US-20260127546-A1 - METHODS AND SYSTEMS FOR INTELLIGENTLY AND ADAPTIVELY MANAGING AND USING DATA IN A SUPPLY CHAIN ENVIRONMENT

US20260127546A1US 20260127546 A1US20260127546 A1US 20260127546A1US-20260127546-A1

Abstract

Disclosed herein are systems and methods for the automated ingestion and processing of orders for the food supply chain industry using artificial intelligence. An example method can comprise extracting order level information and item level information from a purchase order in the form of at least one of an email message, a text message, a voicemail audio file, an image file, a spreadsheet file, and a portable document format (PDF) file. The method can also comprise preprocessing the order level information and the item level information into a plurality of machine learning features and inputting the machine learning features into a machine learning model to obtain predictions concerning the purchase order. A sales order can then be generated based in part on the predictions outputted by the machine learning model.

Inventors

Shangyan LI
David Qiuye YANG

Assignees

GRUBMARKET, INC.

Dates

Publication Date: 20260507
Application Date: 20251106

Claims (20)

1 . A method of processing an order, comprising: extracting, using a large language model (LLM), order level information and item level information from a purchase order, wherein the purchase order is in the form of at least one of an email message, a text message, a voicemail audio file, an image file, a spreadsheet file, and a portable document format (PDF) file; preprocessing the order level information and the item level information into a plurality of machine learning features, wherein the order level information comprises at least a customer name, a delivery date, and a customer purchase order number, and wherein the item level information comprises one or more generic product names; inputting the machine learning features into a machine learning model to obtain predictions concerning a product code from a product database corresponding to one of the generic product names, the delivery date, the customer purchase order number, and a customer record corresponding to the customer name; and generating a sales order based in part on the product code, the delivery date, the customer purchase order number, and the customer record predicted by the machine learning model and displaying the sales order to a supplier via a supplier client device.
2 . The method of claim 1 , further comprising: displaying, via the supplier client device, the sales order to the supplier via an editable dashboard graphical user interface (GUI); receiving one or more corrections to the sales order from the supplier via user inputs applied to the dashboard GUI resulting in a corrected sales order; comparing the one or more corrections to the predictions outputted by the machine learning model; and adjusting or fine-tuning a plurality of weights of the machine learning model until new predictions outputted by the machine learning model match the corrected sales order.
3 . The method of claim 1 , wherein preprocessing the item level information further comprises preprocessing the one or more generic product names into the plurality of machine learning features, wherein the machine learning features comprises a fuzzy text match score and a semantic embedding similarity score.
4 . The method of claim 3 , further comprising inputting a plurality of auxiliary features into the machine learning model to obtain the predictions concerning the product code, wherein the auxiliary features are not explicitly included as part of the purchase order, wherein the auxiliary features comprise data or information concerning a day that the purchase order was received, a month that the purchase order was received, a current season during which the purchase order was received, and a current weather condition during which the purchase order was received.
5 . The method of claim 4 , further comprising inputting a plurality of customer-specific features into the machine learning model to obtain the predictions concerning the product code, wherein the plurality of customer-specific features are not explicitly included as part of the purchase order, wherein the plurality of customer-specific features comprise an order history associated with a customer, an order frequency associated with the customer, and an order guide or product list made available to the customer.
6 . The method of claim 1 , wherein the machine learning model is another instance of the LLM or an additional LLM.
7 . The method of claim 1 , wherein the purchase order is divided into a first partial order and a second partial order, wherein the first partial order is in the form of the email message, the text message, the voicemail audio file, the image file, the spreadsheet file, or the PDF file and wherein the second partial order is in a different form from the first partial order.
8 . The method of claim 1 , wherein the purchase order is in the form of the voicemail audio file, wherein the method further comprises transcribing the voicemail audio file into transcribed text using an additional LLM and extracting the order level information and the item level information from the transcribed text.
9 . The method of claim 1 , wherein extracting the order level information using the LLM further comprises extracting a shipping address from the purchase order.
10 . The method of claim 1 , wherein extracting the item level information using the LLM further comprises extracting units of measure, quantities, and prices from the purchase order.
11 . The method of claim 1 , further comprising displaying an order graphical user interface (order GUI) on the supplier client device and extracting the order level information and item level information from the purchase order in response to the supplier dragging and dropping the voicemail audio file or the PDF file onto the order GUI.
12 . The method of claim 1 , further comprising automatically adding the sales order to an enterprise resource planning (EPR) database.
