Amazon Technologies, Inc.

Prakash Krishnan

Arindam Mandal

Ranju Das

Barath Balasubramanian

Siddhartha Reddy Jonnalagadda

Raefer Christopher Gabriel

Joaquin Zepeda Salvatierra

Shaun Ryan James McDowell

Niels Brouwers

Anant Patel

Ariya Rastrow

Nikko Strom

Rahul Bhotika

Gurumurthy Swaminathan

Avinash Aghoram Ravichandran

Angeliki Metallinou

Rakesh Madhavan Nambiar

Vincent Auvray

Devesh Mohan Pandey

Anushri Mainthia

David Chi-Wai Tang

Minmin Shen

Rohit Prasad

Ashish Kumar Agrawal

Shiv Naga Prasad Vitaladevuni

Amitabh Saikia

Tagyoung Chung

Ying Shi

Aaron Challenner

Shishir Sridhar Bharathi

Savas Parastatidis

Govindarajan Sundaram Thattai

Anushree Venkatesh

Thomas Edward Taylor

Abhay Kumar Jha

Michael Curtis James

Aishwarya Naresh Reganti

Chien-wei Lin

Amol Godbole

Kjel Larsen

Josey Diego Sandoval

Nishtha Gupta

Bonan Zheng

Amotz Maimon

Daniel Rohr

Yun Lei

Patrick Nevels

Arjit Biswas

Meyyappan Sundaram

Shanchan Wu

Shaun Ryan James Mcdowell

Techniques for facilitating natural language interactions with visual interactive content are described. During a build time, a system analyzes various websites and applications relating to a particular user goal to understand website and application navigation and information relating to the user goal. The learned information is used to store configuration data. During runtime, when a user request performance of an action, the system engages in a dialog with the user to complete the user's goal. The system uses the stored configuration data to determine actions to be performed at a website or application to complete the user's goal, and determines system responses to present to the user to facilitate completion of the goal. Such system responses may request information from the user, may inform the user of information displayed at the website or application, etc.

Natural language interactions with interactive visual content

Techniques for feedback-based training may include selecting a scoring machine learning model based at least in part on a test metric, and applying the model on an unlabeled dataset to generate, per dataset item of the unlabeled dataset, a prediction and an importance ranking score for the prediction. Techniques for feedback-based training may further include selecting, based on the importance ranking scores, a result of the application of the model on the unlabeled dataset, providing the result and requesting feedback on the result via a graphical user interface, receiving the feedback via the graphical user interface, adding data from the unlabeled dataset into a training dataset when the feedback indicates a verified result, and retraining the model using the training dataset with the data added from the unlabeled dataset to generate a retrained model.

Feedback-based training for anomaly detection

A natural language system may be configured to act as a participant in a conversation between two users. The system may determine when a user expression such as speech, a gesture, or the like is directed from one user to the other. The system may processing input data related the expression (such as audio data, input data, language processing result data, conversation context data, etc.) to determine if the system should interject a response to the user-to-user expression. If so, the system may process the input data to determine a response and output it. The system may track that response as part of the data related to the ongoing conversation.

Dialog management for multiple users

Techniques for anomaly detection are described. An exemplary method includes receiving one or more requests to train an anomaly detection machine learning model using feedback-based training, the request to indicate one or more of a type of analysis to perform, a model selection indication, and a configuration for a training dataset; training the anomaly detection machine learning model according to the one or more requests using the training data; performing feedback-based training on the trained anomaly detection machine learning model; and using the retrained anomaly detection machine learning model.

Anomaly detection using feedback training

A system that is capable of resolving anaphora using timing data received by a local device. A local device outputs audio representing a list of entries. The audio may represent synthesized speech of the list of entries. A user can interrupt the device to select an entry in the list, such as by saying 'that one.' The local device can determine an offset time representing the time between when audio playback began and when the user interrupted. The local device sends the offset time and audio data representing the utterance to a speech processing system which can then use the offset time and stored data to identify which entry on the list was most recently output by the local device when the user interrupted. The system can then resolve anaphora to match that entry and can perform additional processing based on the referred to item.

Techniques for storing and using multi-session context are described. A system may store context data corresponding to a first interaction, where the context data may include action data, entity data and a profile identifier for a user. Later the stored context data may be retrieved during a second interaction corresponding to the entity of the second interaction. The second interaction may take place at a system different than the first interaction. The system may generate a response during the second interaction using the stored context data of the prior interaction.

Multi-session context

A dialog system receives a user request corresponding to a dialog with a user. The dialog system processes the user request to determine multiple service providers capable of responding to the user request. The dialog system selects one service provider based on a request-to-handle score, and selects another service provider based on a satisfaction rating. The dialog system updates the dialog state based on further input provided by the user to determine an output responsive to the user request.

Goal-oriented dialog system

Techniques for anomaly detection are described. An exemplary method includes receiving a request to create a training data set from at least one image, the request to include an indication of the at least one image and at least one indication of an operation to perform on the at least one image to generate a plurality of images from the at least one image; creating a training dataset by extracting one or more chunks from a first at least one image according to the request; and receiving one or more requests to train an anomaly detection machine learning model using the created training dataset; and training an anomaly detection machine learning model according to one or more requests using the created training data.

Generation of training data from a single image

A technology is described for deploying datasets to a production data store. An example of the technology may include receiving a request at a data deployment service to submit a dataset to a production data store which is accessible to services that utilize datasets stored in the production data store. A temporary dataset may be created in a preproduction staging store and a first consistency check may be performed against the temporary dataset to determine whether the temporary dataset complies with dataset specifications. The temporary dataset may be included in a batch of temporary datasets cached on the preproduction staging store when the first consistency check is successful, and a second consistency check may be performed against the batch to determine whether the at least one temporary dataset complies with the dataset specifications. The temporary datasets may be stored to the production data store when the second consistency check is successful.

Validating data for deployment

Growing community of inventors

Oakland, NJ, United States of America

Prakash Krishnan