
  • Each customer is assigned a unique customer ID.
  • Each product item is assigned a distinct item ID as described above.
  • Customer purchase/return and fitting record can be recorded in a database.
  • An example is shown in Figure 1A.
  • the date of purchase is recorded as shown: "08-1 -2" means a purchase date of January 2 nd , 2008. Again, Date of Purchase 130 is not needed to use the present invention.
  • Figure 1 B contains the same data entries as Figure 1 A, but arranged in a different manner to illustrate that a single customer makes multiple purchases and that a item is purchased by multiple customers.
  • the present invention requires only customer purchase/return records, although customer fitting ratings can be helpful.
  • Figure 1 A and 1 B are only shown for illustrative purpose. In actual implementation, this database will be very big. As an electronic database, with modern database programming techniques, there should be no difficulty in data entry, management, or other standard database manipulations. This database can be constructed for different categories of wearable items, such as shoes, hats, gloves, upper-body clothing, pants, etc.
  • Forward search and reverse search are illustrated in Figure 2.
  • the forward search starts from a customer i of customer set C 210 to find out all items he/she has purchased in item set D 220, which forms a sub-set D(i) 230.
  • the reverse search starts from an item G of item set D 220 to find out all customers who have purchased item G, which forms a sub-set C(G) 240 in customer set C 210.
  • Figure 3 illustrates a forward search 310 starting from customer i resulting in a sub-set D 1 (i) 320 in item set D, followed by a reverse search 330 resulting in a subset C 1 (i) 340 in customer set C.
  • Each customer j in C 1 (i) has purchased at least 1 same ID item as customer i. But more generally, any two customer j and k in sub-set C 1 (i) have not necessarily purchased same ID items.
  • the superscript denotes the number of iteration of forward or reverse search.
  • Figure 4 illustrates a iterative forward and reverse search procedure 410 starting from customer i, which in successive iterations will result in sub-sets D 1 (i), C 1 (i), D 2 (i), C 2 O), ..., D k (i), C k (i), ...
  • the search process is stopped when convergence occurs.
  • sub-set C n (i) is defined through a procedure starting from a particular customer i, there is nothing special about customer i. In fact, starting from any customer in C n (i) will result in the same sub-set C n (i).
  • D n (i) and C n (i) can be expressed as D 1 and Ci respectively.
  • the subscript "1" denotes the first subset found by the iterative process in customer set C and item set D.
  • Figure 5 illustrates part of the customers in sub-set C-i. Any two customer j and k in sub-set Ci have not necessarily purchased same ID items, but could be indirectly connected through other customers. For example, customers 1 and 2 both purchased item A, customers 2 and 3 both purchased item B, customers 3 and 4 both purchased item C, etc.
  • any two items A and K in sub-set Di have not necessarily been purchased by a customer, but could be indirectly connected through other items.
  • items A and B are both purchased by customers 1
  • items B and C are both purchased by customer 2
  • C and D are both purchased by customer 3, etc.
  • sub-sets C 2 and D 2 Starting from another customer j outside of sub-set Ci and follow the same procedure we will get sub-sets C 2 and D 2 .
  • sub-sets C3 and D 3 , C 4 and D 4 ,... can be found. It is evident that the intersection between any two sub-sets D-I, D 2 , D 3 ,..., and between any two sub-sets C-i, C 2 , C 3 ,... are all empty sets.
  • Sub-sets Ci and D-i, sub-sets C 2 and D 2 are mutually exclusive. This means that customers in C-i have only purchased items in D-i, but not items in D 2 , while customers in C 2 have only purchased items in D 2 , but not items in D-i. The customer set and the item set are thus disjointed into sub-sets. Different body shapes lead to different wearable items purchased, and this would be beneficial to targeted marketing, wearable item recommendation, fit prediction and purchase records data self-correction.
  • Foot Shape Similarity is used here as an example, it is understood that the concepts, principles and methods suggested here also can be applied to the similarity analysis on other parts of human body, such as head shape, hand shape, upper body shape, etc.
