Abstract: A system includes a processor, an input unit; and a memory storing software code. The processor executes the software code to receive an activation input from a user, and generate, using the input unit, media content including a performance by the user or a performer. The processor further executes the software code to identify, while generating the media content, a product referenced by the user or performer, dynamically obtain up-to-date marketing data for the product, generate metadata for use in providing one or more product-related prompts synchronized with one or references to the product made by one of the user or the performer during the performance, and output the media content accompanied by the metadata. The one or more product-related prompts enable a consumer of the media content to trigger an interaction associated with the product while consuming the media content.

Abstract: A system for performing synergistic object tracking and pattern recognition for event representation includes a computing platform having processing hardware and a system memory storing a software code. The processing hardware is configured to execute the software code to receive event data corresponding to one or more propertie(s) of an object, to generate, using the event data, a location data estimating a location of each of multiple predetermined landmarks of the object, and to predict, using one or both of the event data and the location data, a pattern corresponding to the propertie(s) of the object. The processing hardware is further configured to execute the software code to update, using the predicted pattern, the location data, and to merge the updated location data and the predicted pattern to provide merged data.

Abstract: A system includes a hardware processor and a memory storing software code and one or more machine learning (ML) model(s) trained to transform content. The hardware processor executes the software code to ingest content components each corresponding respectively to a different feature of multiple features included in a content file, receive sensor data describing at least one of an action or an environment of a system user, and identify, using the sensor data, at least one of the content components as content to be transformed. The hardware processor further executes the software code to transform, using the ML model(s), that identified content to provide at least one transformed content component, combine a subset of the ingested content components with the at least one transformed content component to produce a dynamically transformed content, and output the dynamically transformed content in real-time with respect to ingesting the content components.

Abstract: A system includes a hardware processor and a memory storing software code and one or more machine learning (ML) model(s) trained to transform content. The hardware processor executes the software code to ingest content components each corresponding respectively to a different feature of multiple features included in a content file, receive sensor data describing at least one of an action or an environment of a system user, and identify, using the sensor data, at least one of the content components as content to be transformed. The hardware processor further executes the software code to transform, using the ML model(s), that identified content to provide at least one transformed content component, combine a subset of the ingested content components with the at least one transformed content component to produce a dynamically transformed content, and output the dynamically transformed content in real-time with respect to ingesting the content components.

Abstract: According to one implementation, a method of guiding an interaction between a companion module and a user includes identifying a media content for playout by a media player device, transmitting, to a remote server, an interaction schema request identifying the media content and the companion module, and receiving, from the remote server, behavioral manifest data including an instruction for guiding the interaction between the companion module and the user. Such a method also includes obtaining a play head state of the media player device and a playout timecode state of the media content, and identifying a user interaction behavior for the companion module based on the behavioral manifest data, the play head state of the media player device, and the playout timecode state of the media content.

Abstract: A system for producing conversation-driven character animation includes a computing platform having processing hardware and a system memory storing software code, the software code including multiple trained machine learning (ML) models. The processing hardware executes the software code to obtain a conversation understanding feature set describing a present state of a conversation between a digital character and a system user, and to generate an inference, using at least a first trained ML model of the multiple trained ML models and the conversation understanding feature set, the inference including labels describing a predicted next state of a scene within the conversation. The processing hardware further executes the software code to produce, using at least a second trained ML model of the multiple trained ML models and the labels, an animation stream of the digital character participating in the predicted next state of the scene within the conversation.

Abstract: A system for producing conversation-driven character animation includes a computing platform having processing hardware and a system memory storing software code, the software code including multiple trained machine learning (ML) models. The processing hardware executes the software code to obtain a conversation understanding feature set describing a present state of a conversation between a digital character and a system user, and to generate an inference, using at least a first trained ML model of the multiple trained ML models and the conversation understanding feature set, the inference including labels describing a predicted next state of a scene within the conversation. The processing hardware further executes the software code to produce, using at least a second trained ML model of the multiple trained ML models and the labels, an animation stream of the digital character participating in the predicted next state of the scene within the conversation.

Abstract: A system for performing synergistic object tracking and pattern recognition for event representation includes a computing platform having processing hardware and a system memory storing a software code. The processing hardware is configured to execute the software code to receive event data corresponding to one or more propertie(s) of an object, to generate, using the event data, a location data estimating a location of each of multiple predetermined landmarks of the object, and to predict, using one or both of the event data and the location data, a pattern corresponding to the propertie(s) of the object. The processing hardware is further configured to execute the software code to update, using the predicted pattern, the location data, and to merge the updated location data and the predicted pattern to provide merged data.

