Quantum driven user specific real time dynamic video rendering system

A method comprising:

• at a computing platform comprising at least one processor, a communication interface, and memory:
• receiving chatbot interaction information indicating interactions of a user with a chatbot;
• identifying context information associated with the user;
• generating, by inputting the chatbot interaction information and the context information into a semantic understanding and content summarization engine, a plurality of responses to the user interactions, wherein the semantic understanding and content summarization engine comprises a natural language processing (NLP) engine, natural language understanding (NLU) engine, regression model, classification model, neural network, support vector machine, random forest model, naïve Bayesian model, principal component analysis model, hierarchical clustering model, and K-means clustering model;
• selecting a first response of the plurality of responses to the user interactions;
• generating a plurality of image frames corresponding to the first response;
• arranging, based on the context information and using an intelligent frame estimation engine, the plurality of image frames in a first sequence;
• rendering a video output using the plurality of image frames and based on the first sequence, wherein the video output comprises a response to the chatbot interaction information;
• and
• sending, to a user device of the user, the video output and one or more commands directing the user device to display the video output, wherein sending the one or more commands directing the user device to display the video output causes the user device to display the video output.

One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:

• receive chatbot interaction information indicating interactions of a user with a chatbot;
• identify context information associated with the user;
• generate, by inputting the chatbot interaction information and the context information into a semantic understanding and content summarization engine, a plurality of responses to the user interactions, wherein the semantic understanding and content summarization engine comprises a natural language processing (NLP) engine, natural language understanding (NLU) engine, regression model, classification model, neural network, support vector machine, random forest model, naïve Bayesian model, principal component analysis model, hierarchical clustering model, and K-means clustering model;
• select a first response of the plurality of responses to the user interactions;
• generate a plurality of image frames corresponding to the first response;
• arrange, based on the context information and using an intelligent frame estimation engine, the plurality of image frames in a first sequence;
• render a video output using the plurality of image frames and based on the first sequence, wherein the video output comprises a response to the chatbot interaction information;
• and
• send, to a user device of the user, the video output and one or more commands directing the user device to display the video output, wherein sending the one or more commands directing the user device to display the video output causes the user device to display the video output.