  • Foot Shape Similarity Degree of customers i and j is defined based on the fact that they have both purchased same ID shoes, which have the same fit characteristics. For example, i and j both have purchased shoe A, then their foot shape are similar with each other to a certain extent, which can be expressed in a Foot Shape Similarity Degree. If, furthermore, i and j have both purchased another shoe B, Foot Shape Similarity Degree of i and j should be increased to reflect this new piece of evidence.
  • Foot Shape Similarity Degree is defined as a number between 0-1 , or 0% ⁇ 100%.
  • the Foot Shape Similarity Degree between two customers gets a basic value of a while they both purchase first pair of same ID shoes; when they both purchase a different second pair same ID shoes, the Foot Shape Similarity Degree will increase, but not doubled; the different third purchase of same ID shoes results in a even smaller increment in Foot Shape Similarity Degree, and so on.
  • the number of the same ID footwear items purchased must be a kind of saturated curve as shown in Figure 8B.
  • the reason for this saturation attribute lies in: a new piece of evidence of foot shape similarity is more valuable when the existing evidences are less.
  • Foot Shape Similarity Degree is very close to 100% and the further increase in the number of same ID shoes both purchased only leads to a negligible increase in Foot Shape Similarity Degree.
  • Figure 8A presents S, ti , the value of Foot Shape Similarity Degree between customers i and j, when both customers purchased 1 , 2, 3, ...pair of same ID shoes under different values of a.
  • the values of S, ⁇ , in the table are calculated by the following formula
  • n is the number of same ID shoes both customers i and j purchased.
  • the value of parameter a should be adjusted so that predicted return ratio, as described in Figure 24, coincides with actual return ratio. It is understood that above formula is only an example and any other formulae or methods may be used provided that the "Foot Shape Similarity Degree vs.
  • the number of the same ID shoes purchased" curve is a kind of saturated curve.
  • Foot Shape Similarity Degree has transitive attribute. If customer 1 and customer 2 have similar foot shape, with a certain Foot Shape Similarity Degree between them and customers 2, 3 also have similar foot shape, with a certain Foot Shape Similarity Degree between them, it is reasonable to believe that customers 1 and 3 have similar foot shape to a certain extent. Based on the transitive attribute, even if two customers did not purchase same ID footwear hence their Foot Shape Similarity Degree cannot be calculated directly, their Foot Shape Similarity Degree can be calculated indirectly as shown in Figure 9:
  • the Specified Body Shape Similarity Degree of wearable items can also be calculated based on customer purchase record.
  • Specified Foot Shape Similarity Degree is taken as an example. All concepts, principles and methods described herein can also be applied to the similarity analysis of specified body shape of other kinds of wearable items.
  • Specified Foot Shape Similarity Degree between footwear items and Foot Shape Similarity Degree between customers are highly symmetrical concepts. All attributes for Foot Shape Similarity Degree also apply for Specified Foot Shape Similarity Degree. The difference is Foot Shape Similarity Degree between two customers is derived from similarity analysis of purchased footwear by the two customers; while Specified Foot Shape Similarity Degree between two pairs of footwear is derived from similarity analysis of customers who purchased the two pairs of footwear.
  • Foot Shape Similarity Degree can be calculated indirectly by the transitive attribute of Foot Shape Similarity Degree as shown in Figure 9
  • Specified Foot Shape Similarity Degree can also be calculated indirectly by the transitive attribute of Specified Foot Shape Similarity Degree.
  • the process to calculate indirect Specified Foot Shape Similarity Degree is exactly the same as calculating indirect Foot Shape Similarity Degree aforementioned. The detailed steps are skipped because it should be evident to those skilled in the art.
  • Figure 10 presents an alternative method to calculate Specified Foot Shape Similarity Degree of footwear. There are two items of footwear A and B in item set D 1020, reverse searches form A and B result in sub-set C A 1030 and C B , 1040 in customer set C 1010 respectively.
  • Specified Foot Shape Similarity Degree S A B is within 0-1 , or 0% ⁇ 100%.
