AI State Orchestration for Relevant Context Routing
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Solution Overview
Problem
Current approaches to integrating AI in state management of operating systems pass all execution data to the AI model, leading to irrelevant context, reduced performance, and decreased efficiency, limiting user control over state transitions and making configuration less user-friendly.
Innovation Solution
An AI-based state manager with an embedded AI model, an orchestrator, and memory, allowing users to configure state transitions using natural language or code, and routing relevant data to the AI model and downstream systems, ensuring efficient and predictable system operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If all execution data is passed to the AI model for state management, then the system can leverage AI capabilities for decision-making, but irrelevant context is introduced leading to reduced performance and speed
Solution Approach 1:
The patent segments the data flow by introducing an orchestrator that separates relevant execution data from irrelevant context before passing data to the AI model. This segmentation allows the system to maintain AI-driven adaptability while improving speed by eliminating unnecessary data processing.
Solution Approach 2:
The orchestrator extracts only the relevant context and execution data needed for state management decisions, removing irrelevant information before presenting it to the AI model. This extraction process maintains the versatility of AI decision-making while significantly improving system operation speed.
2Adaptability or versatility
If all execution data is passed to the AI model, then comprehensive context is available for decision-making, but the AI model receives irrelevant context leading to decreased accuracy
Solution Approach 1:
The data is segmented into relevant and irrelevant components by the orchestrator, which selectively routes only pertinent execution data and context to the AI model. This ensures the model receives comprehensive relevant context while filtering out noise that would degrade output accuracy.
Solution Approach 2:
The orchestrator acts as an intermediary between the execution environment and the AI model, mediating the data flow by filtering and preparing only the relevant context. This intermediary role preserves comprehensive context availability while eliminating irrelevant information that would reduce accuracy.
3Reliability
If deterministic data routing is used throughout the computing stack, then predictability and efficiency are enhanced, but flexibility in AI-based state transitions is reduced
Solution Approach 1:
The orchestrator serves as a flexible intermediary that can adaptively route data based on AI model requirements while maintaining deterministic patterns for standard operations. This allows the system to preserve predictability for routine tasks while enabling flexible AI-based state transitions when needed.
Solution Approach 2:
The data routing mechanism is made dynamic by allowing the orchestrator to adjust routing behavior based on the specific state transition requirements. For predictable scenarios, deterministic routing is maintained, while for complex AI-driven transitions, the system dynamically adapts to provide the necessary flexibility.
4Device complexity
If traditional state management approaches are used, then system control is straightforward, but user control over state transitions and configuration is limited and less user-friendly
Solution Approach 1:
The orchestrator acts as an intermediary that simplifies user interaction with the AI model by handling complex data preparation and model invocation automatically. Users can control state transitions through natural language or high-level commands without needing to understand the underlying complexity, significantly improving ease of operation.
Solution Approach 2:
The patent replaces traditional mechanical programming interfaces with AI-based natural language processing. Instead of requiring users to write complex state management code, the system accepts natural language commands and translates them into appropriate state transitions, making the system much more user-friendly while maintaining straightforward control.
Data Source
AI summary
A computer-implemented method is provided, the method comprising: storing, in memory of an AI-based state manager of an operating system, state transition instructions for transitioning states of the operating system; receiving, by an orchestrator of the AI-based state manager, an execution request; retrieving, by the orchestrator, from the memory, state transition instructions for an AI model of the AI-based state manager based on the execution request; providing, by the orchestrator, to the AI model, an AI model input comprising the retrieved state transition instructions; determining, by the AI model, an AI model output based on the AI model input; and applying, by the orchestrator, based at least in part on the AI model output, a state transition to the operating system by invoking one or more downstream systems.