Amazon Technologies, Inc.

Darren Levi Malek

Toby Ray Latin-Stoermer

Brandon Scott Durham

Jason Christopher Hall

Christopher Lon McGilliard

Benjamin Lee Shepherd

Gabriela Ahern

Jonathan Beech

Ralph Cameron Harvey

Andrew McGoogan

Robert Richard Morse

Haowei Lu

Joshua Fredrick Lutes

Hari Gangadharan

Abhishek Mishra

Kintan Dilipkumar Brahmbhatt

Christopher Woolf Phillips

Vladimir Lazebny

William Jarred Leslie

Xinjie Di

Sarvesh Chandra Regmi

The present disclosure provides computer-implemented systems and processes for augmenting user ratings of items, such as a rating of a song playing on a user device, by analyzing contextual information, such as user and/or device activity data associated with the device. The contextual information may be used to determine an associated intensity of the rating or feedback. The determined intensity levels can be used to weigh the associated rating events and improve the quality of item recommendations that are based on such ratings. Contextual information may indicate whether the user transitioned from another application to rate an item, device status information, and so on. In one embodiment, contextual information can be used to assess how intensely the user feels about a music item and to provide improved music recommendations, such as songs to provide in a playlist or radio station, based on the intensity.

System, method and medium for utilizing current and historical device context information to determine content item recommendations

A technology for propagating themes is provided. In one example, a method may include identifying media having a content feature contained in a presentation of the media. The method may include extracting the content feature of the media as a continuous variable and discretizing the continuous variable into a bucket representing a discrete value. A theme label may be applied to the media and may be propagated to other media with continuous variables discretized into the subset of buckets.

Media theme propagation using decretization of continuous variables

Weighting user feedback events based on device context

A computing device can periodically acquire a current track time for a media content item being played. The device can periodically compare the current track time with a respective start time and a respective end time for each line (or segment) in a plurality of lines of text (or segments of text) associated with the media content item. If the device determines that the current track time is between start and end times for a given line of text, then the device can display the given line of text. A user of the device can scroll through the lines of text associated with the media content item. If the user selects a particular line of text, the device can identify a start time for the selected line of text. The device can then play the media content item at the identified start time of the selected line of text.

Navigating media playback using scrollable text

A technology for synchronizing text with audio includes analyzing the audio to identify voice segments in the audio where a human voice is present and to identify non-voice segments in proximity to the voice segments. Segmented text associated with the audio, having text segments, may be identified and synchronized to the voice segments.

Text synchronization with audio

Techniques are described for automatically selecting musical content for playback based on an initial 'seed' of music selected by a user in a way that seamlessly extends the user's listening experience. The initially selected seed music might be, for example, an album or a playlist. Music that follows the seed music is algorithmically selected to match the music selected by the user.

Techniques for selecting musical content for playback

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Seattle, WA, United States of America

Darren Levi Malek