GARMENT SIZE RECOMMENDATION AND FIT ANALYSIS SYSTEM AND METHOD

• US 20170039622A1
• Filed: 04/13/2015
• Published: 02/09/2017
• Est. Priority Date: 04/11/2014
• Status: Abandoned Application

• Current Assignee: Metail Limited
• Original Assignee: Metail Limited
• Inventors: BOLAND, Robert, DOWNING, Jim, MILLER, Ray, ROGERS, Gareth, TOWNSEND, Joe, CHEN, Yu
• Application Number:
  US15/303,369
• Publication Number:
  US 20170039622A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 3/04815 Interaction with a metaphor-based environment or interaction object displayed as three-dimensional, e.g. changing the user viewpoint with respect to the environment or object
• CPC Class Codes: G06F 3/04817 using icons graphical or visual programming using iconic symbols G06F8/34
• CPC Class Codes: G06F 3/04842 Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06T 11/206 Drawing of charts or graphs
• CPC Class Codes: G06T 15/205 Image-based rendering
• CPC Class Codes: G06T 17/00 Three dimensional [3D] modelling, e.g. data description of 3D objects
• CPC Class Codes: G06T 19/20 Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
• CPC Class Codes: G06T 2210/16 Cloth
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

A computer-implemented garment size recommendation and fit analysis system in which a memory stores a virtual profile or model of an end-user and a processor is programmed to receive an end-user'"'"'s selection of a garment and to then determine, using a garment fit algorithm, how well that garment will fit the end-user'"'"'s profile or model, and in which the algorithm is trained on actual sales data.

Claims

1. A computer-implemented method, performed using a garment size recommendation and fit analysis system in which a memory stores a virtual profile or model of an end-user and a processor is programmed to receive an end-user'"'"'s selection of a garment and to then determine, using a garment fit algorithm, how well that garment will fit the end-user'"'"'s profile or model, and in which the algorithm is trained on actual sales data the method including the steps of:

(i) the system training the garment fit algorithm on actual sales data;

(ii) the processor receiving the end-user'"'"'s selection of the garment;

(iii) the processor using the trained garment fit algorithm to determine how well that garment will fit the end-user'"'"'s profile or model, for a plurality of garment sizes, and

(iv) the system recommending to the end-user the best fit or size of the garment.

2. The method of claim 1 in which the algorithm is trained on actual sales data from the retailer of that garment in order to adjust the size charts from that retailer.

3. The method of claim 1 in which the sales data includes one or more of:

a list of items purchased in an order;

the size(s) of the garment(s) in the order;

the body parameters of the customer;

the customer'"'"'s demographic information, location, and fit preference;

whether the garment(s) were kept, returned, or exchanged.

4. The method of claim 1, in which the algorithm is trained using, and updated on, actual, live sales data.

5. The method of claim 1, in which the actual, live sales data for a specific retailer is tracked using a widget or application launched when a customer browses the web retail web site of that retailer.

6. The method of claim 1, in which the trained algorithm requires a relatively small amount of initial training data to make it usable, but updates over time to adapt to different body shape groups associated with different garment categories.

7. The method of claim 1, in which the trained algorithm requires a relatively small amount of initial training data to make it usable, but updates over time to adapt to different garment brands under that retailer.

8. The method of claim 1, in which the training algorithm tracks trends in consumer purchasing and returns behaviour.

9. The method of claim 1, in which the trained algorithm operates even when good garment size charts of the retailer or the brand are not available.

10. The method of claim 1, in which the trained algorithm operates when the manufactured item deviates from the size chart.

11. The method of claim 1, in which the trained algorithm generates a visual plot, such as a scatter plot, showing how measurements associated with a retailer'"'"'s size charts correlates with customers who bought and kept, or bought and returned, a particular size of garment from that retailer.

12. The method of claim 1, in which the profile or model is specific to the end-user and is derived from one or more of:

personal data relating to the user;

height;

weight;

age;

body shape;

cup size;

chest/bust, waist and hip measurements;

previously bought or liked garments;

previous browsing history.

13. The method of claim 1, in which the profile or model is specific to the end-user and hence not a cluster of similar end-users

14. The method of claim 1, in which the trained algorithm compares the virtual profile or model of the end-user with data from the fit-points of the garment retailer'"'"'s size charts.

23. The method of claim 1, in which the trained algorithm computes the similarity of the end-user'"'"'s profile or model and the corresponding measurements of each size of a garment by using a distance metric.

24. The method of claim 23 in which the corresponding measurements of each size of a garment are defined in the size charts from the retailer or manufacturer of that garment.

26. The method of claim 23 in which the distance metric is a metric that takes into account correlation between different body measurements, such as the Mahalanobis distance, or in which the distance metric is a metric that takes into account that different fit points have different levels of impact on size recommendation, such as the Mahalanobis distance.

28. The method of claim 1, in which the training algorithm uses an estimation of the body shape distribution associated with actual sales and returns of each size of a garment and generates a bias to correct the measurement definition in the size chart.

29. The method of claim 1, in which the training algorithm uses a K-Nearest Neighbour (KNN) machine learning algorithm.

32. The method of claim 1, in which the training algorithm uses a Bayesian approach to learn probabilistic models for each garment size from observed body measurement data and a default size chart in order to correct the measurement definitions in the size charts.

34. The method of claim 1, in which the trained algorithm tells the customer how well a specific size of an item currently being viewed would fit against their virtual profile/model (e.g. their bust, waist and hips), for example using predefined terms or categories.

36. The method of claim 1, in which the processor displays an item of a specific size, together with their fitting information with a choose or selection icon (e.g. a tick box) or system (e.g. drag‘

n’

drop) that, if activated, transfers the or each item into an on-line shopping bag for purchase.

37. The method of claim 1, in which (a) the virtual profile or model is generated from user data;

(b) a 3D garment image is generated by analysing and processing multiple 2D photographs of the garment; and

(c) the 3D garment image is shown super-imposed over the 3D virtual body model.

15-22. -22. (canceled)

25. (canceled)

27. (canceled)

30-31. -31. (canceled)

33. (canceled)

35. (canceled)

38. A computer-implemented garment size recommendation and fit analysis system in which a memory stores a virtual profile or model of an end-user and a processor is programmed to receive an end-user'"'"'s selection of a garment and to then determine, using a garment fit algorithm, how well that garment will fit the end-user'"'"'s profile or model, for a plurality of garment sizes, and in which the algorithm is trained on actual sales data wherein the system uses the trained garment fit algorithm to recommend to the end-user the best fit or size of the garment.