Production and logistics management

Patent metadata

Field	Value
Publication number	US-12100018-B2
Application number	US-202217824009-A
Country	US
Kind code	B2
Filing date	May 25, 2022
Priority date	Sep 18, 2019
Publication date	Sep 24, 2024
Grant date	Sep 24, 2024

Abstract

Systems and methods are disclosed for providing a product to consumers by receiving the product from a manufacturer located in a first country; matching the products with a seller in the second country; managing delivery logistics by matching available third-party truckers and third-party warehouses in a second country based on proximity; forecasting demand using one or more neural networks for the manufacturer, the logistics organization, and the warehouses; and managing logistics for the manufacturer through a dashboard populated by the forecasted demand from the one or more neural networks.

First claim

What is claimed is:

1. A method for responding to an order for a product from a customer located in a customer country, comprising:
receiving at a processor product data from a plurality of manufacturers outside of the customer country;
matching the manufacturers with at least a retailer to sell the product;
automatically uploading manufacturer data to a retailer computer including images and dimension data for each product;
training, by the processor, a machine learning model to recommend products for each product type including brands, styles, and sizes customized to customer environments and based on a reference image provided by the customer, wherein the machine learning model includes a generator and a discriminator where the generator generates a first product image from a second product image and the discriminator distinguishes between the first and second product images from the generator;
running the machine learning model based on images from the customer and recommending one or more products to the customer;
receiving, at the processor, a customer order for the product specified using a computer or a smartphone of the customer;
extracting, by the processor, order information from the customer order comprising a geographic location of the customer and mailing address;
recommending one or more products by the machine learning model for the customer from the received images and order information;
generating one or more product images based on the recommended one or more products;
receiving an order for the recommended one or more products and sending the order to the manufacturer located outside of the customer country and managing production for the manufacturer through a dashboard populated with a forecasted demand;
managing delivery logistics by the processor by matching available truckers and warehouses based on proximity;
in response to an order return, generating by the processor a return label and determining a reverse logistic warehouse to receive the product for return; and wherein:
uploading by the manufacturer product information to the server;
searching for a predetermined freight carrier to ship and clear customs when freight arrives in port and deliver to logistic fulfillment warehouse;
matching the manufacturer with an online retailer that wants to sell product with wholesale pricing through an automatic proposal or through a portal and wherein the manufacturer sets marketing and affiliating budget for digital marketing for products; and
retraining the machine learning model with additional first and second images to optimize a recommendation accuracy.

2. The method of claim 1 , wherein the machine learning model comprises one of: one or more neural network, supervised learning network, unsupervised learning network, or reinforcement learning network.

3. The method of claim 1 , comprising managing sales process using the one or more neural networks.

4. The method of claim 1 , comprising managing inventory by forecasting one or more local demands and moving the product close to the one or more local demands.

5. The method of claim 1 , comprising managing shipping based on forecasted demand.

6. The method of claim 1 , comprising managing production based on forecasted demand.

7. The method of claim 1 , comprising uploading product data to a seller or retailer.

8. The method of claim 1 , comprising mapping product data to third party product data.

9. The method of claim 1 , comprising mapping product to a retailer SKU.

10. The method of claim 1 , comprising managing digital assets.

11. The method of claim 1 , comprising providing a marketplace exchange matching manufacturers to retailers.

12. The method of claim 1 , wherein the a first country comprises an ASEAN country or an African country.

13. The method of claim 1 , wherein the a second country comprises one of: European Union, United States, Japan, Korea, China.

14. The method of claim 1 , comprising supporting a remote or offshore supplier with drop shipping where online retailers hold no inventory and the supplier ships the product to the customer, where the customer pays for a product at one or more online retailers that forward the order to the supplier and pay a predetermined discounted price to the supplier for the product.

15. The method of claim 1 , wherein the supplier sends an order to a manufacturer who produces the product and ships the product to the customer.

Assignees

• Tran Bao
• Tran Ha
• Cenports Inc

Inventors

• Tran Bao
• Tran Ha

Classifications

• G06N3/094

  Adversarial learning · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0475

  Generative networks · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06Q30/0202

  Market predictions or forecasting for commercial activities · CPC title