  • S A B 1 , this means that the specified foot shape of A and B are the same;
  • S A B 0 when the purchasers of A and B don't have any customer in common, this means that the Specified Foot Shape of A and B are entirely different.
  • Foot Shape Similarity Degree S, j of customers i and j can be derived from Specified Foot Shape Similarity Degree as defined above between all the shoes they have purchased. Even if customer i and j have not purchased any same ID shoes, their Foot Shape Similarity Degree can be derived as follows.
  • the average of all Specified Foot Shape Similarity Degrees between a piece of footwear purchased by customer i and another piece of footwear purchased by customer j is the Foot Shape Similarity Degree between customer i and j, where the Specified Foot Shape Similarity Degree is obtained by method illustrated in Figure 10. This is another method to calculate Foot Shape Similarity Degree indirectly.
  • the Foot Shape Similarity Network is shown in Figure 12 and Figure 6, where 10 customers are disjointed into two Foot Shape Similar Sub-Networks: 1 , 5, 8, 9, 10 and 2, 3, 4, 6, 7.
  • the Foot Shape Similar Degrees between any two customers in the same sub-network is determined by directly calculated similarity degrees, shown in solid line, or by indirectly calculated similarity degrees, shown in dotted line. Foot Shape Similar Degrees across subnetworks, however, can not be calculated because any customers of different networks have never purchased any same ID footwear.
  • the network is disjointed into two sub-networks 1210 and 1220 as shown in Figure 12.
  • the values of Foot Shape Similarity Degrees are also indicated in Figure 12.
  • Specified Foot Shape Similarity Network displayed in Figure 13 is thus disjointed into two sub-networks 1310 and 1320 with solid lines represent directly calculated similarity degrees and dotted lines represent indirectly calculated similarity degrees.
  • the values of Specified Foot Shape Similarity Degrees are also indicated in Figure 13.
  • Described above is an example on footwear.
  • all the customers in a purchase database like the one shown in Figure 1 A and 1 B, for a particular category of wearable items, such as shoes, hats, gloves, upper-body clothing, pants, etc., can be expressed by a "Body Shape Similarity Network".
  • Each network can be a Body Shape Similarity Network, or Foot Shape Similarity Network, or Head Shape Similarity Network, or Upper-body Shape Similarity Network, or Lower-body Shape Similarity Network, or Hand-shape Similarity Network, etc.
  • the network can be disjointed into a number of sub-networks.
  • the Body Shape Similarity Degree between any two customers across different sub-networks is 0, while any two customers in the same sub-network are connected by a Body Shape Similarity Degree, the value of which is greater than 0.
  • All the items in a purchase database, like the one shown in Figure 1A and 1 B, for a particular category of wearable items can be expressed by a Specified Body Shape Similarity Network.
  • the network can be disjointed into a number of subnetworks. Each sub-network is a Specified Body Shape Similarity Sub-Network.
  • the Specified Body Shape Similarity Degree between any two items across different subnetworks is 0, while any two items in the same network are connected by a Specified Body Shape Similarity Degree, the value of which is greater than 0.
  • cluster Mi is defined through a search procedure beginning with a particular customer i, there is nothing special about customer i. In fact, starting from any customer in Mi will result in the same cluster M-i.
  • M 2 shares no common customers with M-i, i.e., the intersection of M-i and M 2 is empty. It is easy to understand that if there is really a common customer in both M-i and M 2 then it must have been included in M-i when searching M-i's members. Furthermore, according to the transitive nature of similarity, as long as M-i and M 2 have only one common customer then all the customers from M-i and M 2 should have their similarity degrees equal to or higher than 0.8, and so M-i and M 2 are effectively one cluster.
  • each Specified Foot Shape Similarity Network can be divided into a series of disjointed Specified Foot Shape Similarity Clusters N-i, N 2 ... etc., each of them encompass of a number of footwear items highly similar, to an extent adjustable by the threshold value, with each other in terms of fit characteristics.
