Amazon Technologies, Inc.

Google Inc.

Arushan Rajasekaram

Nikko Strom

Yuzo Watanabe

Madan Mohan Rao Jampani

Li Zhang

Aaron Michael Donsbach

Rohit Prasad

Navid Shiee

Christopher Max Breithaupt

Ariya Rastrow

William Folwell Barton

Stephen Frederick Potter

Mark Steven Baumback

Stan Weidner Salvador

Rajiv Ramachandran

Rajiv Reddy

Furqan Muhammad Khan

The present disclosure provides systems and methods that provide feedback to a user of an image capture device that includes an artificial intelligence system that analyzes incoming image frames to, for example, determine whether to automatically capture and store the incoming frames. An example system can also, in the viewfinder portion of a user interface presented on a display, a graphical intelligence feedback indicator in association with a live video stream. The graphical intelligence feedback indicator can graphically indicate, for each of a plurality of image frames as such image frame is presented within the viewfinder portion of the user interface, a respective measure of one or more attributes of the respective scene depicted by the image frame output by the artificial intelligence system.

Systems and methods for providing feedback for artificial intelligence-based image capture devices

A system that detects the location of a user gaze at a display and in response to the duration of the gaze exceeding a threshold, auto-playing content on the display. The system may also determine gaze event data associating the gaze event with the source of the content the user is gazing at. Other information may also be associated with the gaze event such as user ID, time/duration data, or the like. Various actions can be taken in response to the gaze event such as auto-playing of content, outputting a visual indication of the detected gaze, interpreting detected speech using the gaze event data, data aggregation, etc.

Gaze initiated actions

Features are disclosed for generating predictive personal natural language processing models based on user-specific profile information. The predictive personal models can provide broader coverage of the various terms, named entities, and/or intents of an utterance by the user than a personal model, while providing better accuracy than a general model. Profile information may be obtained from various data sources. Predictions regarding the content or subject of future user utterances may be made from the profile information. Predictive personal models may be generated based on the predictions. Future user utterances may be processed using the predictive personal models.

Generation of predictive natural language processing models

Features are disclosed for maintaining data that can be used to personalize spoken language understanding models, such as speech recognition or natural language understanding models. The personalization data can be used to update the models based on some or all of the data. The data may be obtained from various data sources, such as applications or services used by the user. Personalized spoken language understanding models may be generated or updated based on updates to the personalization data or some other portion of the stored personalization data. Generation of personalized spoken language understanding models may be prioritized such that the generation process accommodates multiple users.

Efficient generation of personalized spoken language understanding models

A system for controlling multiple devices using automatic speech recognition (ASR) even when the devices may not be capable of performing ASR themselves. A device such as a media player, appliance, or the like may be recognized by a network. The configured controls for the device (such as a remote control or other mechanism) are incorporated into a device control registry which catalogs device command controls. Individual ASR grammars are constructed for the devices so speech commands for those devices may be processed by an ASR device. The ASR device may then process those speech commands and convert them into the appropriate inputs for the controlled device. The inputs may then be sent to the controlled device, resulting in ASR control for non-ASR devices.

Speech recognition capability generation and control

Features are disclosed for maintaining data that can be used to personalize spoken language processing, such as automatic speech recognition ('ASR'), natural language understanding ('NLU'), natural language processing (&#x201c;NLP&#x201d;), etc. The data may be obtained from various data sources, such as applications or services used by the user. User-specific data maintained by the data sources can be retrieved and stored for use in generating personal models. Updates to data at the data sources may be reflected by separate data sets in the personalization data, such that other processes can obtain the update data sets separate from other data.

Retrieval and management of spoken language understanding personalization data

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Seattle, WA, United States of America

Arushan Rajasekaram