Abstract: A transcript of a conference (e.g., a video conference, an audio conference, or a telephone call with two or more participants) is processed to extract a conference summary. The transcript includes strings that are associated with respective timestamps and respective speaker identifiers. Speaker segments—sequences of consecutive strings attributed to the same speaker—are identified in the transcript. A speaker segment is selected based on its duration in time and one or more strings are selected from with the selected speaker segment for inclusion in the conference summary. A short video conference summary or a short audio conference summary is then generated using timestamps from the transcript associated with strings (e.g., sentences) that have been selected for inclusion in the conference summary. The short video or audio summary may be presented to users to enable efficient storage and transmission of information from the conference within a unified communications system.

Abstract: A transcript of a conference (e.g., a video conference, an audio conference, or a telephone call with two or more participants) is processed to extract a conference summary. The transcript includes strings that are associated with respective timestamps and respective speaker identifiers. Speaker segments—sequences of consecutive strings attributed to the same speaker—are identified in the transcript. A speaker segment is selected based on its duration in time and one or more strings are selected from with the selected speaker segment for inclusion in the conference summary. A short video conference summary or a short audio conference summary is then generated using timestamps from the transcript associated with strings (e.g., sentences) that have been selected for inclusion in the conference summary. The short video or audio summary may be presented to users to enable efficient storage and transmission of information from the conference within a unified communications system.

Abstract: A virtual assistant is configured to automatically identify tasks for a user by processing text from various applications of a unified communications platform (e.g., transcripts of conferences, voicemails, emails, and chat logs) to detect action items and infer associated action item data (e.g., task owner, location, and due date). For example, a virtual assistant system may be configured to utilize machine learning natural language understanding technology to extract action items from various input text to form a to-do list with due dates for the task owner. In some implementations, a two-tier machine learning model topology is used to identify action items in strings. The system may recognize named entities such as nouns, verbs, dates/times, locations of action item sentences. The output information may be displayed on a dashboard, in push notifications, or within other user interface aspects of a personal device, thus providing notification or task planning for personal assistance.

Abstract: Voicemail spam detection is performed based on content of voicemail messages. The content of an incoming voicemail message is compared to a spam template that includes a representation of a spam voicemail. Spam templates may be generated based on spam indications provided by users for voicemail messages they have received. User indications for sufficiently similar voicemail messages may be aggregated by maintaining a vote count for a spam template that reflects how many times a user has indicated a matching voicemail message is spam. A spam template may also include an occurrence count that reflects how many times voicemail messages matching a spam template have been detected in a telephony system. An incoming voicemail message may be compared to spam templates and, responsive to a match of content and/or a corresponding vote count or occurrence count meeting a condition, the voicemail message may be identified as spam.

Abstract: One example system event recommendation includes a processor and at least one memory device. The memory device includes instructions that are executable by the processor to cause the processor to receive a plurality of user features associated with a user, receive a plurality of event features, each of said event features associated with one or more events, and determine a predicted favorite score personalized for each user for each of the one or more events, the predicted favorite score based at least in part on the plurality of user features and the plurality of event features. The memory device also includes instructions that cause the processor to display at least one of the one or more events to the user based at least in part on the personalized predicted favorite score associated with each of the one or more events.

Abstract: Voicemail spam detection is performed based on content of voicemail messages. The content of an incoming voicemail message is compared to a spam template that includes a representation of a spam voicemail. Spam templates may be generated based on spam indications provided by users for voicemail messages they have received. User indications for sufficiently similar voicemail messages may be aggregated by maintaining a vote count for a spam template that reflects how many times a user has indicated a matching voicemail message is spam. A spam template may also include an occurrence count that reflects how many times voicemail messages matching a spam template have been detected in a telephony system. An incoming voicemail message may be compared to spam templates and, responsive to a match of content and/or a corresponding vote count or occurrence count meeting a condition, the voicemail message may be identified as spam.

Abstract: A transcript of a conference (e.g., a video conference, an audio conference, or a telephone call with two or more participants) is processed to extract a conference summary. The transcript includes strings that are associated with respective timestamps and respective speaker identifiers. Speaker segments—sequences of consecutive strings attributed to the same speaker—are identified in the transcript. A speaker segment is selected based on its duration in time and one or more strings are selected from with the selected speaker segment for inclusion in the conference summary. A short video conference summary or a short audio conference summary is then generated using timestamps from the transcript associated with strings (e.g., sentences) that have been selected for inclusion in the conference summary. The short video or audio summary may be presented to users to enable efficient storage and transmission of information from the conference within a unified communications system.

Abstract: A transcript of a conference is processed to extract a conference summary. The transcript includes strings that are associated with respective timestamps and respective speaker identifiers. Speaker segments—sequences of consecutive strings attributed to the same speaker—are identified in the transcript. A speaker segment is selected based on its duration in time and one or more strings are selected from with the selected speaker segment for inclusion in the conference summary. A short video conference summary or a short audio conference summary is then generated using timestamps from the transcript associated with strings that have been selected for inclusion in the conference summary. The short video or audio summary may be presented to users to enable efficient storage and transmission of information from the conference within a unified communications system.

Abstract: A virtual assistant is configured to automatically identify tasks for a user by processing text from various applications of a unified communications platform (e.g., transcripts of conferences, voicemails, emails, and chat logs) to detect action items and infer associated action item data (e.g., task owner, location, and due date). For example, a virtual assistant system may be configured to utilize machine learning natural language understanding technology to extract action items from various input text to form a to-do list with due dates for the task owner. In some implementations, a two-tier machine learning model topology is used to identify action items in strings. The system may recognize named entities such as nouns, verbs, dates/times, locations of action item sentences. The output information may be displayed on a dashboard, in push notifications, or within other user interface aspects of a personal device, thus providing notification or task planning for personal assistance.