  • a particular cluster of footwear may include quite different footwear items belonging to different function lines, different brands, different styles or different colors; they are clustered into one sub-cluster because they are interchangeable. If one of them fits to a customer then very likely the other items in the same cluster would fit to the same customer, to a extent adjustable by the threshold value. While the sizing standards of footwear are meant to standardize size across different brands, functional lines, and styles, in actuality a typical customer may find him/herself wear for example size 8 running shoes in one brand, size 8.5 dress shoes in another brand, etc.
  • the clustering method proposed can cluster footwear and other wearable items in terms of fitting characteristics, not the nominal sizes.
  • Figure 15A summarizes and clarifies the terms "Customer Set”, “Sub-set”, “Customer Network”, “Sub-networks” and "Customer clusters”.
  • Customer Set 151 1 contains all customers in the purchase record database. Through the iterative forward and reverse search 1514, Sub-Sets 1515 are discovered. When similarity degrees are calculated 1512, Customer Set 151 1 becomes Customer Network 1513, and Sub-Sets 1515 become Sub-Networks 1516. The Sub-Networks 1516 are divided through clustering process 1517 into Customer Clusters 1518.
  • the original Customer Set contains all customers in the purchase record database.
  • Sub-Sets are obtained.
  • the Customer Set becomes Foot Shape Similarity Network and Sub- Sets become Foot Shape Similarity Sub-Networks.
  • the Foot Shape Similarity Sub- Networks are divided into Foot Shape Similarity Clusters using a preset similarity degree threshold. Other categories of wearable items follow the same process.
  • FIG 15B summarizes and clarifies the terms “Item set”, “Item Sub-set”, “Item Network”, “Sub-networks” and “Item clusters”.
  • Item Set 1521 contains all items in the purchase record database. Through the iterative forward and reverse search 1524, Sub-Sets 1525 are discovered. When similarity degrees are calculated 1522, Item Set 1521 becomes Item Network 1523, and Sub-Sets 1525 become Sub- Networks 1526. The Sub-Networks 1526 are divided through clustering process 1527 into Item Clusters 1528.
  • the original Item Set contains all footwear in the purchase record database.
  • Sub-Sets are obtained.
  • the Item Set becomes Specified Foot Shape Similarity Network and Sub-Sets become Specified Foot Shape Similarity Sub-Networks.
  • the Specified Foot Shape Similarity Sub-Networks are divided into Specified Foot Shape Similarity Clusters using a preset similarity degree threshold. Other categories of wearable items follow the same process.
  • Figure 16 is customers' fitting rating form for footwear items.
  • the customer will pick a Fitting Rating for a footwear item after actual try on.
  • the database will assign the corresponding Fit Score.
  • the scores represent departures of footwear's specified foot shape from customers' foot shape. To a certain extent, the absolute value of the scores reflects the extent to which the footwear item fit or doesn't fit a customer.
  • Figure 17 shows that customers' fitting ratings can be used to calculate minor difference between body shapes and to refine their Body Shape Similarity Degrees.
  • the following description uses footwear as an example.
  • customers' fitting rating on the fit, tight or loose, of a footwear item is ranked in 9 levels of Fit Score: -0.4, -0.3, -0.2, -0.1 , 0.0, 0.1 , 0.2, 0.3, 0.4, with an increment between levels of 0.1.
  • Fit Score 9 levels of Fit Score: -0.4, -0.3, -0.2, -0.1 , 0.0, 0.1 , 0.2, 0.3, 0.4, with an increment between levels of 0.1.
  • FIG 17 suppose purchase record shows that customers i and j 1720 both have purchased footwear G 1710, which means their foot shapes are similar to a certain degree. However, if the fitting ratings 1730 on the fit of footwear G from the two customers are different, there exist some Minor Difference d, j G 1740 between their foot shapes.
  • the Foot Shape Similarity Degree between two customers can be corrected.
  • )) s, j -
  • the Specified Foot Shape Similarity Degree of two footwear items G and P can be corrected.
  • )) S ⁇ G P - 1 d ⁇ G P I .
  • fit ratings, fit scores and the parameter a presented here are for illustrative purpose only. During implementation, these values can be adjusted without departing from the principles of the present invention.
