AGI Blueprint Visual Thought, Meta-Cognition & Human-Level Architecture - 2025 by Derek Van Derven - HTML preview

UNITED STATES PATENT APPLICATION

Inventor: Derek Van Derven

Date of Conception: April 20th, 2025

1. Title of the Invention

System and Method for Multimodal Cognitive Architecture Featuring Visual Thought Simulation, Internal Scene Construction, and Meta-Cognitive Feedback

2. Abstract

This invention proposes a novel artificial general intelligence (AGI) system that integrates visual thought simulation and meta-cognitive reflection as core cognitive processes. While some may argue that visualization is just an additional layer or a tool for perception, this system goes beyond that. It makes visual thought central to reasoning itself, enabling the AGI to visualize both abstract and concrete concepts and reason in a human-like manner.

By using a dynamic internal model where visualization of thoughts drives decision-making, the AGI can reflect on its reasoning, adjust based on reflection, and engage with the world in a much deeper and more adaptive way than current models. This innovative approach doesn’t just make the system understand, but enables it to engage in complex philosophical reflection and adapt dynamically to new situations, marking a true leap in AGI capability.

While this invention may enable artificial general intelligence, we refer to it here as a 'multimodal cognitive system' to emphasize its technical, system-level design.

This document details a complete, technically functional multimodal cognitive system architecture capable of interpreting and responding to both abstract and concrete prompts through multi-modal sensory integration, internal simulation, and self-correcting feedback. Every module is defined in terms of software stack, processing model, memory architecture, and sensory translation mechanisms.

Visual, symbolic, and philosophical inputs are processed through clearly defined computational steps tied to real-time simulation and physical execution systems. The architecture is implementable today using available hardware (TPUs, GPUs, microcontrollers) and software (LLMs, Unity, ROS, Prolog/CLIPS).

NOTE:

This document reflects the original conceptual design of the system. Diagram flow is simplified and not strictly chronological. For development, module activation order may vary depending on implementation. In human cognition, thinking is visual—even when the concepts we ponder are abstract. For example, consider a simple phrase like, “purple elephant”. Upon hearing it, your mind instantly visualizes the image of a purple elephant, without needing any additional explanation. This visualization is an essential part of how we process thoughts, ideas, and concepts.

To further illustrate, consider the question: “What is the meaning of life?” At first glance, it seems like an abstract concept. But almost immediately, an image forms in your mind—a subtle, fleeting picture, perhaps of an old man looking up at the sky or a tree, pondering the question. While you didn’t explicitly create this image, it appeared almost instantly as you began to think about the question.

Philosophical concepts often evoke subtle and fleeting imagery. In contrast, more concrete ideas, such as 'red balloon,' generate clearer, more vivid mental images. These images frequently pass unnoticed, often fading almost instantly, without us being fully aware of their presence.

This is how we think—abstract concepts are tied to visual images, whether 2D, 3D, or even 3D virtual journeys. For instance, after saying, "I am going to the store.", one might visualize the store, an aisle, or different stages of the trip. We experience this in dreams, where a 'second mind' narrates, already knowing what will unfold before we do. An AGI might require an implementation of this 'second mind,' and it could also have some use for dreams.

Over time, we learn to recognize that even the most abstract thoughts, including those in dreams or in theoretical ideas, take the form of mental images. This is how the brain makes sense of the world: it visualizes the information it processes, and this visual thinking is critical for true understanding.

Similarly, for an AGI to develop human-like cognition, it must be able to visualize abstract concepts as it processes them. If an AGI cannot see what it’s processing, it cannot fully understand or reason the way humans do. This is the core of the system we propose—the ability for AGI to "see" its thoughts, beliefs, and concepts, enabling it to engage in meaningful reasoning and self-reflection.

3. Field of the Invention

The present invention discloses a framework for multimodal cognitive architecture that processes user input—ranging from simple physical tasks to abstract philosophical questions—through a multi-layered cognitive architecture.

Unlike narrow AI systems limited to single-domain tasks, this invention provides a unified model that integrates multimodal perception, internal simulation, symbolic memory, and meta-cognitive refinement.

4. Background / Field of the Invention

This invention relates to the field of artificial intelligence, and more specifically to multimodal cognitive architecture systems capable of performing a wide range of tasks beyond narrow AI domains. Current AI systems typically lack true comprehension of abstract ideas, contextual memory integration, and self-guided evolution.

