Method and a system for adaptive generation of conversational content

A method for adaptive generation of conversational content, the method comprising:

• receiving, by an application server, data associated with a first user from a plurality of sources, wherein the data is provided by an input user, wherein the input user is an entity selected from the set of entities consisting of a living person associated with the first user and the first user themselves;
• creating, by the application server, a personalized profile associated with the first user based on the received data;
• training a conversation simulator based on the received data and personalized profile associated with the first user, and wherein the conversation simulator employs a reinforcement learning model that continuously improves its conversational strategies by receiving rewards based on the at least one query users engagement levels and satisfaction scores, wherein the application server uses federated learning techniques to train the conversation simulator across multiple devices and data sources without compromising privacy of the at least one query users, and wherein the personalized profile includes psychological and behavioral analysis of the first user for allowing the conversation simulator to tailor content based on personality traits, cognitive styles, and individual psychological needs;
• generating, by the application server, conversational content in real-time to enable interaction between at least one query users and the trained conversation simulator based on the personalized profile associated with the first user;
• providing, by the application server, the conversational content to the at least one query users during an interaction with the trained conversation simulator, wherein during the interaction, the at least one query users provide at least one input queries, and the trained conversation simulator generates the conversational content in real-time in response to the at least one input queries provided by the at least one query users;
• analyzing real-time feedback to identify user preferences associated with the at least one query users and engagement levels of the at least one query users, wherein the real-time feedback is selected from the feedback types consisting of biometric data from the at least one query users to further personalize and adapt the conversational content based on the at least one query users physiological and emotional state, said biometric data being selected from the set of biometric data consisting of:
• heart rate, galvanic skin response, stress levels, and facial expression analysis;
• dynamically adapting, by the application server, the conversational content based on the real-time feedback from the at least one query users and from at least one interaction patterns within the interaction, wherein the dynamically adapted conversational content is personalized and provides an engaging interaction experience;
• and
• providing, by the application server, the dynamically adapted conversational content to the at least one query users during the interaction.