  • Figure 19 demonstrates an example of "virtual try on" of footwear.
  • customer i, 1910 is attracted by a style X 1920 of footwear while browsing website, catalogs or in a shop window.
  • customer i does not know the exact size suitable and doesn't want to try, or cannot as in the case of online shopping. In this case the proper size can be recommended based on the present invention.
  • the method can be described as follows: search the sizes of footwear style X purchased by customers belonging to the same Foot Shape Similarity Cluster 1930 as customer i. If p customers 1 , 2, ...
  • p are found having purchased footwear style X, the sizes are m/, m 2 x , ..., m p x respectively, and the Foot Shape Similarity Degree of each of those customers with respect to customer i are known as S,i, S, 2 , ..., S, p respectively, then the weighted average of m/, m 2 x , ..., m p x with S,i, S 12 , ..., S ⁇ p as the weights is likely the right size of footwear G for customer i to purchase. The average should be rounded to a nearest standard size.
  • Figure 20-23 illustrate four examples of fit prediction methods for wearable items. As customer i intend to purchase footwear item G, the following methods can be applied to predict the fit of G to i, without actual try on.
  • Figure 20 Search all the footwear items A, B, ..., F 2030 customer i 2010 has purchased and the Specified Foot Shape Similarity Degrees of those items with respect to G 2020 are S A G , S B G , ..., S F G , then the average of these values indicates the Fit Score f G of item G to customer i.
  • Figure 21 Search all the customers j, k, I,..., o 2130 who have purchased item G 2120 and the Foot Shape Similarity Degree of them with respect to i 21 10 are Si J , Si, k , ..., S ⁇ ,o, then the average of these values indicates the Fit Score f G of item G to customer i.
  • Figure 22 Search all the footwear items A, B, ..., F customer i has purchased.
  • the Fit Scores of customer i on these items are e, A , e, B , ..., e, F 2210, and the specified foot shape Minor Differences of items A, B, ..., F with respect to item G are d A G , d B G ,...,d F G 2220.
  • Minor Differences are calculated from the Fit Scores of those customers who have purchased item G and one or more items among items A, B, ..., F.
  • the weights are cs G A , cs G B ,...,cs G F 2250, which are the corrected Specified Foot Shape Similarity Degree of each of A, B, ..., F with respect to G.
  • Figure 23 Search all the customers j, k, ..., I who have purchased item G.
  • the Fit Scores of these customers on item G are e/ 3 , e k G , ..., e G 2310.
  • the corrected Foot Shape Similarity Degree of each of these customers with customer i are cs,, j , OS,, k , ..., CS
  • the weights are cs. j , cs,, k , ..., CS
  • Figure 24 shows an example, demonstrating the relations between e G and r G .
  • Figure 25 summarizes the present invention, showing its components and processes.
  • the system consists of 3 main blocks: The Customer Purchase Record Database 2510, The Algorithm & Program 2520, and The Customer Services 2530.
  • the Customer Purchase Record Database block 2510 includes a Basic Database 251 1 , customers' Fitting Rating and Fit Score Database 2512, and Data of Purchase 2513.
  • the Basic Database includes Customer ID, Item ID.
  • Body Shape Similarity Network and Specified Body Shape Similarity Networks 2526 are obtained through 2529.
  • Sub-Networks 2526 are also obtained based on similarity connections.
  • the Body Shape Similarity Sub-Networks and Specified Body Shape Similarity Sub-Networks 2526 are divided into smaller clusters based on a threshold of similarity degree 2556.
  • Body Shape Similarity Clusters of customers and Specified Body Shape Similarity Clusters of items 2527 are obtained.
  • the Customer Services block 2530 includes Virtual Try On: Item Size Recommendation 2531 , Fit Prediction of a Wearable Items to a Customer 2532; Prediction of Product Return and Return Ratio Control 2533FIG. 1 illustrates an embodiment of a 20.
