Geometric Pattern Recognition for Lower-Cost Multivariable AI Models
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Solution Overview
Problem
Existing information analysis systems, including artificial intelligence and natural language processing, are limited by the rules available to express relationships in data and face high computational costs in calculating those relationships.
Innovation Solution
A method that translates multi-variable data into geometric shapes, defines a boundary, sets the shapes in motion, captures their locations, combines these locations into system signatures, and uses a trained model to recognize patterns in these signatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mathematical relationships are used to express input data sets, then the relationships can be calculated systematically, but the computational costs become excessively high and the system is limited by available rules
Solution Approach 1:
The patent replaces the traditional mathematical calculation system with a geometric-visual system. Instead of computing mathematical relationships between multi-variable data points, the system translates data into geometric shapes and their spatial arrangements, allowing pattern recognition through geometric intuition and visual processing rather than intensive mathematical computation. This substitution reduces computational costs while maintaining pattern recognition capability.
Solution Approach 2:
The patent transforms the parameter representation of data from numerical/mathematical form to geometric form. By changing the parameters from abstract numerical relationships to concrete geometric properties (shapes, positions, spatial arrangements), the system enables pattern recognition through geometric transformations rather than mathematical calculations, thereby reducing computational burden.
2Productivity
If traditional mathematical methods are used for multi-variable data analysis, then systematic calculation is possible, but the complexity of expressing relationships increases
Solution Approach 1:
The patent substitutes complex mathematical relationship expressions with geometric representations. By translating multi-variable data into geometric shapes and their spatial configurations, the system simplifies the expression of relationships between variables. Patterns emerge through geometric arrangements rather than complex mathematical formulas, reducing system complexity while improving recognition efficiency.
Solution Approach 2:
The patent introduces a geometric dimension to data representation. Instead of analyzing multi-variable relationships in abstract numerical space, the system maps data onto geometric space where relationships are expressed through spatial arrangements, shapes, and positions. This dimensional transformation simplifies complex relationships by visualizing them in geometric terms.
Data Source
AI summary
Pattern recognition by receiving a set multi-variable data records, each record including a plurality of variables, representing at least two of the plurality of variables as geometric shapes, defining a boundary enclosing the geometric shapes, configuring at least one geometric shape to move within the boundary, capturing a location of each of the geometric shapes within the boundary as a system state, one or more times, combining one or more system states as a system signature, providing a model trained to recognize patterns in system signatures, and recognizing a pattern in the system signature.