Geometric Graph Random Number Generation With Rule 30 Seeding

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Solution Overview

Problem

Existing random number generators produce predetermined sequences that appear random but are not truly unpredictable, lacking true randomness.

Innovation Solution

The method employs hyperbolic geometric graphs and cellular automaton distributions to generate random numbers, utilizing inverse probability integral transform and Rule 30 as a baseline rule to create a randomized seed key for generating unpredictable number sequences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional mathematical calculation methods are used to generate random numbers, then the generation process is simple and deterministic, but the output sequences are predetermined and lack true unpredictability

Engineering Contradiction:
Improveunpredictability of random number sequenceVSAvoidcomplexity of generation method
Core Design Contradiction:
ReliabilityVSDevice complexity

Solution Approach 1:

The patent replaces traditional mathematical calculation-based random number generation with a hybrid system that incorporates geometric graph sampling and cellular automaton Rule 30 evolution. This substitution transforms the deterministic mechanical calculation process into a system that combines geometric probability sampling with chaotic cellular automaton dynamics, thereby achieving true unpredictability while maintaining manageable complexity through the structured application of these two components

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The invention creates a composite random number generation system by integrating two distinct methodologies: hyperbolic geometric graph sampling (which provides structured randomness through inverse probability integral transform) and cellular automaton Rule 30 (which provides chaotic unpredictability through iterative cell state evolution). This composite approach combines the advantages of both methods to produce random number sequences with enhanced unpredictability properties

Inventive Principle:
Principle #40Composite materials

2Reliability

If geometric graphs and cellular automaton are used to generate random numbers, then true unpredictability is achieved, but the generation process becomes more complex

Engineering Contradiction:
Improvegenuine randomness of number sequenceVSAvoidcomplexity of hyperbolic geometric graph and cellular automaton system
Core Design Contradiction:
ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments the random number generation process into distinct modular stages: (1) hyperbolic geometric graph construction and sampling phase, (2) inverse probability integral transform phase, and (3) cellular automaton Rule 30 evolution phase. Each segment performs a specific function and can be independently implemented and optimized, reducing the perceived overall complexity while maintaining the genuine randomness output

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The system dynamically adjusts parameters including the hyperbolic geometric graph structure, sampling density, and cellular automaton iteration count based on the desired randomness characteristics. By allowing parameter variation, the system can optimize between complexity and randomness quality for different application requirements, making the complexity manageable and adaptive

Inventive Principle:
Principle #35Parameter changes

Data Source

PatentUS20250370717A1Random number generating method using geometric graphs and cellular automaton distribution.
Publication Date: 2025.12.04 TIDWELL LEITH

AI summary

The present invention is a method for generating random numbers using discrete randomized geometric graphs and cellular automaton mechanisms. Number generators often fail to provide unpredictable results which pass tests for statistical randomness. The present invention method increases unpredictability while meeting the tests for cryptographic random number generation.