  • steps of the method of operating the system 20 are listed in a preferred order, the steps may be performed in differing orders or combined such that one operation may perform multiple steps. Furthermore, a step or steps may be initiated before another step or steps are completed, or a step or steps may be initiated and completed after initiation and before completion of (during the performance of) other steps. [00170] The preceding description has been presented only to illustrate and describe exemplary embodiments of the methods and systems of the present invention. It is not intended to be exhaustive or to limit the invention to any precise form disclosed. It will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention.

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Abstract

L'invention porte sur un procédé, qui comprend l'identification d'un client effectuant une commande et d'un article pouvant être porté désiré, et l'identification d'une pluralité d'articles pouvant être portés qui sont associés au client effectuant une commande. Le client effectuant une commande a essayé au moins une partie de la pluralité d'articles pouvant être portés. Le procédé comprend également l'identification d'au moins un degré de similarité de forme de corps spécifié pour chacun de la pluralité d'articles pouvant être portés associés à l'article pouvant être porté désiré, et l'obtention d'un résultat d'adaptation estimé pour l'article pouvant être porté désiré vis-à-vis du client effectuant une commande.
PCT/IB2009/006037 2008-05-12 2009-05-12 Système et procédé pour prédiction d'adaptation et recommandation de chaussure et de vêtement Ceased WO2009138879A2 (fr)

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US5229408P 2008-05-12 2008-05-12
US61/052,294 2008-05-12

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WO2009138879A3 WO2009138879A3 (fr) 2010-01-28

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Cited By (5)

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Publication number Priority date Publication date Assignee Title
US8762292B2 (en) 2009-10-23 2014-06-24 True Fit Corporation System and method for providing customers with personalized information about products
WO2016105809A1 (fr) * 2014-12-22 2016-06-30 Intel Corporation Essai de dispositif vestimentaire avant achat
US10685457B2 (en) 2018-11-15 2020-06-16 Vision Service Plan Systems and methods for visualizing eyewear on a user
US10841591B2 (en) 2017-04-21 2020-11-17 Zenimax Media Inc. Systems and methods for deferred post-processes in video encoding
US20230334546A1 (en) * 2014-09-30 2023-10-19 Ebay Inc. Garment size mapping

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5930769A (en) * 1996-10-07 1999-07-27 Rose; Andrea System and method for fashion shopping
US6965868B1 (en) * 1999-08-03 2005-11-15 Michael David Bednarek System and method for promoting commerce, including sales agent assisted commerce, in a networked economy
US7584122B2 (en) * 2001-03-08 2009-09-01 Saint Laurie Ltd. System and method for fitting clothing
US20050256771A1 (en) * 2004-05-12 2005-11-17 Garret E R System and method of matching artistic products with their audiences
US7421306B2 (en) * 2004-09-16 2008-09-02 Sanghati, Llc Apparel size service

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8762292B2 (en) 2009-10-23 2014-06-24 True Fit Corporation System and method for providing customers with personalized information about products
US20230334546A1 (en) * 2014-09-30 2023-10-19 Ebay Inc. Garment size mapping
US12266001B2 (en) * 2014-09-30 2025-04-01 Ebay Inc. Garment size mapping
WO2016105809A1 (fr) * 2014-12-22 2016-06-30 Intel Corporation Essai de dispositif vestimentaire avant achat
KR20170097007A (ko) * 2014-12-22 2017-08-25 인텔 코포레이션 구매-전 웨어러블 디바이스 테스팅
US9965789B2 (en) 2014-12-22 2018-05-08 Intel Corporation Pre-purchase wearable device testing
KR102444056B1 (ko) * 2014-12-22 2022-09-19 인텔 코포레이션 구매-전 웨어러블 디바이스 테스팅
US10841591B2 (en) 2017-04-21 2020-11-17 Zenimax Media Inc. Systems and methods for deferred post-processes in video encoding
US11778199B2 (en) 2017-04-21 2023-10-03 Zenimax Media Inc. Systems and methods for deferred post-processes in video encoding
US10685457B2 (en) 2018-11-15 2020-06-16 Vision Service Plan Systems and methods for visualizing eyewear on a user

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