Amazon Technologies, Inc.

Mugunthan Govindaraju

Brandon V Taylor

Aparna Nandyal

Reza Abdollahi

Gaurav Singh

Joseph Spencer King

Eric Allen Menninga

Vasant Manohar

Christopher Geiger Parker

Leopold Bushkin

Piyush Bhargava

Rajagopalan Ranganathan

Ashley Alonzo Ricardo Karl Mitchell

Suresh Boddu

Jintomon Joseph

Yamini Muralitharan

Vidya Narasimhan

This disclosure relates to answer prediction in a speech processing system. The system may disambiguate entities spoken or implied in a request to initiate an action with respect to a target user. To initiate the action, the system may determine one or more parameters; for example, the target (e.g., a contact/recipient), a source (e.g., a caller/requesting user), and a network (voice over internet protocol (VOIP), cellular, video chat, etc.). Due to the privacy implications of initiating actions involving data transfers between parties, the system may apply a high threshold for a confidence associated with each parameter. Rather than ask multiple follow-up questions, which may frustrate the requesting user, the system may attempt to disambiguate or determine a parameter, and skip a question regarding the parameter if it can predict an answer with high confidence. The system can improve the customer experience while maintaining security for actions involving, for example, communications.

Answer prediction in a speech processing system

A speech-processing system may provide access to multiple virtual assistants. Speech-processing systems may perform actions for or on behalf of users with the aid of skills; e.g., a shopping skill, navigation skill, communications skill, etc. Some skills may be associated with more than one assistant. The speech-processing system may determine which assistant to invoke upon receiving a command from a user device. The identity of the virtual assistant is propagated to the skill and the device, as well as other components of the speech-processing system. In some cases, however, a multi-assistant skill may determine that an assistant other than the one initially selected by the speech-processing system is to handle the command. The skill may send the identity of the new assistant back to the speech-processing system. The speech-processing system may restart the command dissemination process to provide each component of the system with the updated assistant identity.

Assistant determination in a skill

Techniques for enabling a system to create a multimedia messaging service (MMS) message or a short message service (SMS) message from a spoken message are described. A system may receive audio data corresponding to a spoken message. The system may determine a recipient of the spoken message as well as a device associated with the recipient. The system may determine the type of messaging supported by the device. Based on the messaging capabilities, the system generates either a MMS message or a SMS message. The MMS message may include a message payload including a portion of text corresponding to content of the message as well as audio data embedded in the message payload. The SMS message may include a message payload including a portion of text corresponding to content of the message as well as a link to message content audio data stored by the system.

Voice-based messaging

Disclosed are techniques and systems to detect a layout of a source document. A process may include receiving content from a first page and a second page of the source document, designating sections in each page along a first direction of the page, and assigning similar sections to a group. For the group, the process may proceed by dividing sections for each page into discrete portions associated with 2D coordinate areas, and identifying sets of 2D coordinate areas for the discrete portions that contain content. The number of times each portion contains some content may be compared to a threshold to determine a layout of the group of sections.

Detection of layouts in electronic documents

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Chennai, India

Mugunthan Govindaraju