They rely on pre-trained models with limited understanding of real-world environments, internal thought representation, or self-aware reasoning.

Current AGI systems, such as DeepMind's World Models and OpenCog, excel at modeling environments and solving problems in controlled settings. However, while some might argue these systems effectively simulate tasks and environments, they fall short of addressing the deeper need for reflective, abstract reasoning.

These systems lack the ability to visualize abstract thoughts—concepts like freedom, justice, or complex emotional states—on a fundamental cognitive level. This invention shifts the paradigm. It doesn’t just simulate the world around the AGI but allows it to internalize and reflect on abstract concepts, creating a deeper, more adaptable level of reasoning.

Unlike existing models, which process information through limited task-based simulations, this invention enables the AGI to engage in philosophical and abstract reflection, paving the way for reasoning that is truly human-like.

The need exists for a multimodal cognitive system that can simulate and process both physical and philosophical tasks, generate internal sensory representations, and self-reflect through a meta-cognitive loop, thereby approaching a more generalized, human-like intelligence framework.

5. Summary of the Invention

The invention describes a multimodal cognitive system framework in which user input—ranging from simple physical commands to complex philosophical queries—is processed through a layered system consisting of:

- **Internal Focus Modules**, including natural language parsing, semantic

association, and visual thought simulation. - **Meta-Cognition Modules**, enabling self-assessment, real-world logic

comparison, and internal/external feedback cycles.

- **Contextual Synthesis**, differentiating between concrete and abstract tasks

using reasoning, memory recall, and oppositional analysis.

- **Internal 3D Scene Builder**, used to simulate tasks visually, emulate senses,

and model interactions.

- **External Action Modules**, including avatar-based outputs and real-world

mappings.

- **Memory Encoding**, for storing visual scenes and linking them to verbal

inputs for future recall.

- **Iterative Learning Loop**, which adjusts internal reasoning, resolves

contradictions, and monitors for emotional or logical bias.

Optional expansion modules may include emotion modeling, goal prioritization, ethical filtering, and long-form dialogue memory.

This architecture enables the multimodal cognitive system to understand and simulate complex concepts, act in virtual or physical environments, and evolve its responses through repeated experience and introspection

This invention integrates visual thought simulation and meta-cognitive reflection as the central cognitive processes in artificial general intelligence. While some might argue that traditional symbolic reasoning or neural-symbolic integration is sufficient, the integration of visual thought simulation takes reasoning far beyond what symbolic representations can achieve.

By visualizing abstract concepts like ‘freedom’ or ‘justice’ and concrete objects like a ‘red apple’, this AGI system internalizes its understanding through visual representation, not just symbolic abstraction.

This enables it to adjust its thinking and decision-making based on dynamic internal feedback loops, reflecting in real-time, and providing human-like flexibility that current systems lack. This visual feedback allows the system to constantly refine its understanding and engage with the world with much deeper reasoning and adaptability.

6. Detailed Description of the Invention

The invention comprises a multimodal cognitive architecture designed to interpret both **concrete instructions** (e.g., physical tasks) and **abstract inquiries** (e.g., philosophical or conceptual questions). It does so by utilizing a multi-layered system of interconnected cognitive modules.

Note: The Meta-Cognition Module is invoked at multiple stages (as seen in both early-stage analysis and during output validation), hence its appearance in two locations in the architecture diagram.

The AGI system described here uses a visualization engine to create internal representations of both concrete objects and abstract concepts. While some may suggest that deep learning or neural networks alone can handle abstract reasoning without the need for explicit visualizations, this system demonstrates that visual thought simulation is an essential building block for human-like reasoning.

For example, when tasked with reasoning about a red apple, the AGI doesn’t simply access symbolic data; it visualizes the apple—considering its shape, color, and texture—and reflects on that visualization. This allows the system to reason about its properties, context, and interactions dynamically, enabling it to adjust in real-time based on its internal feedback loops.

When tasked with more abstract queries, such as ‘What is the meaning of life?’, the system visualizes its experiences and synthesizes the data into a human-like response. This internal visualized feedback gives the AGI the ability to reason with both concrete and abstract concepts, in a way that current neural networks are not yet capable of achieving.

Unified Cognitive System Blueprint

(Combining Abstract and Concrete Task Handling)