Amazon Technologies, Inc.

Neil David Cooper

Laura Varnum Finney

Bhavesh Anil Doshi

Prashant Hegde

Kevin Robert McGehee

John Ryan Sherritt

Louis LeRoi LeGrand, Iii

Daniel Bibireata

Chang Yuan

Oleg Rybakov

Geoffrey Heller

Avnish Sikka

Sonjeev Jahagirdar

Junxiong Jia

Christopher John Lish

Saurabh Verma

An extendable augmented reality (AR) system for recognizing objects. The AR system may incorporate plugins to improve the AR system's ability to recognize objects, such as by adding external matchable data or using external recognition algorithms. The plugins may be installed/activated dynamically by a user to expand the system's AR capabilities. The AR system may also incorporate plugins to improve the AR system's post-recognition features such as supplying a user with supplemental content or follow-up functionality. The AR system may filter communications with the plugins to centralize identification processes, to shield plugins, and to ensure plugins are only activated at appropriate times.

Extendable architecture for augmented reality system

Various embodiments describe systems and methods for utilizing a reduced amount of processing capacity for incoming data over time, and, in response to detecting a scene-change-event, notify one or more data processors that a scene-change-event has occurred, and cause incoming data to be processed as new data. In some embodiments, an incoming frame can be compared with a reference frame to determine a difference between the reference frame and the incoming frame. The reference frame may be correlated to a latest scene-change-event. In response to a determination that the difference does not meet one or more difference criteria, a user interface or at least one processor of the computing device can be notified to reduce processing of incoming data over time. In response to a determination that the difference meets the one or more difference criteria, the user interface or the at least one processor can be notified that a scene-change-event has occurred. Incoming data to the computing device is then treated as new and processed as those under an active condition. The current incoming frame can be selected as a new reference frame for detecting next scene-change-event.

Visual and audio recognition for scene change events

The accuracy of a text recognition process can be improved using a set of semantic boosting rules, as may be contained in a sequence or other such arrangement. When text is output from a text recognition process, that text can have alternatives and confidence values for different characters or portions of the string. In order to improve the accuracy, this data can be processed using the organized rules, where rules are applied as long as any preconditions for that rule are satisfied, and each rule has the ability to modify the confidence values or modify one or more of the alternatives. When a result it produced with a minimum confidence level, or all applicable rules have been applied, the result can be provided as a refined text output of the recognition process.

Semantic boosting rules for improving text recognition

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Redmond, WA, United States of America

Neil David Cooper