System and method for retail planning with smart product attributes

Patent metadata

Field	Value
Publication number	US-11030574-B1
Application number	US-201816185983-A
Country	US
Kind code	B1
Filing date	Nov 9, 2018
Priority date	Aug 29, 2018
Publication date	Jun 8, 2021
Grant date	Jun 8, 2021

Abstract

A system and method are including a computer and a processor and memory. The computer receives a product class representing a product in a supply chain network including one or more supply chain entities and generates one or more new products for the product class using one or more automatically generated templates including a graphical representation of an exemplary product using a first smart product attribute value, the first smart product attribute value defined by a quantifiable measurement of a product attribute. The computer further causes items to be transported among the one or more supply chain entities to restock the inventory of the one or more items of the product class according to the current state of items in the supply chain network and the one or more new products.

First claim

What is claimed is:

1. A computer-implemented method for assortment planning using smart attributes by a computer, comprising:
receiving a product class representing a product in a supply chain network comprising one or more supply chain entities, wherein an inventory of the one or more supply chain entities is used to store one or more items of the product class;
generating one or more new products for the product class using one or more automatically generated templates comprising a graphical representation of an exemplary product using a first smart product attribute value, the first smart product attribute value defined by a quantifiable measurement of a product attribute;
generating a user interface comprising a virtual canvas having a display area and one or more tools and rendering a product model for display on the display area of the virtual canvas, wherein the attribute values of the product model correspond to product features of a modeled product; and
displaying a graphic representation of the product model that represents the attribute values of the product model; and
causing items to be transported among the one or more supply chain entities to restock the inventory of the one or more items of the product class according to the current state of items in the supply chain network and the one or more new products.

2. The computer-implemented method of claim 1 , further comprising:
calculating contribution of different product features from encoded smart attribute values; and
generating predictions for product features that will affect future demand for a new product assortment.

3. The computer-implemented model of claim 2 , further comprising:
detecting user input on the virtual canvas in connection with the displayed representations of the attribute values of the product model;
modifying the attribute values of the product model in accordance with one or more tools that alter the displayed representations of the attribute values of the product model; and
using smart attribute definitions to update the attribute values of a data representation of the product according to the modified attribute values.

4. The computer-implemented model of claim 3 , further comprising:
displaying a product image upload interface of the user interface;
receiving one or more images of one or more planned assortment products by the product image upload interface;
processing the received one or more images by one or more machine learning techniques to identify one or more features of the planned assortment products of the one or more images;
identifying one or more recommended templates of the one or more automatically generated templates, wherein the recommended templates comprise one or more attribute values that correspond to one or more of the identified features of the planned assortment products; and
generating one or more planned assortment product models comprising attributes and attribute values corresponding to the one or more recommended templates.

5. The computer-implemented model of claim 4 , further comprising:
displaying on the user interface the one or more recommended templates that correspond to one or more of the identified features of the planned assortment products;
detecting by the user interface selection of the one or more recommended templates;
scoring the selection of the one or more recommended templates according to the one or more images of the one or more planned assortment products; and
training the one or more machine learning techniques using the scored selections of the one or more recommended templates.

6. The computer-implemented model of claim 5 , wherein the one or more new products are clothing, and the product features encoded by the smart attribute values are grouped into one or more smart attributes that correspond to one or more of a color, material, design, pattern, and size.

7. A system for assortment planning using smart attributes, comprising:
a computer comprising a processor and a memory, the computer configured to:
receive a product class representing a product in a supply chain network comprising one or more supply chain entities, wherein an inventory of the one or more supply chain entities is used to store one or more items of the product class;
generate one or more new products for the product class using one or more automatically generated templates comprising a graphical representation of an exemplary product using a first smart product attribute value, the first smart product attribute value defined by a quantifiable measurement of a product attribute;
generate a user interface comprising a virtual canvas having a display area and one or more tools and render a product model for display on the display area of the virtual canvas, wherein the attribute values of the product model correspond to product features of a modeled product;
display a graphic representation of the product model that represents the attribute values of the product model; and
cause items to be transported among the one or more supply chain entities to restock the inventory of the one or more items of the product class according to the current state of items in the supply chain network and the one or more new products.

8. The system of claim 7 , wherein the computer is further configured to:
calculate contribution of different product features from encoded smart attribute values; and
generate predictions for product features that will affect future demand for a new product assortment.

9. The system of claim 8 , wherein the computer is further configured to:
detect user input on the virtual canvas in connection with the displayed representations of the attribute values of the product model;
modify the attribute values of the product model in accordance with one or more tools that alter the displayed representations of the attribute values of the product model; and
use smart attribute definitions to update the attribute values of a data representation of the product according to the modified attribute values.

10. The system of claim 9 , wherein the computer is further configured to:
display a product image upload interface of the user interface;
receive one or more images of one or more planned assortment products by the product image upload interface;
process the received one or more images by one or more machine learning techniques to identify one or more features of the planned assortment products of the one or more images;
identify one or more recommended templates of the one or more automatically generated templates, wherein the recommended templates comprise one or more attribute values that correspond to one or more of the identified features of the planned assortment products; and
generate one or more planned assortment product models comprising attributes and attribute values corresponding to the one or more recommended templates.

11. The system of claim 10 , wherein the computer is further configured to:
display on the user interface the one or more recommended templates that correspond to one or more of the identified features of the planned assortment products;
detect by the user interface selection of the one or more recommended templates;
score the selection of the one or more recommended templates according to the one or more images of the one or more planned assortment products; and
train the one or more machine learning techniques using the scored selections of the one or more recommended templates.

12. The system of claim 11 , wherein the one or more new products are clothing, and the product features encoded by the smart attribute values are grouped into one or more smart attributes that corre

Assignees

• Jda Software Group Inc
• Blue Yonder Group Inc

Inventors

• Grande Karine
• Melancon Gabrielle Gauthier

Classifications

• G06N3/045

  Combinations of networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/08

  Learning methods · CPC title

• G06Q10/087 Primary

  Inventory or stock management, e.g. order filling, procurement or balancing against orders · CPC title