Using Transcendental Math to Generate Better Random Numbers

A method for creating high-quality random numbers by using complex transcendental math equations to process raw data.

What it covers

This patent describes a way to make random numbers more reliable by using transcendental equations. It takes raw, messy data—like noise from a sensor—and converts it into an algebraic number. This number is then plugged into a transcendental function, which is a type of math that produces highly unpredictable results. By solving these equations, the system generates a sequence of pseudo-random numbers that are cleaner and more secure than those produced by simpler methods.

What it doesn't cover

• —Does not cover standard random number generation methods that rely solely on linear congruential generators.
• —Does not cover hardware-only random number generation that lacks the specific transcendental equation processing step.
• —Does not cover general-purpose data encryption algorithms that do not utilize transcendental functions as the primary transformation mechanism.

The clever bit

It uses the inherent complexity and non-repeating nature of transcendental functions to transform simple, potentially biased input data into highly unpredictable outputs.

Why it matters

Random numbers are the backbone of digital security. If a computer's random number generator is predictable, hackers can guess encryption keys. This patent provides a sophisticated mathematical approach to 'whiten' or clean up raw data, making the resulting numbers more statistically random and harder to crack.

Real-world examples

• 1.Cryptographic key generation
• 2.Secure communication protocols
• 3.Digital simulation software
• 4.Blockchain entropy sources