Amazon Technologies, Inc.

Pawel Matykiewicz

Amber Roy Chowdhury

Hassan Haider Malik

Sanjay Kumar

Anand Vishwanath Suvarnkar

Sihui Zhang

Uday S Sandhar

Ming Ma

Systems and methods for generating a series of connected secure, unique glyph carriers configured to interface with customizable finite state machines are described herein. In some embodiments, the present disclosure relates to systems and methods for scanning the secure, unique glyph carriers disclosed herein. The systems and methods may include a database in communication with a finite state machine. In various configurations, the systems and methods may be used to detect a forgery of the secure, unique glyphs described herein.

Secure unique glyph carrier configured to interface with customizable finite state machine

Devices and techniques are generally described for machine learning personalization as a service for speech processing applications. In various examples, a first request for machine learning prediction for a first speech processing skill. First skill data schema data may be received that describes content of the first speech processing skill. A first machine learning model for the first speech processing skill may be determined. A first feature definition describing a first aspect of the content may be determined. A second feature definition describing user profile data may be determined. A prediction request may be received from the first speech processing skill. First feature data may be generated according to the first feature definition and second feature data may be generated according to the second feature definition based at least in part on the prediction request. The first machine learning model may generate prediction data based at least in part on the prediction request, the first feature data, and the second feature data.

Personalization for speech processing applications

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Bellevue, WA, United States of America

Pawel Matykiewicz