Twilio Inc.

Tyler Cole

Umair Akeel

Luke Percival De Oliveira

Alfredo Láinez Rodrigo

Nicolas Acosta Amador

Byeongung Ahn

Sahil Kumar

Elaine Lee

Eduardo Pardo San Llorente

Pablo Pérez Diez

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 and labeling the content of voice conversations. An Engagement Intelligence Platform (EIP) analyzes conversation transcripts to find states and information for each of the states (e.g., interest rate quoted and value of the interest rate). An annotator User Interface (IU) is provided for performing queries, such as, 'Find calls were the agent asked the customer for their name and the customer did not answer;' “Find calls where the customer objected after the interest rate for the loan was quoted, “Find calls where the agent asked for consent for recording the call, but no customer confirmation was received.” 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.

Tool for annotating and reviewing audio conversations

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 searching and labeling the content of voice conversations. An Engagement Intelligence Platform (EIP) analyzes conversation transcripts to find states and information for each of the states (e.g., interest rate quoted and value of the interest rate). An annotator User Interface (IU) is provided for performing queries, such as, 'Find calls were the agent asked the customer for their name and the customer did not answer;' 'Find calls where the customer objected after the interest rate for the loan was quoted, &#x201c;Find calls where the agent asked for consent for recording the call, but no customer confirmation was received.' The EIP analyzes the conversation and labels (e.g., &#x201c;tags&#x201d;) the text where the conversation associated with the label took place, such as, &#x201c;An interest rate was provided.&#x201d; The labels are customizable, so each client can define its own labels based on business needs.

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Diamond Bar, CA, United States of America

Tyler Cole