Method For Providing A Custom-Like Fit In Ready-To-Wear Apparel

Abstract

A method to provide an improved size grid for garments is provided. Also a method to select ready-to-wear garments with a custom-like fit from the improved size grid is provided. The method uses an algorithm to select the garment size based on body measures of the customer. A method to sell and buy read-to-wear garments with a custom-like fit is also provided herein.

What is claimed is: 
     
         1 . A method to create a size grid for ready-to-wear garments with a custom-like fit, said method comprising the steps of:
 a. obtaining a list of standard measures for a type of garment;   b. identifying points of areas of the garment where fit is most often an issue;   c. running statistical analyses on body measures of a significant number of consumers related to the standard measure of step a)   d. categorizing each garment measure from step a) as averaged if not identified in step b) or as variant if positively identified in step b);   e. prioritizing the variant measures of step d) based on level of correlation to other measures and importance to fit, and determining the highest priority measure as anchor variant measure;   f. arranging body scan data into groups with set increments for anchor variant measure;   g. arranging body scan data of step f) in into sub-groups with set increments for second highest priority of the variant measures of step e);   h. arranging body scan data of step g) in into further sub-groups with set increments for third highest priority of the variant measures of step e);   i. optionally creating additional sub-groups by repeating step h) with fourth, fifth and so on highest priority;   j. selecting a specific measure for each variant sub group of step g), h) or i);   k. within the final sub-groups of step h) or i) running statistical analysis for each of the averaged measure of step d) and the specific measure of step j); and   l. creating the size grid based on analysis of step k).   
     
     
         2 . The method of  claim 1 , wherein the size grid has 30-250 sizes. 
     
     
         3 . A method to create ready-to-wear garments comprising the steps of  claim 1  to create the sizing grid, and subsequently the steps of creating a single pattern in step l) of  claim 1 , and fitting and modifying the pattern on a fit model falling within the variant measures of the sub group. 
     
     
         4 . A method to provide a garment with a custom-like fit for a person comprising the steps of  claim 3  to create the garments, and subsequently the steps of a consumer providing all his/her variant measures and applying an algorithm starting with the anchor measure followed by the variant measures of step h) or i) of  claim 3  to determine the best fit for the consumer. 
     
     
         5 . A method to select a garment with a custom-like fit, said method comprising a customer providing individual measures for predetermined variant measures and applying the measures on an algorithm, said algorithm being constructed by:
 a) obtaining a list of standard measures for a type of garment;   b) identifying points of areas of the garment where fit is most often an issue;   c) running statistical analyses on body measures of a significant number of consumers related to the standard measure of step a)   d) categorizing each garment measure from step a) as averaged if not identified in step b) or as variant if positively identified in step b);   e) prioritizing the variant measures of step d) based on level of correlation to other measures and importance to fit, and determining the highest priority measure as anchor variant measure;   f) arranging body scan data into groups with set increments for anchor variant measure;   g) arranging body scan data of step f) in into sub-groups with set increments for second highest priority of the variant measures of step e);   h) arranging body scan data of step g) in into further sub-groups with set increments for third highest priority of the variant measures of step e);   i) optionally creating additional sub-groups by repeating step h) with fourth, fifth and so on highest priority;   j) selecting a specific measure for each variant subgroup of step g), h) or i);   k) within the final sub-groups of step g), h) or i) and the specific measures of step j) running statistical analysis for each of the averaged measure of step d) and the specific measures of step j); and   l) finalizing size grid based on analysis of step k);   wherein the algorithm starts with the anchor measure followed by the variant measures of step g), h) or i) to determine the best fit for the consumer, and matches the consumer with an actual article of clothing that is produced from a pattern created according to the size grid of step l.   
     
     
         6 . The method of  claim 5 , wherein the customer provides the individual measures in a store. 
     
     
         7 . The method of  claim 6 , wherein the measures are scanned in the store and saved in his information profile. 
     
     
         8 . The method of  claim 7 , wherein the customer may use the saved measures for later ordering custom fit garments on line. 
     
     
         9 . The method of  claim 5 , wherein the customer provides the measures online and receives the custom fit garments through an online order. 
     
     
         10 . An improved size grid for garments, wherein the grid is created by a method comprising the steps of:
 a) obtaining a list of standard measures for a type of garment;   b) identifying points of areas of the garment where fit is most often an issue;   c) running statistical analyses on body measures of a significant number of consumers related to the standard measure of step a);   d) categorizing each garment measure from step a) as averaged if not identified in step b) or as variant if positively identified in step b);   e) prioritizing the variant measures of step d) based on level of correlation to other measures and importance to fit, and determining the highest priority measure as anchor variant measure;   f) arranging body scan data into groups with set increments for anchor variant measure;   g) arranging body scan data of step f) in into sub-groups with set increments for second highest priority of the variant measures of step e);   h) arranging body scan data of step g) in into further sub-groups with set increments for third highest priority of the variant measures of step e);   i) optionally creating additional sub-groups by repeating step h) with fourth, fifth and so on highest priority;   j) selecting a specific measure for each variant sub group of step g), h) or i); and   k) within the final sub-groups of step g), h) or i) running statistical analysis for each of the averaged measure of step d) and the specific measures of step j).   
     
     
         11 . The size grid of  claim 10 , wherein the number of sizes is between 30 and 250.