Ebay, Incorporated

Twilio Inc.

Elaine Lee

Amit Srivastava

Tomer Lancewicki

Braddock Craig Gaskill

Farah Abdallah

Robert Enyedi

Dominique Jean Bouchon

Xinyu Zhang

Jayanth Vasudevan

Nicolas Acosta Amador

Tyler Cole

Byeongung Ahn

Sahil Kumar

Pablo Pérez Diez

Eduardo Pardo San Llorente

Aspect pre-selection techniques using machine learning are described. In one example, an artificial assistant system is configured to implement a chat bot. A user then engages in a first natural-language conversation. As part of this first natural-language conversation, a communication is generated by the chat bot to prompt the user to specify an aspect of a category that is a subject of a first natural-language conversation and user data is received in response. Data that describes this first natural-language conversation is used to train a model using machine learning. Data is then received by the chat bot as part of a second natural-language conversation. This data, from the second natural-language conversation, is processed using the model as part of machine learning to generate the second search query to include the aspect of the category automatically and without user intervention.

Aspect pre-selection using machine learning

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

Aspect pre-selection techniques using machine learning are described. In one example, an artificial assistant system is configured to implement a chat bot. A user then engages in a first natural-language conversation. As part of this first natural-language conversation, a communication is generated by the chat bot to prompt the user to specify an aspect of a category that is a subject of a first natural-language conversation and user data is received in response. Data that describes this first natural-language conversation is used to train a model using machine learning. Data, is then be received by the chat bot as part of a second natural-language conversation. This data, from the second natural-language conversation, is processed using the model as part of machine learning to generate the second search query to include the aspect of the category automatically and without user intervention.

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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Elaine Lee