N-Dimensional Space Geometric Point Prediction for Dynamic Events
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Solution Overview
Problem
Existing methods struggle to accurately predict the consequences of events or processes with dynamic evolution in space and time, as the relationship between measured data and spatial/temporal evolution is non-linear and non-deterministic, making it challenging for artificial intelligence devices to evaluate and react to such data effectively.
Innovation Solution
A method that defines a set of parameters describing the event or process, represents them in an n-dimensional space, determines a geometrical point as the accumulation of forces generated by the evolution, and displays this point to predict the most probable consequences, using equations to calculate the harmonic center and segment the space into classes based on harmony levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If measured data is used to determine current condition and predict future consequences, then the ability to evaluate and react to data is improved, but the complexity of determining consequences in non-linear systems increases
Solution Approach 1:
The patent introduces an intermediary mathematical model that mediates between measured data and consequence prediction. This model uses parameter transformations and geometric representations to bridge the gap between raw measurements and future predictions, making the non-linear relationship manageable and systematic.
Solution Approach 2:
The patent transforms measured parameters into a standardized representation system with specific mathematical properties. By changing the parameter representation to include geometric and topological characteristics, the system can handle non-linear relationships more effectively while maintaining computational tractability.
2Measurement precision
If the relationship between measured data and spatial evolution is expressed with exact equations, then the precision of prediction is improved, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex system into distinct components: measured parameters, geometric representations, topological features, and consequence models. Each component is handled separately with appropriate mathematical tools, reducing the overall complexity while maintaining prediction precision.
Solution Approach 2:
The patent introduces additional mathematical dimensions to the problem by representing data geometrically and topologically. This dimensional transformation allows complex non-linear relationships to be expressed in a more manageable form, improving precision without proportionally increasing complexity.
3Reliability
If human intelligence skills are simulated in devices, then the ability to extract consequences from data is improved, but the difficulty of implementing non-linear and non-deterministic skills increases
Solution Approach 1:
The patent replaces direct simulation of human cognitive processes with a mathematical and computational system. Instead of trying to replicate human neural processes, the system uses parameter transformations, geometric representations, and algorithmic approaches to achieve similar functional outcomes in a more manufacturable way.
Data Source
AI summary
A method of determining features of events or processes having a dynamic evolution in space and/or time using measurements of parameters that calculate the most probable consequences of the event or process at a certain time includes:defining a set of measurable parameters describing the effects of the event or process, characteristic of the event or process, and measurable at a certain time;defining a n-dimensional space where the parameters describing the event or process are represented by entity points;determining, as a function of the measured values of the characteristic parameters describing the event or process at the certain time, a geometrical point in the n-dimensional space forces accumulate that are generated by the evolution of the event of process in time; anddisplaying or printing the n-dimensional space where the characteristic parameters are shown as entity points and as a geometrical point.