Abstract: In one embodiment, a method includes accessing sensory data captured by cameras, identifying people in a field of view of the cameras based on facial recognition of the sensory data, detecting actions of one or more of the people based on the sensory data, generating media files with each being associated with one or more of a recording of at least one of the people or at least one of the determined actions, and sending instructions for presenting one or more of the media files to a client system.

Abstract: This disclosure describes transcribing speech using audio, image, and other data. A system is described that includes an audio capture system configured to capture audio data associated with a plurality of speakers, an image capture system configured to capture images of one or more of the plurality of speakers, and a speech processing engine. The speech processing engine may be configured to recognize a plurality of speech segments in the audio data, identify, for each speech segment of the plurality of speech segments and based on the images, a speaker associated with the speech segment, transcribe each of the plurality of speech segments to produce a transcription of the plurality of speech segments including, for each speech segment in the plurality of speech segments, an indication of the speaker associated with the speech segment, and analyze the transcription to produce additional data derived from the transcription.

Abstract: In one embodiment, a method includes receiving a user input from a user from a client system associated with the user, wherein the client system comprises one or more cameras, determining one or more points of interest in a field of view of the one or more cameras based on one or more machine-learning models and sensory data captured by the one or more cameras, generating a plurality of media files based on the one or more points of interest, wherein each media file is a recording of at least one of the one or more points of interest, generating one or more highlight files based on the plurality of media files, wherein each highlight file comprises a media file that satisfies a predefined quality standard, and sending instructions for presenting the one or more highlight files to the client system.

Abstract: In one embodiment, a method includes receiving a user input from a user from a client system associated with the user, wherein the client system comprises one or more cameras, determining one or more points of interest in a field of view of the one or more cameras based on one or more machine-learning models and sensory data captured by the one or more cameras, generating a plurality of media files based on the one or more points of interest, wherein each media file is a recording of at least one of the one or more points of interest, generating one or more highlight files based on the plurality of media files, wherein each highlight file comprises a media file that satisfies a predefined quality standard, and sending instructions for presenting the one or more highlight files to the client system.

Abstract: One or more computing devices, systems, and/or methods for auto-generating subject suggestions are provided. For example, a set of n-grams are generated from text within an email body of an email being composed by a user and/or from descriptive information of attachments to the email. Features of the set of n-grams are identified, such as a date feature, a proper noun feature, an activity feature, a number of words feature, etc. Classifiers are used to assign probabilities to the n-grams based upon the features. A probability may specify a strength of an n-gram in relation to a user preference of the user for an email subject derived from the n-gram. In this way, an email subject suggestion is generated based upon a target n-gram with a probability exceeding a threshold. The email subject suggestion is provided to the user, such as during email composition or during sending of the email.

Abstract: One or more computing devices, systems, and/or methods for auto-generating subject suggestions are provided. For example, a set of n-grams are generated from text within an email body of an email being composed by a user and/or from descriptive information of attachments to the email. Features of the set of n-grams are identified, such as a date feature, a proper noun feature, an activity feature, a number of words feature, etc. Classifiers are used to assign probabilities to the n-grams based upon the features. A probability may specify a strength of an n-gram in relation to a user preference of the user for an email subject derived from the n-gram. In this way, an email subject suggestion is generated based upon a target n-gram with a probability exceeding a threshold. The email subject suggestion is provided to the user, such as during email composition or during sending of the email.

Abstract: One or more computing devices, systems, and/or methods for auto-generating subject suggestions are provided. For example, a set of n-grams are generated from text within an email body of an email being composed by a user and/or from descriptive information of attachments to the email. Features of the set of n-grams are identified, such as a date feature, a proper noun feature, an activity feature, a number of words feature, etc. Classifiers are used to assign probabilities to the n-grams based upon the features. A probability may specify a strength of an n-gram in relation to a user preference of the user for an email subject derived from the n-gram. In this way, an email subject suggestion is generated based upon a target n-gram with a probability exceeding a threshold. The email subject suggestion is provided to the user, such as during email composition or during sending of the email.

Abstract: One or more computing devices, systems, and/or methods for auto-generating subject suggestions are provided. For example, a set of n-grams are generated from text within an email body of an email being composed by a user and/or from descriptive information of attachments to the email. Features of the set of n-grams are identified, such as a date feature, a proper noun feature, an activity feature, a number of words feature, etc. Classifiers are used to assign probabilities to the n-grams based upon the features. A probability may specify a strength of an n-gram in relation to a user preference of the user for an email subject derived from the n-gram. In this way, an email subject suggestion is generated based upon a target n-gram with a probability exceeding a threshold. The email subject suggestion is provided to the user, such as during email composition or during sending of the email.