AI-Driven Theoretical Testing (+AIdtt) System
OwnerErik Micael Bauminventor
Life of this patent
Estimating the term…
Abstract
The AI-Driven Theoretical Testing (+AIdtt) System is a comprehensive framework for automating theory-building. The system integrates AI-driven tools and a Model Rendering Algorithm to enable efficient, rigorous, and accessible theory building. Key components include FRaw for input validation, a Model Rendering Algorithm for generating initial models, and refinement tools such as Alfct, Alfemp, and Alfeth for iterative refinement. The system ensures that theoretical models are logically coherent, empirically grounded, and ethically sound, while significantly reducing the time and effort required for theory building.
Claims14
A system for AI-driven theoretical testing, comprising:
- a. a FRaw module configured to validate inputs using natural language processing (NLP) and anomaly detection algorithms;
- b. a fragmentation module configured to break down validated inputs into basic theoretical constituents;
- c. a Model Rendering Algorithm configured to generate initial models based on the fragmented constituents;
- d. a plurality of refinement tools configured to iteratively refine the models, wherein the refinement tools include AIfit, AIfct, AIfemp, AIfcr, AIfsim, AIfeth, and AIfmeta;
- e. a multi-modal interface configured to enable user interaction and recursive refinement through one or more of natural language, voice commands, visual inputs, and other interaction modalities, including but not limited to gestures, haptic feedback, and augmented reality (VR) interfaces. wherein the interface interprets user queries, provides real-time feedback, and triggers iterative refinement based on user input.
- f. an iterative feedback loop between the refinement tools and the Model Rendering Algorithm, wherein the models are recursively refined based on feedback from the refinement tools.
Dependencies are inferred from the claim text.