Abstract: Techniques performed by a data processing system for a machine learning driven teleprompter include displaying a teleprompter transcript associated with a presentation on a display of a computing device associated with a presenter; receiving audio content of the presentation including speech of the presenter in which the presenter is reading the teleprompter transcript; analyzing the audio content of the presentation using a first machine learning model to obtain a real-time textual translation of the audio content, the first machine learning model being a natural language processing model trained to receive audio content including speech and to translate the audio content into a textual representation of the speech; analyzing the real-time textual representation and the teleprompter transcript with a second machine learning model to obtain transcript position information; and automatically scrolling the teleprompter transcript on the display of the computing device based on the transcript position informatio

Abstract: Techniques performed by a data processing system for a machine learning driven teleprompter include displaying a teleprompter transcript associated with a presentation on a display of a computing device associated with a presenter; receiving audio content of the presentation including speech of the presenter in which the presenter is reading the teleprompter transcript; analyzing the audio content of the presentation using a first machine learning model to obtain a real-time textual translation of the audio content, the first machine learning model being a natural language processing model trained to receive audio content including speech and to translate the audio content into a textual representation of the speech; analyzing the real-time textual representation and the teleprompter transcript with a second machine learning model to obtain transcript position information; and automatically scrolling the teleprompter transcript on the display of the computing device based on the transcript position informatio

Abstract: Techniques performed by a data processing system for a machine learning driven teleprompter include displaying a teleprompter transcript associated with a presentation on a display of a computing device associated with a presenter; receiving audio content of the presentation including speech of the presenter in which the presenter is reading the teleprompter transcript; analyzing the audio content of the presentation using a first machine learning model to obtain a real-time textual translation of the audio content, the first machine learning model being a natural language processing model trained to receive audio content including speech and to translate the audio content into a textual representation of the speech; analyzing the real-time textual representation and the teleprompter transcript with a second machine learning model to obtain transcript position information; and automatically scrolling the teleprompter transcript on the display of the computing device based on the transcript position informatio

Abstract: Computer systems, devices, and associated methods of serializing a web page that is of a first format and includes a plug-in including properties of a second format are disclosed herein. In one embodiment, a method includes identifying, in a manifest for the plug-in, properties for inclusion in the first format in a serialized web page. The manifest may also include metadata associated with the properties in the list of properties. The method includes formatting the properties, including associated property values and metadata, in the first format and adding the formatted properties to the serialized web page. The plug-in properties not included in the manifest can be added to the serialized web page in the second format.

Abstract: Computer systems, devices, and associated methods of serializing a web page that is of a first format and includes a plug-in including properties of a second format are disclosed herein. In one embodiment, a method includes identifying, in a manifest for the plug-in, properties for inclusion in the first format in a serialized web page. The manifest may also include metadata associated with the properties in the list of properties. The method includes formatting the properties, including associated property values and metadata, in the first format and adding the formatted properties to the serialized web page. The plug-in properties not included in the manifest can be added to the serialized web page in the second format.

Abstract: A web application intended to be called by a cloud service is debugged locally in a developer's machine. A debugging session is configured to initiate execution of the web application in its intended cloud context by utilizing a reverse proxy mechanism to forward network traffic from a calling cloud service to the developer's machine. In a first embodiment, an integrated development environment (IDE) is able to establish a connection with the reverse proxy mechanism during the debugging session. In a second embodiment, a separate process is used to establish the connection with the reverse proxy mechanism where the separate process is outside of the IDE.

Abstract: A web application intended to be called by a cloud service is debugged locally in a developer's machine. A debugging session is configured to initiate execution of the web application in its intended cloud context by utilizing a reverse proxy mechanism to forward network traffic from a calling cloud service to the developer's machine. In a first embodiment, an integrated development environment (IDE) is able to establish a connection with the reverse proxy mechanism during the debugging session. In a second embodiment, a separate process is used to establish the connection with the reverse proxy mechanism where the separate process is outside of the IDE.