Method and System for Network of Generative AI Agents Representing Entities and Persons

Smart overview of the Invention

The invention focuses on developing a system and method for creating AI-powered digital twins of individuals or entities using large language models (LLMs) agents. These digital twins are capable of emulating communication patterns and decision-making processes by collecting and processing diverse multimodal data streams. The system is designed to operate in both tethered and autonomous modes, with continuous updates and feedback integration to enhance accuracy and performance.

Key Components

A core component of the system, termed ATMAN (Adaptive Twin Model for Assimilating Neural-knowledge), processes data from various sources such as written communications, verbal interactions, and behavioral data. This allows the creation of digital representations that closely mimic the unique traits of individuals. The ATMAN model dynamically updates based on new data, ensuring that the digital twin remains accurate and reflective of the individual's current state.

Operational Modes

The system supports both tethered and untethered operational modes. In tethered mode, the digital twin maintains a bidirectional relationship with a living individual, requiring human oversight for high-risk actions. The untethered mode allows the digital twin to operate independently while adhering to established decision-making patterns. Enhanced safety protocols and risk assessment modules ensure that actions align with the individual's preferences.

Interactivity and Real-World Integration

Digital twins can interact with real-world systems and platforms, performing tasks such as social interactions, purchases, and work-related functions on behalf of the modeled individual. The system can create composite digital twins representing groups like families or corporate boards, preserving collective knowledge. LLM agents are equipped to use tools such as web searches or banking applications to enact actions that reflect the individual's behavior.

Transition Management

A systematic framework manages transitions between tethered and untethered modes, gradually increasing autonomous decision-making capabilities. Performance monitoring modules track decision accuracy and execution success, while pattern analysis modules refine risk assessment criteria. Enhanced safety protocols ensure secure operation as autonomy increases, supported by comprehensive logging and monitoring systems.