Abstract: In some aspects, a computing device receives a scan of a code displayed on an order post located near a restaurant, determines that the code is associated with the restaurant, and automatically opens a software application and navigates the software application to an ordering page associated with the restaurant. The computing device initiates receiving, via the software application, input associated with an order, sends the input to a machine learning based software agent executing on a server, receives a predicted response to the input, provides the predicted response as audio output and/or displays the predicted response on the touchscreen display device. After the order is complete, the computing device sends order data associated with the order to the restaurant. After receiving an indication from the restaurant that the order is ready, the computing device indicates that the order is ready to be picked up.

Abstract: In some aspects, an edge appliance is placed in an active mode and causes a software agent that is based on a machine learning algorithm to engage in a conversation to take an order from a customer that is located at an order post. The edge appliance provides, using a communication interface, audio data that includes the conversation, to a communications system of a restaurant. The edge appliance provides, using the communication interface, a content of a cart associated with the order to a point-of-sale terminal of the restaurant. If the edge appliance determines, using the communication interface, that a microphone of the communication system is receiving audio input from an employee, the edge appliance automatically transitions the edge appliance from the active mode to an override mode, enabling the employee to receive a remainder of the order from the customer.

Abstract: In some aspects, an edge appliance is placed in an active mode and causes a software agent that is based on a machine learning algorithm to engage in a conversation to take an order from a customer that is located at an order post. The edge appliance provides, using a communication interface, audio data that includes the conversation, to a communications system of a restaurant. The edge appliance provides, using the communication interface, a content of a cart associated with the order to a point-of-sale terminal of the restaurant. If the edge appliance determines, using the communication interface, that a microphone of the communication system is receiving audio input from an employee, the edge appliance automatically transitions the edge appliance from the active mode to an override mode, enabling the employee to receive a remainder of the order from the customer.

Abstract: In some aspects, a computing device receives a scan of a code displayed on an order post located near a restaurant, determines that the code is associated with the restaurant, and automatically opens a software application and navigates the software application to an ordering page associated with the restaurant. The computing device initiates receiving, via the software application, input associated with an order, sends the input to a machine learning based software agent executing on a server, receives a predicted response to the input, provides the predicted response as audio output and/or displays the predicted response on the touchscreen display device. After the order is complete, the computing device sends order data associated with the order to the restaurant. After receiving an indication from the restaurant that the order is ready, the computing device indicates that the order is ready to be picked up.

Abstract: A software agent, comprising a machine learning algorithm trained to engage in a conversation with a customer to take an order, receives an utterance from a customer. The utterance is converted to text and an analysis of the text performed. If the software agent determines, based on the analysis, that the software agent is untrained to respond to the text, the software agent establishes a connection to a point-of-sale device associated with a human agent. The human agent may perform a modification (e.g., an edit to the text, a modification to a cart, or provide input) to a modifiable portion displayed by the point-of-sale device. The software agent, based at least in part on the modification, resumes the conversation with the customer. The human agent does not directly interact with the customer during the conversation between the software agent and the customer.

Abstract: In some aspects, an order post detects, using one or more sensors, a presence of a customer, determines an identity of the customer, retrieves previous orders of the customer, indicates at least one item in the previous orders, receives an order comprising input that includes an utterance of the customer, modifies the utterance to create a modified utterance, sends the modified utterance to a software agent comprising a natural language processor and one or more classifiers, receives a predicted response to the modified utterance from the software agent, plays back the predicted response via the speaker, determines that the order is complete, receives payment information for the order from the customer, sends order data associated with the order to a restaurant, receives an indication from the restaurant that the order is ready for pickup, and instructs the customer to pick up the order.