Amazon Technologies, Inc.

University of Southern California

Kemal Oral Cansizlar

Li Zhou

Ashish Kumar Agrawal

Arindam Mandal

Scott Allen Mongrain

Rahul H Bhagat

Kevin Michael Small

Raefer Christopher Gabriel

Suranjit Adhikari

Roger Zimmermann

Ravendar Lal

Peng Shao

Aritra Biswas

Constantin Daniel Marcu

Taruja Sanjay Parande

Beomjoo Seo

Leslie S Liu

Vishnu Narayanan

Shucheng Zhu

Bhanu Pratap Jain

Vaclav Petricek

Vijit Singh

Srinivasa Sandeep Atluri

Techniques for conversational-based searching are described. A system may receive a first spoken user input, and may determine that the first spoken user input corresponds to a request for information associated with an entity. The system may retrieve item results corresponding to the entity. The system may determine a suggested user input based on the retrieved item results, and may determine output data corresponding to the suggested user input. The system may send output data to a user device, where the output data includes the item results and the suggested user input. The system may receive a second spoken user input, and may determine that the second spoken user input corresponds to the suggested user input. In response, the system may send the previously determined output data to the device.

Voice based searching and dialog management system

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

User interactions with presented content can be tracked and a corresponding user behavior identified. An enhancement trigger can be detected by comparing the user behavior with a pattern of behavior associated with the enhancement trigger. A modified experience can be provided at the user device. The modified experience can include a user interface for interacting with a chat bot.

Search result refinement and item information exploration

Disclosed are various embodiments for recommending a list of digital media to a user based upon digital media that the user is currently consuming. The list of recommendations is generated from an analysis of the digital media. Subsequently, the list of recommendations is stored on the user's device, and the user is alerted to the list's existence through the rendering of a notification on the user's display. Upon selection of an item from the list of recommendations, the selected digital media is rendered on the display of the user's device within the context of the user's application.

Digital media relationship analyzer and recommender

Widgets or other code modules for applications written in structured languages such as HTML can be tested advantageously by separating the extraction and validation logic. To test the functionality of a widget in response to an action, widget features of interest are identified. Context and feature nodes for these features are extracted from a page tree structure to generate a property vector that is representative of the state of the widget. After performing the action, context nodes for the features are attached to the new page tree for the newly loaded page, and an observed property vector is generated that represents the state of the widget after the action. The previous property vector is used with updated state information to generate an expected property vector. The observed property vector is compared with the expected property vector, and if the vectors match the validation is claimed to be successful.

Systems and methods for testing widgets

Systems and methods for testing widgets in computer environments

A peer to peer audio communication screen in system with adaptive bandwidth control. The peer-to-peer system can be used, for example, in classroom applications. More interactive peers are moved to a position where they will receive lower latency from the peer-to-peer system. This may be done by maintaining a score indicative of the amount of interactivity, and then using that score to move a peer either close to the source of audio packets, e.g., the core or further from that source.

Audio chat system based on peer-to-peer architecture

Growing community of inventors

Seattle, WA, United States of America

Kemal Oral Cansizlar