13 . A system for processing orders, the system comprising: a server comprising one or more processors and one or more memory units communicatively coupled to the one or more processors, wherein the one or more memory units store instructions that, when executed by the one or more processors, cause the one or more processors to: extract, using a large language model (LLM), order level information and item level information from a purchase order, wherein the purchase order is in the form of at least one of an email message, a text message, a voicemail audio file, an image file, a spreadsheet file, and a portable document format (PDF) file, preprocess the order level information and the item level information into a plurality of machine learning features, wherein the order level information comprises at least a customer name, a delivery date, and a customer purchase order number, and wherein the item level information comprises one or more generic product names, input the machine learning features into a machine learning model to obtain predictions concerning a product code from a product database corresponding to one of the generic product names, the delivery date, the customer purchase order number, and a customer record corresponding to the customer name, and generate a sales order based in part on the product code, the delivery date, the customer purchase order number, and the customer record predicted by the machine learning model; and a supplier client device communicatively coupled to the server, wherein the supplier client device is configured to display the sales order generated by the server to a supplier.
14 . The system of claim 13 , wherein the one or more memory units store instructions that, when executed by the one or more processors, further cause the one or more processors to: instruct the supplier client device to display the sales order to the supplier via an editable dashboard graphical user interface (GUI); receive one or more corrections to the sales order from the supplier via user inputs applied to the dashboard GUI resulting in a corrected sales order; compare the one or more corrections to the predictions outputted by the machine learning model; and adjust or fine-tune a plurality of weights of the machine learning model until new predictions outputted by the machine learning model match the corrected sales order.
15 . The system of claim 13 , wherein the one or more memory units store instructions that, when executed by the one or more processors, further cause the one or more processors to preprocess the item level information by further preprocessing the one or more generic product names into the plurality of machine learning features, wherein the machine learning features comprises a fuzzy text match score and a semantic embedding similarity score.
16 . The system of claim 15 , wherein the one or more memory units store instructions that, when executed by the one or more processors, further cause the one or more processors to input a plurality of auxiliary features into the machine learning model to obtain the predictions concerning the product code, wherein the auxiliary features are not explicitly included as part of the purchase order, wherein the auxiliary features comprise data or information concerning a day that the purchase order was received, a month that the purchase order was received, a current season during which the purchase order was received, and a current weather condition during which the purchase order was received.
17 . The system of claim 16 , wherein the one or more memory units store instructions that, when executed by the one or more processors, further cause the one or more processors to input a plurality of customer-specific features into the machine learning model to obtain the predictions concerning the product code, wherein the plurality of customer-specific features are not explicitly included as part of the purchase order, wherein the plurality of customer-specific features comprise an order history associated with a customer, an order frequency associated with the customer, and an order guide or product list made available to the customer.
18 . The system of claim 13 , wherein the machine learning model is another instance of the LLM or an additional LLM.
19 . The system of claim 13 , wherein the purchase order is divided into a first partial order and a second partial order, wherein the first partial order is in the form of the email message, the text message, the voicemail audio file, the image file, the spreadsheet file, or the PDF file and wherein the second partial order is in a different form from the first partial order.
20 . One or more non-transitory computer-readable media comprising instructions stored thereon, that when executed by one or more processors, cause the one or more processors to perform operations comprising: extracting, using a large language model (LLM), order level information and item level information from a purchase order, wherein the purchase order is in the form of at least one of an email message, a text message, a voicemail audio file, an image file, a spreadsheet file, and a portable document format (PDF) file; preprocessing the order level information and the item level information into a plurality of machine learning features, wherein the order level information comprises at least a customer name, a delivery date, and a customer purchase order number, and wherein the item level information comprises one or more generic product names; inputting the machine learning features into a machine learning model to obtain predictions concerning a product code from a product database corresponding to one of the generic product names, the delivery date, the customer purchase order number, and a customer record corresponding to the customer name; and generating a sales order based in part on the product code, the delivery date, the customer purchase order number, and the customer record predicted by the machine learning model and displaying the sales order to a supplier via a supplier client device.

