Supervisory Knowledge Node Architecture for Adaptive Actor Analysis
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Solution Overview
Problem
Current computing systems face challenges in dynamically adapting to changing circumstances and processing large, seemingly infinite datasets, particularly in categorizing and scaling performance across a vast actor population, due to limitations in translating qualitative behavior criteria into numerical values and inability to change attributes or criteria dynamically.
Innovation Solution
A system comprising interconnected compute servers with a supervisory hardware node and knowledge hardware nodes, utilizing a common taxonomy for data normalization and fuzzy logic-based relevancy processing to categorize and scale performance across an infinite actor population, enabling dynamic assessment and alteration of system parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional computing systems use fixed numerical thresholds for qualitative criteria, then analysis can be performed, but the system cannot adapt dynamically to changing circumstances or contexts
Solution Approach 1:
The patent implements dynamic thresholds that automatically adjust based on contextual parameters such as time of day, user behavior patterns, and environmental conditions. Instead of fixed numerical values, the system uses adaptive algorithms that modify criteria thresholds in real-time to match changing circumstances, enabling the system to adapt dynamically without requiring manual reconfiguration
Solution Approach 2:
The system changes the parameter representation from fixed numerical thresholds to context-dependent variable thresholds. By introducing contextual parameters (time, location, user profile, behavior history) and using these to dynamically adjust the numerical criteria, the system transforms static analysis parameters into adaptive ones that respond to changing conditions
2Quantity of substance
If the system processes large amounts of data from extensive actor populations, then comprehensive analysis is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent divides the large actor population into smaller segments or clusters based on shared characteristics, behaviors, or contextual attributes. By processing segments independently and in parallel, the system reduces the computational burden on any single processing unit and enables faster overall analysis while maintaining comprehensive coverage of the entire population
Solution Approach 2:
The system implements incremental processing that analyzes data in chunks or streams rather than requiring complete data sets before analysis. This allows the system to produce preliminary results from partial data and continuously refine them as more data becomes available, reducing waiting time for initial insights
3Measurement precision
If the system uses context-specific qualitative criteria, then analysis accuracy improves, but the difficulty of defining and translating these criteria into numerical values increases
Solution Approach 1:
The patent implements automated threshold generation algorithms that learn optimal numerical criteria directly from historical data and contextual patterns. Instead of requiring manual translation of qualitative criteria by domain experts, the system automatically derives appropriate numerical thresholds by analyzing data distributions, identifying patterns, and adapting to contextual variations, thereby eliminating the difficult translation step while maintaining high accuracy
Data Source
AI summary
The present design is directed to a series of interconnected compute servers including a supervisory hardware node and a plurality of knowledge hardware nodes, wherein the series of interconnected compute servers are configured to categorize and scale performance of multiple disjoint algorithms across a seemingly infinite actor population, wherein the series of interconnected compute servers are configured to normalize data using a common taxonomy, distribute normalized data relatively evenly across the plurality of knowledge hardware nodes, supervise algorithm execution across knowledge hardware nodes, and collate and present results of analysis of the seemingly infinite actor population.