Amazon Technologies, Inc.

James Robert Blair

Steven Todd Rabuchin

Mark Chien

Farah Lynn Houston

Luis Felipe Cabrera

Rahul Sharma

Scott Kerns Windsor

Siddharth Vivek Joshi

Nicole A Deflaux

Canku Alp Calargun

Charles H Bell

Vikram Madan

Greg Roberts

Jang Han Goo

Vishal Bethur Sathyamurthy

Luis Valencia

Paul Damian Tidwell

Mohamed Reza Hussein

John Benjamin Dalin

David Aronchick

Daniel Zi Wang

Vicky Ge

Hyma Srinivasa Murthy

Eric Sungmin Kim

Dylan Robert House

This disclosure describes a tabletop game assistant system configured to ingest and guide tangible games (such as board games, card games, etc.) using natural language interaction and image capture/visual display components. The system can include features enabling a game developer to 'teach' the system the rules of a game using natural language, such as written instructions, to reduce or eliminate the need for writing dedicated code. The system may process images of a game board and/or tokens such as game pieces and/or cards to further generate game data in the form of a logical game model. The system can use the game data to guide human players of the game and, in some cases, participate as a player itself. The system may further be configured to observe a game and detect invalid actions, answer questions regarding the rules, and suggest moves. The system may provide additional utilities such as generating a random output (e.g., rolling virtual dice) and learning to recognize new game pieces.

Natural language configuration and operation for tangible games

Exemplary embodiments relate to using a voice user interface for creating a poll to send to users. A poll requestor can create a poll using voice instructions and specify response options and setup information. After creating the poll, the system broadcasts it to users who can respond to the poll. The poll may be broadcasted while a user is receiving content. The users can respond using a voice user interface. The system compiles the responses and provides them to the poll requestor.

Polling with a natural language interface

Techniques for generating and utilizing machine learning based adaptive instructions for annotation are described. An annotation service can use models to identify edge case data elements predicted to elicit differing annotations from annotators, 'bad' data elements predicted to be difficult to annotate, and/or 'good' data elements predicted to elicit matching or otherwise high-quality annotations from annotators. These sets of data elements can be automatically incorporated into annotation job instructions provided to annotators, resulting in improved overall annotation results via having efficiently and effectively &#x201c;trained&#x201d; the annotators how to perform the annotation task.

Machine learning based adaptive instructions for annotation

Techniques are described for facilitating performance of tasks involving human review of media data (e.g., images, audio clips, video clips, etc.), such as to identify one or more targets of interest. The media data review may be performed for various reasons, such as to assist in locating one or more target objects in a large geographical area (e.g., one or more missing people or vehicles) based on human review of large quantities of images that each correspond to a small subset of the geographical area. When one or more of the reviewed media data pieces are identified as being relevant, information about those identified media data pieces may be used in various ways. Furthermore, in at least some situations, the media data review may be part of a charitable effort that uses human volunteers to review the media data without monetary payment or other compensation.

Managing performance of human review of media data

Tracking waiting lists associated with merchants and presenting deals to users based on adding the users to waiting lists is described. A service provider may enable merchants to track waiting lists associated with their own services and services of other merchants. Users may be automatically added to waiting lists of merchants based at least in part on a service provider determining that a user is at a geographic location of a merchant. Based at least in part on adding a user to a waiting list for a particular merchant, the service provider may access and select deals from one or more alternative merchants. The one or more alternative merchants may not have waiting lists or may have waiting lists with fewer groups than the waiting list for the particular merchant. The service provider may offer the deals to the user via a user device.

Computerized waiting list tracking system

Growing community of inventors

Seattle, WA, United States of America

James Robert Blair