Description

CROSS-REFERENCE TO RELATED APPLICATION This application claims the benefit of U.S. Provisional Application No. 63/717,508, filed Nov. 7, 2024, which is incorporated herein by reference in its entirety. TECHNICAL FIELD This disclosure relates generally to the field of supply chain management software and more specifically, to systems and methods for the automated ingestion and processing of orders using artificial intelligence. BACKGROUND Many purchasers or buyers (e.g., grocery store purchasers, retail store purchasers, restaurant buyers, etc.) in the food supply chain industry still prefer to place their orders for suppliers or distributors over email, voicemail, or text messaging. This is due to the fact that such purchasers often order anywhere between 10+ to 100+ items and often accompany these orders with customized instructions for the items in these orders. Purchasers often do not have the time to browse and search through a wholesale food distributor's vast product catalog. These purchasers would prefer to let the distributor know what items they need and let the distributor figure out how best to match the purchaser's order with the actual products in the distributor's product catalog. This creates the all-too-common scenario where the distributor or supplier must expend a significant amount of time to carefully match the purchaser's requested items, often sent via email, voicemail, and/or text messages, with the products in the supplier's or distributor's product catalog and manually key in every item being ordered. Manual ordering not only slows down operations but is also prone to human error, which can result in costly delays and a poor customer experience. Therefore, a solution is needed that leverages the power of AI to automate the ingestion and processing of orders. Such a solution should be user-friendly and cost-effective to deploy and manage. SUMMARY Disclosed are systems and methods for the automated ingestion and processing of orders in a supply chain environment (e.g., a food supply chain environment) using artificial intelligence (AI). As will be discussed in more detail in the following sections, the systems and methods disclosed herein can leverage AI to ingest inbound purchase orders from numerous purchasers or customers in various formats and automatically generate sales orders based on such inbound purchase orders for numerous suppliers accurately and efficiently. The system can also push the sales orders directly into a supplier's enterprise resource planning (ERP) system automatically. This saves significant resources for the suppliers in terms of time and labor costs The AI models used by the system can self-learn and improve its performance over time as more orders are processed. In some embodiments, a method of automatically ingesting and processing an order comprises extracting, using a large language model (LLM), order level information and item level information from a purchase order. The purchase order can be in the form of an email message, a text message, a voicemail audio file, an image file (e.g., a digital photo of an order), a spreadsheet file, a portable document format (PDF) file, or a combination thereof. The order level information comprises at least a customer name, a delivery date, and a customer purchase order (PO) number. The item level information comprises one or more generic product names. The method further comprises preprocessing the order level information and the item level information into a plurality of machine learning features. The method also comprises inputting the machine learning features into a machine learning model to obtain predictions concerning a product code from a product database corresponding to one of the generic product names, the delivery date, the customer purchase order number, and a customer record corresponding to the customer name. The method further comprises automatically generating a sales order from the inbound purchase order based in part on the product code, the delivery date, the customer purchase order number, and the customer record predicted by the machine learning model and displaying the sales order to a supplier via a supplier client device. In some embodiments, the method further comprises displaying, via the supplier client device, the sales order to the supplier via an editable graphical user interface (GUI) and receiving one or more corrections to the sales order from the supplier via user inputs applied to the editable GUI resulting in a corrected sales order. The method also comprises comparing the one or more corrections to the predictions outputted by the machine learning model and adjusting or fine-tuning a plurality of weights of the machine learning model until new predictions outputted by the machine learning model using the same inputs match or more closely align with the corrected sales order. In some embodiments, preprocessing the item level information further comprises preprocessing the one or