A script production method and a show production system, in which a user has a script seed input that the user wishes to be the starting narrative or starting point for a video. In an example, the script production method includes: receiving the script seed input; generating, using the script seed input and a script writer module: a script which continues the narrative; generating, using the script and a script analysis module: respective segment metadata for the script; and generating, using the script, the respective segment metadata and one or more rendering modules: one or more video segments. An interface screen on a device can be used to add, edit, delete, or approve proposed script segments of the script in real-time after the video segments are generated, in which those approved proposed script segment are also generated into video segments which further continue the narrative.

A script production method and a show production system, in which a user has a script seed input that the user wishes to be the starting narrative or starting point for a video. In an example, the script production method includes: receiving the script seed input; generating, using the script seed input and a script writer module: a script which continues the narrative; generating, using the script and a script analysis module: respective segment metadata for the script; and generating, using the script, the respective segment metadata and one or more rendering modules: one or more video segments. An interface screen on a device can be used to add, edit, delete, or approve proposed script segments of the script in real-time after the video segments are generated, in which those approved proposed script segment are also generated into video segments which further continue the narrative.

One or more computing devices, systems, and/or methods are provided. In an example, a conversation path associated with a revised code segment of a conversational interaction entity is identified by a processor. The conversation path has a predetermined intent. A conversational phrase is generated by the processor for the conversation path. The conversational interaction entity is employed by the processor using the conversation path and the conversational phrase to generate a resultant intent. An issue report is generated by the processor for the conversational interaction entity responsive to the resultant intent not matching the predetermined intent.

Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to effectively and efficiently generate one or more abstractive summaries of one or more multi-section documents. For example, certain embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to generate an abstractive summary of a multi-section document comprising one or more sections, by generating one or more section summaries, section input batches for each selected section, model outputs created by one or more text summarization machine learning models through the performance of a batch processing operation sequence, abstractive summaries, and then storing the abstractive summaries.

In various embodiments, a meeting narration application generates visualizations of recorded meeting data. The meeting narration application generates a first visualization of a set of parameters based on a set of transcript sentences associated with the recorded meeting data. The meeting narration application displays the first visualization and a first expanded content visualization of a first transcript sentence included in the set of transcript sentences within a graphical user interface (GUI). Subsequently, the meeting narration application receives a user event associated with the first visualization via the GUI. The meeting narration application modifies a first parameter selection associated with the set of parameters based on the user event to generate a modified parameter selection. Based on the modified parameter selection, the meeting narration application displays a first compressed content visualization of the first transcript sentence within the GUI.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, relating to synthetic call status updates. In some implementations, a method includes determining, by a task manager module, that a triggering event has occurred to provide a current status of a user call request. The method may then determine, by the task manager module, the current status of the user call request. A representation of the current status of the user call request is generated. Then, the generated representation of the current status of the user call request is provided to the user.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, relating to synthetic call status updates. In some implementations, a method includes determining, by a task manager module, that a triggering event has occurred to provide a current status of a user call request. The method may then determine, by the task manager module, the current status of the user call request. A representation of the current status of the user call request is generated. Then, the generated representation of the current status of the user call request is provided to the user.

Implementations described herein relate to transitioning a computing device between operating modes according to whether the computing device is suitably oriented for received non-audio related gestures. For instance, the user can attach a portable computing device to a docking station of a vehicle and, while in transit, wave their hand near the portable computing device in order to invoke the automated assistant. Such action by the user can be detected by a proximity sensor and/or any other device capable of determining a context of the portable computing device and/or an interest of the user in invoking the automated assistant. In some implementations location, orientation, and/or motion of the portable computing device can be detected and used in combination with an output of the proximity sensor to determine whether to invoke the automated assistant in response to an input gesture from the user.

Implementations described herein relate to transitioning a computing device between operating modes according to whether the computing device is suitably oriented for received non-audio related gestures. For instance, the user can attach a portable computing device to a docking station of a vehicle and, while in transit, wave their hand near the portable computing device in order to invoke the automated assistant. Such action by the user can be detected by a proximity sensor and/or any other device capable of determining a context of the portable computing device and/or an interest of the user in invoking the automated assistant. In some implementations location, orientation, and/or motion of the portable computing device can be detected and used in combination with an output of the proximity sensor to determine whether to invoke the automated assistant in response to an input gesture from the user.

A system for generating wrap-up information is capable of learning how interactions are transformed into contact notes and outcome codes using natural language processing and can generate the contact notes and outcome codes for new incoming interactions by applying prediction models trained on interaction data, contact notes and outcome codes. The system for generating wrap-up information receives interaction data, including interaction audio data, interaction transcripts, associated contact notes and associated outcome codes. The interaction transcripts are generated from the previous interactions between agents and customers. The contact notes and outcome codes are generated by agents during the associated previous interactions. The system processes and uses the interaction data to train prediction models to analyze interaction audio data and interaction transcripts and predict appropriate contact notes and outcome codes for the interaction. Once trained the prediction model(s) can generate appropriate contact notes and outcome codes for new interactions.