Using artificial intelligence to determine a value for a variable size component
A machine learning model for predicting a size fit satisfaction for a variable size component is trained using at least sizing profiles of a plurality of items and feedbacks of subjects regarding sizing of the plurality of items. The machine learning model is used to determine a value for the variable size component that corresponds to an optimal predicted size fit satisfaction. The determined value of the variable size component is provided for use in creating a new item with a sizing variation based on the determined value.
1. A method, comprising:
using a processor to train a machine learning model for predicting a size fit satisfaction for a variable size component associated with a base garment, wherein the base garment is associated with one or more fixed size components and one or more variable size components, wherein the machine learning model is trained using at least sizing profiles of a plurality of items and feedbacks of subjects regarding sizing of the plurality of items;
using the machine learning model to determine a value for the variable size component that corresponds to an optimal predicted size fit satisfaction; and
providing the determined value of the variable size component for use in creating a new item with a sizing variation based on the determined value, wherein the new item is manufactured based on the one or more fixed sized components and at least the determined value of the variable size component.
2. The method of claim 1 , wherein the machine learning model is trained using at least the sizing profiles of the plurality of items selected as matching a silhouette category of the new item and the feedbacks.
3. The method of claim 1 , wherein the machine learning model is trained using at least the feedbacks of the subjects with a sizing profile that matches a silhouette category to be optimized.
4. The method of claim 1 , further comprising manufacturing the new item.
5. The method of claim 1 , wherein creating the new item includes providing the base garment.
6. The method of claim 1 , wherein the feedbacks of the subjects regarding sizing of the plurality of items include body measurements of the subjects.
7. The method of claim 6 , wherein the body measurements of the subjects include a body height, a sleeve length, or a body shape.
8. The method of claim 6 , wherein the body measurements of the subjects include a body height or a cup size.
9. The method of claim 1 , wherein the feedbacks of the subjects regarding sizing of the plurality of items include fit challenges.
10. The method of claim 9 , wherein the fit challenges include a sleeve length, neck size, or chest size fit challenge.
11. The method of claim 1 , wherein the feedbacks of the subjects regarding sizing of the plurality of items include fit preferences.
12. The method of claim 11 , wherein the fit preferences include a slim, a regular, and a relaxed fit preference.
13. The method of claim 11 , wherein the fit preferences include a loose or a fitted fit preference.
14. The method of claim 1 , wherein the feedbacks of the subjects regarding sizing of the plurality of items include a fit rating or a size rating.
15. The method of claim 1 , wherein the plurality of items each has a silhouette category.
16. The method of claim 15 , wherein the silhouette category defines a collection of measurements.
17. The method of claim 16 , wherein the measurements include a shoulder-to-shoulder, chest, sleeve length, or bicep measurement.
18. The method of claim 16 , wherein the measurements include a waist, thigh, knee, rise, leg opening, or inseam measurement.
19. A system, comprising:
a processor; and
a memory coupled with the processor, wherein the memory is configured to provide the processor with instructions which when executed cause the processor to:
train a machine learning model for predicting a size fit satisfaction for a variable size component associated with a base garment, wherein the base garment is associated with one or more fixed size components and one or more variable size components, wherein the machine learning model is trained using at least sizing profiles of a plurality of items and feedbacks of subjects regarding sizing of the plurality of items;
use the machine learning model to determine a value for the variable size component that corresponds to an optimal predicted size fit satisfaction; and
provide the determined value of the variable size component for use in creating a new item with a sizing variation based on the determined value, wherein the new item is manufactured based on the one or more fixed size components and at least the determined value of the variable size component.
20. A computer program product, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for:
training a machine learning model for predicting a size fit satisfaction for a variable size component associated with a base garment, wherein the base garment is associated with one or more fixed size components and one or more variable size components, wherein the machine learning model is trained using at least sizing profiles of a plurality of items and feedbacks of subjects regarding sizing of the plurality of items;
using the machine learning model to determine a value for the variable size component that corresponds to an optimal predicted size fit satisfaction; and
providing the determined value of the variable size component for use in creating a new item with a sizing variation based on the determined value, wherein the new item is manufactured based on the one or more fixed size components and at least the determined value of the variable size component.