Abstract: According to one implementation, a method of guiding an interaction between a companion module and a user includes identifying a media content for playout by a media player device, transmitting, to a remote server, an interaction schema request identifying the media content and the companion module, and receiving, from the remote server, behavioral manifest data including an instruction for guiding the interaction between the companion module and the user. Such a method also includes obtaining a play head state of the media player device and a playout timecode state of the media content, and identifying a user interaction behavior for the companion module based on the behavioral manifest data, the play head state of the media player device, and the playout timecode state of the media content.

Abstract: A system for performing synergistic object tracking and pattern recognition for event representation includes a computing platform having processing hardware and a system memory storing a software code. The processing hardware is configured to execute the software code to receive event data corresponding to one or more propertie(s) of an object, to generate, using the event data, a location data estimating a location of each of multiple predetermined landmarks of the object, and to predict, using one or both of the event data and the location data, a pattern corresponding to the propertie(s) of the object. The processing hardware is further configured to execute the software code to update, using the predicted pattern, the location data, and to merge the updated location data and the predicted pattern to provide merged data.

Abstract: According to one implementation, a method of guiding an interaction between a companion module and a user includes identifying a media content for playout by a media player device, transmitting, to a remote server, an interaction schema request identifying the media content and the companion module, and receiving, from the remote server, behavioral manifest data including an instruction for guiding the interaction between the companion module and the user. Such a method also includes obtaining a play head state of the media player device and a playout timecode state of the media content, and identifying a user interaction behavior for the companion module based on the behavioral manifest data, the play head state of the media player device, and the playout timecode state of the media content.

Abstract: Methods, systems and apparatus for inducing and verifying the level of neural entrainment at any target frequency, through a combination of biofeedback mechanisms, data analysis, and modulation of synergistic combinations of adaptable stimuli.

Abstract: A system for performing machine language (ML) model based condition and property detection includes a computing platform having processing hardware and a system memory storing a software code that includes a trained ML model. The processing hardware is configured to execute the software code to receive a dataset, and perform an analysis of the dataset, using a first stage of the trained ML model, to detect a presence of a predetermined data attribute. The processing hardware is further configured to execute the software code to predict, using a second stage of the trained ML model when the analysis of the dataset detects the presence of the predetermined data attribute, a probability that the predetermined data attribute is indicative of a condition or a property.

Abstract: A system for performing synergistic object tracking and pattern recognition for event representation includes a computing platform having processing hardware and a system memory storing a software code. The processing hardware is configured to execute the software code to receive event data corresponding to one or more propertie(s) of an object, to generate, using the event data, a location data estimating a location of each of multiple predetermined landmarks of the object, and to predict, using one or both of the event data and the location data, a pattern corresponding to the propertie(s) of the object. The processing hardware is further configured to execute the software code to update, using the predicted pattern, the location data, and to merge the updated location data and the predicted pattern to provide merged data.

Abstract: A system for synchronizing media content playout includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor executes the software code to receive a first state message from a first media player playing a first media content and a second state message from a second media player playing a second media content, the first media content and the second media content being the same media content. The software code further determines a coordination state for playout of the first media content and the second media content based on one or more of the first and second state messages, and transmits a first coordination message including the coordination state to the first media player and a second coordination message including the coordination state to the second media player to synchronize playout of the first media content and the second media content.

Abstract: According to one implementation, a method of guiding an interaction between a companion module and a user includes identifying a media content for playout by a media player device, transmitting, to a remote server, an interaction schema request identifying the media content and the companion module, and receiving, from the remote server, behavioral manifest data including an instruction for guiding the interaction between the companion module and the user. Such a method also includes obtaining a play head state of the media player device and a playout timecode state of the media content, and identifying a user interaction behavior for the companion module based on the behavioral manifest data, the play head state of the media player device, and the playout timecode state of the media content.

Abstract: A system for synchronizing media content playout includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor executes the software code to receive a first state message from a first media player playing a first media content and a second state message from a second media player playing a second media content, the first media content and the second media content being the same media content. The software code further determines a coordination state for playout of the first media content and the second media content based on one or more of the first and second state messages, and transmits a first coordination message including the coordination state to the first media player and a second coordination message including the coordination state to the second media player to synchronize playout of the first media content and the second media content.

Abstract: A system for synchronizing media content playout includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor executes the software code to receive a first state message from a first media player playing a first media content and a second state message from a second media player playing a second media content, the first media content and the second media content being the same media content. The software code further determines a coordination state for playout of the first media content and the second media content based on one or more of the first and second state messages, and transmits a first coordination message including the coordination state to the first media player and a second coordination message including the coordination state to the second media player to synchronize playout of the first media content and the second media content.

Abstract: A system for synchronizing media content playout includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor executes the software code to receive a first state message from a first media player playing a first media content and a second state message from a second media player playing a second media content, the first media content and the second media content being the same media content. The software code further determines a coordination state for playout of the first media content and the second media content based on one or more of the first and second state messages, and transmits a first coordination message including the coordination state to the first media player and a second coordination message including the coordination state to the second media player to synchronize playout of the first media content and the second media content.