Abstract: A transcript of a conference (e.g., a video conference, an audio conference, or a telephone call with two or more participants) is processed to extract a conference summary. A graphical user interface is presented that enables a user to edit highlighting to select portions of a conference recording for inclusion in the conference summary. The user can edit highlighting on strings of a transcript and/or on a playback slide bar and the highlighting edits are propagated to both of these interfaces. Video clips selected for inclusion in a summary based on timestamps of strings in a transcript may be extended forward in time to incorporate a nearest I-frame of video. The short video or audio summary may be presented to users along with a text summary of the conference to enable efficient storage and transmission of information from the conference within a unified communications system.

Abstract: A transcript of a conference (e.g., a video conference, an audio conference, or a telephone call with two or more participants) is processed to extract a conference summary. The transcript includes strings that are associated with respective timestamps and respective speaker identifiers. Speaker segments—sequences of consecutive strings attributed to the same speaker—are identified in the transcript. A speaker segment is selected based on its duration in time and one or more strings are selected from with the selected speaker segment for inclusion in the conference summary. A short video conference summary or a short audio conference summary is then generated using timestamps from the transcript associated with strings (e.g., sentences) that have been selected for inclusion in the conference summary. The short video or audio summary may be presented to users to enable efficient storage and transmission of information from the conference within a unified communications system.

Abstract: A transcript of a conference (e.g., a video conference, an audio conference, or a telephone call with two or more participants) is processed to extract a conference summary. A short video conference summary or a short audio conference summary is then generated using timestamps from the transcript associated with strings (e.g., sentences) in the transcript that have been selected for highlighting as part of the conference summary. The short video or audio summary may be presented to users along with a text summary of the conference to enable efficient storage and transmission of information from the conference within a unified communications system.

Abstract: A transcript of a conference (e.g., a video conference, an audio conference, or a telephone call with two or more participants) is processed to extract a conference summary. A graphical user interface is presented that enables a user to edit highlighting to select portions of a conference recording for inclusion in the conference summary. The user can edit highlighting on strings of a transcript and/or on a playback slide bar and the highlighting edits are propagated to both of these interfaces. Video clips selected for inclusion in a summary based on timestamps of strings in a transcript may be extended forward in time to incorporate a nearest I-frame of video. The short video or audio summary may be presented to users along with a text summary of the conference to enable efficient storage and transmission of information from the conference within a unified communications system.

Abstract: A transcript of a conference (e.g., a video conference, an audio conference, or a telephone call with two or more participants) is processed to extract a conference summary. A short video conference summary or a short audio conference summary is then generated using timestamps from the transcript associated with strings (e.g., sentences) in the transcript that have been selected for highlighting as part of the conference summary. The short video or audio summary may be presented to users along with a text summary of the conference to enable efficient storage and transmission of information from the conference within a unified communications system.

Abstract: A transcript of a conference (e.g., a video conference, an audio conference, or a telephone call with two or more participants) is processed to extract a conference summary. Scores are determined for strings of the transcript that are used to select strings for inclusion in the conference summary. Determining the scores includes determining respective sentence vectors for strings. A sentence vector has elements corresponding to words in the transcript that are proportional to occurrences of the word in the string and inversely proportional to occurrences of the word in the transcript. A short video conference summary or a short audio conference summary is then generated using timestamps from the transcript associated with strings (e.g., sentences) that have been selected for inclusion in the conference summary. The short video or audio summary may be presented to users to enable efficient storage and transmission of conference information within a unified communications system.

Abstract: A transcript of a conference (e.g., a video conference, an audio conference, or a telephone call with two or more participants) is processed to extract a conference summary. The transcript includes strings that are associated with respective timestamps and respective speaker identifiers. Speaker segments—sequences of consecutive strings attributed to the same speaker—are identified in the transcript. A speaker segment is selected based on its duration in time and one or more strings are selected from with the selected speaker segment for inclusion in the conference summary. A short video conference summary or a short audio conference summary is then generated using timestamps from the transcript associated with strings (e.g., sentences) that have been selected for inclusion in the conference summary. The short video or audio summary may be presented to users to enable efficient storage and transmission of information from the conference within a unified communications system.

Abstract: A transcript of a conference (e.g., a video conference, an audio conference, or a telephone call with two or more participants) is processed to extract a conference summary. The transcript includes strings that are associated with respective timestamps and respective speaker identifiers. Speaker segments—sequences of consecutive strings attributed to the same speaker—are identified in the transcript. A speaker segment is selected based on its duration in time and one or more strings are selected from with the selected speaker segment for inclusion in the conference summary. A short video conference summary or a short audio conference summary is then generated using timestamps from the transcript associated with strings (e.g., sentences) that have been selected for inclusion in the conference summary. The short video or audio summary may be presented to users to enable efficient storage and transmission of information from the conference within a unified communications system.

Abstract: Voicemail spam detection is performed based on content of voicemail messages. The content of an incoming voicemail message is compared to a spam template that includes a representation of a spam voicemail. Spam templates may be generated based on spam indications provided by users for voicemail messages they have received. User indications for sufficiently similar voicemail messages may be aggregated by maintaining a vote count for a spam template that reflects how many times a user has indicated a matching voicemail message is spam. A spam template may also include an occurrence count that reflects how many times voicemail messages matching a spam template have been detected in a telephony system. An incoming voicemail message may be compared to spam templates and, responsive to a match of content and/or a corresponding vote count or occurrence count meeting a condition, the voicemail message may be identified as spam.