AR Persona Simulation via Neural Network Interaction Synchronization
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Solution Overview
Problem
Current AI chatbots are limited in simulating human-like interactions, especially in personal settings, as they primarily rely on fact-based responses and struggle to adapt to different users and locations, leading to a lack of realism in their interactions.
Innovation Solution
A computer-implemented augmented reality (AR) essence generation platform that synchronizes interaction and location data to create a virtual persona of an inactive user, using a neural network engine to simulate interactions based on past experiences, allowing for adaptive and realistic interactions with active users in various locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current AI chatbots simulate generic human behavior with fact-based responses, then they can pass the Turing test in certain circumstances, but they are easily identified by users in personal settings and lack realism in interactions beyond standardized information
Solution Approach 1:
The system creates a virtual replica of a specific person by copying their interaction patterns, communication style, and behavioral characteristics. This allows the chatbot to simulate that particular individual rather than a generic agent, enabling users to interact with a personalized virtual version of someone they know, thereby achieving realism in personal settings while maintaining reliable simulation of that person's typical responses
Solution Approach 2:
The system performs preliminary data collection and analysis of a person's communication patterns, interaction history, and behavioral characteristics before the actual interaction begins. This pre-processing of personal data enables the chatbot to be pre-configured with specific individual traits, allowing it to seamlessly simulate that person's unique mannerisms and response styles from the first interaction, resolving the contradiction between generic reliability and personalized realism
2Stability of the object's composition
If chatbots use standardized introductory scripts and fact-based responses, then they can provide consistent customer service information, but they cannot adapt to different users, locations, or personal interaction contexts
Solution Approach 1:
The system dynamically adjusts the chatbot's behavior, communication style, and response patterns based on real-time inputs including user identity, location data, and interaction context. Rather than following fixed scripts, the virtual persona adapts its characteristics dynamically to match the specific situation and individual user, enabling consistent yet personalized interactions that maintain stability in information delivery while achieving adaptability to different contexts
Solution Approach 2:
The system changes multiple parameters of the chatbot's operation including communication tone, response length, topic preferences, and interaction style based on user profile data and location information. By adjusting these parameters dynamically, the chatbot maintains stable core information delivery while adapting its delivery mechanism to suit different users and contexts, resolving the contradiction between standardized service and personalized interaction
Data Source
AI summary
A computer-implemented augmented reality essence generation platform has a processor. An interaction and location synchronization engine synchronizes interaction data between an active user and an inactive user with location data of a geographical location at which the active user and the inactive user participated in a real-world experience during a time period in which the inactive user was active. Moreover, an essence generation engine generates, via the processor, a virtual persona model of the inactive user based upon the interaction data and the location data. Additionally, a neural network engine generates, via the processor, a neural network that simulates, during a time period in which the inactive user is inactive, a virtual persona of the inactive user based on the virtual persona model during a virtual interaction between the active user and a virtual representation of the inactive user.