Cognitive Architecture System Dynamic Service Composition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems lack the ability to dynamically evolve and provide unified, variable functionality in response to user needs, as they are typically fixed in functionality and not designed to handle novel user requests effectively.
Innovation Solution
A dynamically evolving cognitive architecture system that prompts for additional user input, forming intents and creating plans using multiple action and concept objects, allowing for the synthesis of new services and capabilities through third-party developer contributions, enabling the system to autonomously learn and adapt.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If software services are built with specific purpose and pushed to server unchanged, then service stability is maintained, but system adaptability to novel user requests deteriorates
Solution Approach 1:
The patent implements dynamic service composition by allowing the system to automatically generate and modify service combinations at runtime based on user requests. The service composition engine dynamically assembles services from available components without requiring pre-defined fixed configurations, enabling the system to adapt to novel requests while maintaining stability through structured composition rules.
Solution Approach 2:
The system employs a universal service composition framework that can handle multiple types of user requests through a common architecture. The service composition engine provides multi-functional capability by orchestrating different services based on request patterns, allowing a single system to serve diverse purposes without requiring separate dedicated services for each function.
2Device complexity
If system functionality is fixed in design, then development complexity is reduced, but system versatility to handle varying user needs deteriorates
Solution Approach 1:
The patent segments the system into modular service components that can be independently developed, deployed, and combined. The service composition engine breaks down user requests into atomic service operations, allowing complex functionality to be built from simple, well-defined building blocks. This segmentation reduces development complexity by creating reusable service modules while enabling versatility through flexible composition.
Solution Approach 2:
The system performs preliminary action by pre-defining service interfaces and composition patterns that can be automatically applied to new requests. The service composition engine has pre-established rules and templates for combining services, allowing it to handle novel requests without requiring complex ad-hoc configuration. This preliminary structuring reduces development effort while maintaining system versatility.
3Adaptability or versatility
If services are combined to meet end user needs, then system functionality is enhanced, but system complexity increases
Solution Approach 1:
The service composition engine acts as an intermediary layer between user requests and individual services. It mediates the complexity by translating high-level user needs into coordinated service calls, shielding end users and service providers from the complexity of service interactions. The engine manages service dependencies, error handling, and coordination automatically, reducing perceived system complexity while enhancing functionality.
Solution Approach 2:
The system implements feedback mechanisms where the service composition engine learns from service execution outcomes and user interactions. It uses feedback to optimize service selection and composition strategies, automatically adjusting to improve performance without requiring manual reconfiguration. This feedback-driven approach manages complexity by enabling self-optimization rather than manual tuning.
Data Source
AI summary
A dynamically evolving cognitive architecture system based on prompting for additional user input is described. A system forms an intent based on a user input, and creates a plan based on the intent. The plan includes a first action object that transforms a first concept object associated with the intent into a second concept object and also includes a second action object that transforms the second concept object into a third concept object associated with a goal of the intent. The first action object and the second action object are selected from multiple action objects. The system executes the plan, and outputs a value associated with the third concept object.