US 12,340,358 B2
Artificial intelligence (AI) order taking system enabling agents to make corrections via point of sale (POS) devices
Jon Dorch, Austin, TX (US); Zubair Talib, Irvine, CA (US); Ruchi Bafna, Bengaluru (IN); Akshaya Labh Kayastha, Karnataka (IN); Yuganeshan Aj, Karnataka (IN); Vinay Kumar Shukla, Austin, TX (US); and Rahul Aggarwal, Austin, TX (US)
Assigned to ConverseNowAI, Austin, TX (US)
Filed by ConverseNowAI, Austin, TX (US)
Filed on May 17, 2022, as Appl. No. 17/746,931.
Application 17/746,931 is a continuation in part of application No. 17/184,207, filed on Feb. 24, 2021, granted, now 11,810,550.
Prior Publication US 2022/0277282 A1, Sep. 1, 2022
Int. Cl. G06Q 20/20 (2012.01); G06N 20/00 (2019.01); G06Q 30/0601 (2023.01)
CPC G06Q 20/203 (2013.01) [G06N 20/00 (2019.01); G06Q 20/202 (2013.01); G06Q 30/0601 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
training a software agent comprising a machine learning algorithm using conversation data that includes hundreds of thousands of conversations;
after training the software agent, deploying the software agent on a server;
receiving, by the software agent, an utterance from a customer, the software agent trained to engage in a conversation with the customer to take an order;
creating, via the software agent, a cart associated with the order;
converting, via the software agent, the utterance to text;
providing, to an encoder of a natural language processing pipeline, as structured data:
an order context including an interaction history between the software agent and the customer; and
a conversation state;
predicting, by the encoder, based on the text and the structured data, an utterance vector;
determining, based at least in part on the utterance vector, that the software agent is untrained to respond to the text;
establishing a connection between:
the server hosting the software agent; and
a point-of-sale device that is associated with a human agent;
receiving, from the human agent, a modification to order data displayed by the point-of-sale device;
resuming, by the software agent and based at least in part on the modification to the order data, the conversation with the customer, wherein the human agent does not directly interact with the customer during the conversation between the software agent and the customer; and
after determining, by the software agent, that the customer has completed the order, retraining the software agent based at least in part on:
the conversation between the software agent and the customer;
the text;
a content of the cart; and the modification made by the human agent,
the retraining causing an improvement in an accuracy of the software agent in order taking.