Samsung Electronics Co., Ltd.

Amazon Technologies, Inc.

Suranjit Adhikari

Arindam Mandal

Stacie Lynn Hibino

Raefer Christopher Gabriel

Dilek Zeynep Hakkani-Tur

Rohit Prasad

Ashish Kumar Agrawal

Nikko Strom

Jeffrey Scott Pierce

Chunkwok Lee

Angeliki Metallinou

Tagyoung Chung

Kemal Oral Cansizlar

Dion M Isselhardt

Dhana Dhanasarnsombat

Tom Brinck

Daniela Karin Busse

Tao Ma

Moe Tanabian

Esther Kim

Parker Ralph Kuncl

Qing Liu

Sridhar Yadav Manoharan

Ankita De

James Roy Carl Hicks

Shucheng Zhu

Techniques for conversation recovery in a dialog management system are described. A system may determine, using dialog models, that a predicted action to be performed by a skill component is likely to result in an undesired response or that the skill component is unable to respond to a user input of a dialog session. Rather than informing the user that the skill component is unable to respond, the system may send data to the skill component to enable the skill component to determine a correct action responsive to the user input. The data may include an indication of the predicted action and/or entity data corresponding to the user input. The system may receive, from the skill component, response data corresponding to the user input, and may use the response data to update a dialog context for the dialog session and an inference engine of the dialog management system.

Dialog management system

A dialog manager receives text data corresponding to a dialog with a user. Entities represented in the text data are identified. Context data relating to the dialog is maintained, which may include prior dialog, prior API calls, user profile information, or other data. Using the text data and the context data, an N-best list of one or more dialog models is selected to process the text data. After processing the text data, the outputs of the N-best models are ranked and a top-scoring output is selected. The top-scoring output may be an API call and/or an audio prompt.

Goal-oriented dialog system

In one aspect, a method for detecting an object and/or managing an inventory will be described. A motion of an object is detected at a storage structure. One or more images are acquired of at least a part of the object. A direction of the motion of the object is determined. An object is identified based on the one or more images. In various embodiments, an inventory for the storage structure is updated based on the motion direction determination and the object identity.

Object recognition for a storage structure

An electronic system includes: a communication unit is configured to communicate a challenge; a user interface, coupled to the communication unit is configured to select rich content for a point of view based on the challenge; and a storage unit, coupled to the user interface, is configured to capture a response to the point of view based on the challenge.

Electronic system with challenge mechanism and method of operation thereof

An electronic system includes a storage interface configured to access a kinematic model for representing one or more motions of a carrier; a control unit, coupled to the storage interface, configured to determine an attachment location of an attachable device based on the kinematic model; and generate a device setting based on the attachment location for provisioning the attachable device.

Electronic system with dynamic localization mechanism and method of operation thereof

A method of operation of a display system includes: receiving a recap request for a source media; generating source content descriptors for the source media with a control unit; extracting a source recap segment from the source media based on correlation of the source content descriptors with a recap theme associated with the recap request; and generating a custom recap with the source recap segment for displaying on a device.

Display system with media processing mechanism and method of operation thereof

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San Jose, CA, United States of America

Suranjit Adhikari