Ontogenesis Engine for Dynamic Context Adaptation
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Solution Overview
Problem
Conventional cognitive engines require re-training with new data, have limited context sensitivity, and provide inadequate temporal context, making them inefficient for dynamic data processing and decision-making in computer systems.
Innovation Solution
The system collects information to perform automated ontogenesis operations, determining context, simulating actions, and calculating confidence levels to optimize computer system operations by identifying the best simulation results and applying them to control and performance optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional cognitive engines use pre-defined rules from static models, then they provide quantitative analysis capability, but they require re-training each time new data is input and have limited context sensitivity
Solution Approach 1:
The patent transforms static models into dynamic models that continuously evolve and adapt to new data in real-time. The cognitive engine uses dynamic modeling to automatically update its understanding of system behavior without requiring manual re-training, thereby resolving the contradiction between adaptability and time loss.
Solution Approach 2:
The system implements self-service through automated ontogenesis operations where the cognitive engine autonomously learns from new data, performs self-updating of models, and continuously improves its context sensitivity without external intervention or manual re-training processes.
2Loss of information
If conventional cognitive engines provide quantitative analysis, then they offer analytical capability, but they provide little temporal context
Solution Approach 1:
The patent introduces temporal dimension to the analysis by implementing time-series modeling and historical data integration. This adds the missing temporal context dimension to the quantitative analysis without fundamentally increasing system complexity, as the temporal analysis builds upon existing quantitative frameworks.
3Measurement precision
If the system performs comprehensive simulation of multiple action sets, then it improves decision accuracy through confidence values, but it increases computational complexity
Solution Approach 1:
The system applies partial action by selectively simulating only the most promising action sets based on preliminary evaluation criteria. Instead of exhaustively simulating all possible actions, it focuses computational resources on a subset of high-potential scenarios, maintaining decision confidence accuracy while reducing overall simulation complexity.
Data Source
AI summary
Systems and methods for controlling operations of a Computer System (“CS”). The methods comprise: collecting information about events occurring in CS; performing automated ontogenesis operations using the collected information to determine a context of a given situation associated with CS using stored ontogenetic knowledge, define parameters for different sets of actions that could occur in the context of the given situation, simulate the sets of actions to generate a set of simulation results defining predicted consequences resulting from performance of behaviors by nodes, determine a confidence value for each simulation result that indicates a level of confidence that a successful outcome will result if action(s) associated with the simulation result are performed by CS, and analyze the confidence values to identify a simulation result with the best confidence level; and using the parameters associated with the best simulation result to control and optimize performance of CS.