Conversenowai

Julia Milanese

Vinay Kumar Shukla

Vrajesh Navinchandra Sejpal

Rahul Aggarwal

Zubair Talib

Fernando Ezequiel Gonzalez

Matias Grinberg

Akshay Labh Kayastha

Yuganeshan A J

Pranav Nirmal Mehra

German Kurt Grin

Leonardo Cordoba

In some examples, a software agent executing on a server receives a communication comprising a first utterance from a customer and predicts, using an intent classifier, a first intent of the first utterance. Based on determining that the first intent is order-related, the software agent predicts, using a dish classifier, a cart delta vector based at least in part on the first utterance and modifies a cart associated with the customer based on the cart delta vector. The software agent predicts, using a dialog model, a first dialog response based at least in part on the first utterance and provides the first dialog response to the customer using a text-to-speech converter.

Automated ordering system

In some examples, a software agent executing on a server an utterance from a customer. The software agent converts the utterance to text. The software agent creates an audio representation of the text and performs a comparison of the audio representation and the utterance. The software agent creates edited text based on the comparison. For example, the software agent may determine, based on the comparison, audio differences between the audio representation and the utterance, create a sequence of edit actions based on the audio differences, and apply the sequence of edit actions to the text to create the edited text. The software agent outputs the edited text as a dialog response to the utterance.

Using machine learning to correct the output of an automatic speech recognition system

Growing community of inventors

Berlin, Germany

Julia Milanese