Abstract: Disclosed is method for guiding Large Language Models (LLMs) for task planning and execution, method comprising creating playbook database (302) comprising set of playbooks (SOP); obtaining user query (308) received by Large Language Model (LLM) (306, 208); determining whether or not given task (GT) matches corresponding descriptor of any playbook from amongst SOP; and wherein when it is determined that GT matches given descriptor of given playbook (GP) (314, 400) from amongst SOP, LLM selects one or more ET (212, 316); a software framework associated with the LLM tracks and executes the given set of sub-tasks in the given playbook, using the one or more external tools, in coordination with the LLM; and LLM generates query response including results of the execution of the given set of sub-tasks, and sends the query response to the user device.

Abstract: Systems and technologies for providing an on-demand personalized news program are disclosed. The system and technologies allow an end user to create a news program that best matches the end user's tastes and preferences. To achieve this, one aspect of the disclosure relates to systematic personalization. Systematic personalization involves determining an end user's interests based on an assessment of user behavior as it relates to accessible content. The personalization process may also provide content to users that have been editorially curated.

Abstract: Systems and technologies for providing an on-demand personalized news program are disclosed. The system and technologies allow an end user to create a news program that best matches the end user's tastes and preferences. To achieve this, one aspect of the disclosure relates to systematic personalization. Systematic personalization involves determining an end user's interests based on an assessment of user behavior as it relates to accessible content. The personalization process may also provide content to users that have been editorially curated.

Abstract: Systems and methods of rendering a textual animation are provided. The methods include receiving an audio sample of an audio signal that is being rendered by a media rendering source. The methods also include receiving one or more descriptors for the audio signal based on at least one of a semantic vector, an audio vector, and an emotion vector. Based on the one or more descriptors, a client device may render the textual transcriptions of vocal elements of the audio signal in an animated manner. The client device may further render the textual transcriptions of the vocal elements of the audio signal to be substantially in synchrony to the audio signal being rendered by the media rendering source. In addition, the client device may further receive an identification of a song corresponding to the audio sample, and may render lyrics of the song in an animated manner.

Abstract: Systems and methods of synchronizing media are provided. A client device may be used to capture a sample of a media stream being rendered by a media rendering source. The client device sends the sample to a position identification module to determine a time offset indicating a position in the media stream corresponding to the sampling time of the sample, and optionally a timescale ratio indicating a speed at which the media stream is being rendered by the media rendering source based on a reference speed of the media stream. The client device calculates a real-time offset using a present time, a timestamp of the media sample, the time offset, and optionally the timescale ratio. The client device then renders a second media stream at a position corresponding to the real-time offset to be in synchrony to the media stream being rendered by the media rendering source.

Abstract: Systems and methods of synchronizing media are provided. A client device may be used to capture a sample of a media stream being rendered by a media rendering source. The client device sends the sample to a position identification module to determine a time offset indicating a position in the media stream corresponding to the sampling time of the sample, and optionally a timescale ratio indicating a speed at which the media stream is being rendered by the media rendering source based on a reference speed of the media stream. The client device calculates a real-time offset using a present time, a timestamp of the media sample, the time offset, and optionally the timescale ratio. The client device then renders a second media stream at a position corresponding to the real-time offset to be in synchrony to the media stream being rendered by the media rendering source.

Abstract: Systems and methods of synchronizing media are provided. A client device may be used to capture a sample of a media stream being rendered by a media rendering source. The client device sends the sample to a position identification module to determine a time offset indicating a position in the media stream corresponding to the sampling time of the sample, and optionally a timescale ratio indicating a speed at which the media stream is being rendered by the media rendering source based on a reference speed of the media stream. The client device calculates a real-time offset using a present time, a timestamp of the media sample, the time offset, and optionally the timescale ratio. The client device then renders a second media stream at a position corresponding to the real-time offset to be in synchrony to the media stream being rendered by the media rendering source.

Abstract: Systems and methods of synchronizing media are provided. A client device may be used to capture a sample of a media stream being rendered by a media rendering source. The client device sends the sample to a position identification module to determine a time offset indicating a position in the media stream corresponding to the sampling time of the sample, and optionally a timescale ratio indicating a speed at which the media stream is being rendered by the media rendering source based on a reference speed of the media stream. The client device calculates a real-time offset using a present time, a timestamp of the media sample, the time offset, and optionally the timescale ratio. The client device then renders a second media stream at a position corresponding to the real-time offset to be in synchrony to the media stream being rendered by the media rendering source.

Abstract: Systems and methods of rendering a textual animation are provided. The methods include receiving an audio sample of an audio signal that is being rendered by a media rendering source. The methods also include receiving one or more descriptors for the audio signal based on at least one of a semantic vector, an audio vector, and an emotion vector. Based on the one or more descriptors, a client device may render the textual transcriptions of vocal elements of the audio signal in an animated manner. The client device may further render the textual transcriptions of the vocal elements of the audio signal to be substantially in synchrony to the audio signal being rendered by the media rendering source. In addition, the client device may further receive an identification of a song corresponding to the audio sample, and may render lyrics of the song in an animated manner.