Ontogenesis Intelligence Engine for Adaptive Context Sensitivity
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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 and adaptive tasks in computer systems.
Innovation Solution
The system collects information to perform automated ontogenesis operations, determining context and simulating actions to predict consequences, allowing for adaptive control of computer operations and behavior models, including modifying cyber behavior models and mission plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional cognitive engines use pre-defined rules from static models, then quantitative analysis of data is provided, but the models require re-training each time new data is input and context sensitivity is restricted
Solution Approach 1:
The patent transforms static models into dynamic models that automatically adapt to new data without re-training. The system uses dynamic model generation that evolves based on incoming data streams, allowing the cognitive engine to maintain quantitative analysis capabilities while gaining context sensitivity through continuous adaptation to changing conditions.
Solution Approach 2:
The system performs preliminary ontogenesis operations to generate dynamic models before data analysis is needed. By pre-establishing the framework for model adaptation and context recognition, the system enables both precise quantitative analysis and high context sensitivity from the outset, eliminating the need for re-training when new data arrives.
2Measurement precision
If conventional cognitive engines use static models, then quantitative analysis is achieved, but temporal context is provided inadequately
Solution Approach 1:
The patent implements dynamic models that inherently capture temporal relationships in data. By using models that evolve over time and automatically adapt to new information, the system maintains quantitative analysis precision while fully preserving temporal context, as the models continuously update their understanding of time-dependent patterns without requiring re-training.
3Measurement precision
If models are re-trained with new data in conventional systems, then analysis accuracy is maintained, but system efficiency decreases and human involvement increases
Solution Approach 1:
The patent implements self-service through automated ontogenesis operations where the system automatically generates and updates dynamic models using new data without human intervention. This self-updating mechanism maintains analysis accuracy equivalent to re-training while eliminating the time loss and human involvement associated with conventional re-training processes, thereby improving system efficiency.
4Extent of automation
If conventional cognitive engines use pre-defined rules, then routine tasks can be automated, but adaptability to changing situations is limited
Solution Approach 1:
The patent replaces static pre-defined rules with dynamic models that automatically adapt to changing situations. The dynamic models maintain the automation of routine tasks while gaining the ability to adapt to new conditions, as they continuously evolve based on incoming data without requiring manual rule updates or human intervention.
Solution Approach 2:
The patent creates universal dynamic models that can handle both routine automated tasks and adaptive response to changing situations within a single framework. The dynamic models serve multiple functions: they automate routine operations while simultaneously adapting to novel conditions, eliminating the need for separate systems for automation and adaptability.
Data Source
AI summary
Systems and methods for controlling operations of a computer system. The methods comprises: collecting, by at least one computing device, information about events occurring in the computer system; performing automated ontogenesis operations by the at least one computing device using the collected information to determine a context of a given situation associated with the computer system, define parameters for a plurality of different sets of actions that could occur in the context of the given situation, and simulate the sets of actions to generate predicted consequences resulting from the performance of certain behaviors by nodes of the computer system; and using the parameters of at least one of the predicted consequences to control operations of the computer system.