Amazon Technologies, Inc.

Facebook, Inc.

Menghan Li

Rami Faris Habal

Joseph White

Venkata Snehith Cherukuri

Vinodth Kumar Mohanam

Abhishek Bapna

Eric Hsin-Chun Huang

Shuo Li

Weitong Liu

This disclosure describes, in part, techniques for determining device groupings, or clusters, for multiple voice-enabled devices. The device clusters may be determined based on metadata data for audio signals (or audio data) generated by each of the multiple voice-enabled devices. For example, a remote system may analyze timestamp data for the audio signals received from the devices, and determine that the devices detected the same voice command of a user based on the timestamp data indicating that the audio signals were received within a threshold period of time from each other. Additionally, the remote system may analyze other metadata of the audio data, such as signal-to-noise (SNR) values, and determine that the SNR values are within a threshold value. The remote system may determine device clusters for the voice-enabled devices of a user based on these, and potentially other, types of metadata of the audio signals.

Determining device groups

The present disclosure is directed toward systems and methods for dynamically providing digital content to client devices at different insertion points of a digital video based on predicted total value of inserting the digital content and predicted engagement loss of inserting the digital content. For example, system and methods described herein determine that an insertion point is approaching in an actively playing digital video. In response, systems and methods identify digital content for insertion at the insertion point. In one or more embodiments, the described systems and methods insert the identified digital content by comparing the predicted total value of inserting the digital content at the insertion point of the digital video and a predicted engagement loss associated with inserting the digital content at the insertion point of the digital video.

Dynamically providing digital content to client devices by analyzing insertion points within a digital video

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San Jose, CA, United States of America

Menghan Li