Atom Tickets, LLC

Amazon Technologies, Inc.

Alan Raddatz

Ameesh Paleja

Kenley Bryan Capps

Alexander Rouse

Michael J Burns

Matthew Bakal

Geoff Shaevitz

Samuel Scott Gigliotti

Bradley Baron

Mitchell Kim

Kenley Capps

Michael Burns

Features are disclosed relating to a collaborative ticketing system that manages various aspects of ticketing for events (e.g., movies, concerts, sporting events, and the like) using knowledge about the contacts, friends, and other social connections of system users. The collaborative ticketing system may generate dynamic ticket offers that are based on the number of friends, invited by users, ultimately obtaining tickets. The collaborative ticketing system can also streamline the procurement of concessions and other event-related items by allowing users to pre-purchase such items (e.g., in connection with ticket offers), and then pick up the items at the event venue with little or no wait. In addition, users can preview event-related content (e.g., movie trailers) for event providers (e.g., movie studios), and answer questions or otherwise provide feedback about the event-related content in return for special offers, rewards, and other considerations.

Collaborative ticketing system

Features are disclosed relating to a collaborative ticketing system that manages various aspects of ticketing for events (e.g., movies, concerts, sporting events, and the like) using knowledge about the contacts of system users. The collaborative ticketing system includes a mobile application that includes a personalized user interface through which users can identify movies or other events to attend at specific venues. The mobile application provides functionality for users to publish information to their contacts about the events they want to attend, and to arrange group outings to selected events.

Collaborative system with personalized user interface for organizing group outings to events

Data driven wheel-based interface for event browsing

Metrics may be gathered at various points of a media distribution system as digital media files are being transmitted to client devices. The metrics may be analyzed to determine whether the media distribution system is performing at an appropriate level. If it is determined that the media distribution system is not performing at an appropriate level, configuration changes may be made to the media distribution system to improve performance. Examples of configuration changes may include changing the number of servers or server instances that are being utilized, changing the quality of the files that are being transmitted, changing the amount of CPU resources being used, adjusting client priorities, and so forth.

Optimizing media distribution using metrics

Growing community of inventors

Seattle, WA, United States of America

Alan Raddatz