Twilio Inc.

Luke Percival De Oliveira

Alfredo Láinez Rodrigo

Umair Akeel

Tyler Cole

Nicolas Acosta Amador

Byeongung Ahn

Sahil Kumar

Liat Barda Dremer

Methods, systems, and computer programs are presented for formatting raw text. One method includes an operation for accessing raw text comprising words corresponding to one or more sentences. The raw text is lowercase text without any punctuation. Further, the method includes operations for creating a plurality of sub-words corresponding to the raw text, and for generating, by a machine-learning (ML) model, an output for each sub-word based on the created sub-words. The output for each sub-word indicates a formatting operation for the corresponding sub-word. The method further includes an operation for generating, based on the formatting operations in the outputs for the sub-words, formatted text corresponding to the raw text. The formatted text is text with correct grammar, proper punctuation, and proper capitalization according to a meaning of words spoken by a speaker associated with the raw text.

Text formatter

Methods, systems, and computer programs are presented for searching the content of voice conversations. The conversations are translated into text and analysis of the conversation is performed to identify information in the conversation. The information identified includes turns taking data in the conversation and states identified within each state. A powerful user interface (UI) is provided to review the conversations and add annotations that tag the different turns. Additionally, parameter values are extracted from the text. A powerful search engine is provided with multiple search options, such as searching for text, searching by state within the conversation, searching by parameters extracted from the conversation, or a combination thereof.

Transition-driven transcript search

Methods, systems, and computer programs are presented for classifying information in conversations and extracting information from the conversations. An Engagement Intelligence Platform (EIP) analyzes transcripts of conversations to find different states and information associated with each of the states (e.g., identification that the interest rate was quoted, and the quoted value of the interest rate). The EIP analyzes the conversation and labels (e.g., 'tags') the text where the conversation associated with the label took place, such as, “An interest rate was provided.” The labels are customizable, so each client can define its own labels based on business needs. Further, the EIP extracts data from the conversation (e.g., the interest rate is “3%”).

Tool for categorizing and extracting data from audio conversations

Methods, systems, and computer programs are presented for abstractive summarization of text by viewing sequence transduction as a language modeling problem. One method comprises an operation for training a machine-learning program to create a machine-learning model that estimates a word to be added to a running summary for the text being summarized. The method further comprises operations for detecting the text to be summarized, initializing the running summary, and performing a plurality of iterations. Each iteration comprises providing, to the machine-learning model, the source text and the running summary, and adding, using the machine-learning model, a new word to the running summary. Further, the method comprises an operation for storing, on a memory, the running summary as the summary of the text.

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San Francisco, CA, United States of America

Luke Percival De Oliveira