Functional Information System Using N-Dimensional Coordinate Model
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Solution Overview
Problem
Current methods for analyzing complex systems lack an underlying functional model, leading to inconsistencies and inefficiencies in data representation, prediction, and search capabilities, particularly in areas like environmental, economic, and political systems, due to the absence of a structured syntax for organizing qualitative information, which results in vulnerability to misinformation and limited understanding of relationships within these systems.
Innovation Solution
A method for algorithmically determining proximity among elements of underlying functional systems represented in n-dimensional space, using a logical data model to structure data sets, assigning tags to represent functional properties, and executing a functional proximity algorithm to compute correspondence among data entities, thereby facilitating improved search, navigation, and predictive analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data is organized in coordinate format geographically or temporally, then search and navigation capabilities are significantly improved, but other data management domains lack such standardized coordinate models
Solution Approach 1:
The patent creates a universal coordinate model that can be applied across multiple data management domains beyond just geographic and temporal data. The functional information system uses a standardized coordinate system that can represent various types of data (environmental, economic, political, etc.) in a unified framework, enabling consistent search and navigation capabilities across diverse domains without requiring domain-specific customizations
2Device complexity
If existing search systems display results as independent categories, then simplicity is maintained, but the ability to characterize phenomena and predict behavior accurately is inhibited
Solution Approach 1:
The patent transitions from displaying search results as independent categories (flat, one-dimensional organization) to representing data entities as points in an n-dimensional coordinate space. This dimensional transformation allows the system to capture complex relationships and functional dependencies among data elements, enabling accurate characterization of phenomena and reliable prediction of system behavior while maintaining organized and navigable result presentations
3Adaptability or versatility
If machine learning techniques model phenomena without systematic representative space generation, then flexibility is maintained, but the curse of dimensionality occurs with random correlations among dimensions
Solution Approach 1:
The patent performs preliminary organization of data into a systematic n-dimensional coordinate space before applying machine learning techniques. By pre-establishing meaningful relationships among dimensions through the functional information system's coordinate model, the system eliminates the curse of dimensionality problem. This preliminary structuring ensures that correlations among dimensions are based on actual functional relationships rather than random associations, improving measurement precision while preserving modeling flexibility
Data Source
AI summary
The invention includes an algorithmic method for dynamically computing complex relationships among objects of an underlying functional system. The invention includes a method to algorithmically determine a set of functional locations in n-dimensional functional space of a set of elements of a functional system by electronically representing a set of data entities in a database system, the database system comprising a logical data model for structuring data sets from which functional information can be derived, using the logical data model to associate a set of characteristics with a reference point in the functional information system, selecting a functional positioning algorithm, and wherein the functional positioning algorithm executes a set of steps that takes as input a set of characteristics and returns as output a set of locations in n-dimensional functional space.