Cognitive Architecture for Dynamic Service Composition
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Solution Overview
Problem
Existing systems lack the ability to dynamically evolve and adapt to novel user requests, as they are typically fixed in functionality and not designed to combine services in innovative ways, limiting their ability to provide unified and variable user experiences.
Innovation Solution
A dynamically evolving cognitive architecture system that allows third-party developers to contribute concept objects, action objects, and other mechanisms, enabling the system to form intents and create plans that combine services in new ways, autonomously learn from user interactions, and adapt at runtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a system is designed with fixed functionality and pre-coded logic, then the system maintains stability and reliability, but it cannot adapt to novel user requests or dynamically evolve its capabilities
Solution Approach 1:
The patent implements a dynamically evolving cognitive architecture where the system transitions from static, pre-coded functionality to dynamic, runtime adaptability. Third-party developers can contribute new concept objects, action objects, and services that are integrated into the cognitive architecture without requiring system redesign. The system continuously evolves its capabilities by incorporating external contributions and learning from user interactions, enabling it to adapt to novel requests while maintaining operational stability through its structured cognitive framework.
Solution Approach 2:
The cognitive architecture serves multiple functions: it processes user requests, integrates third-party services, learns from interactions, and adapts its behavior. The system provides a universal platform that can handle diverse user needs by combining services in innovative ways, rather than requiring separate specialized systems for each function. This multi-functionality is achieved through a unified cognitive framework that can dynamically compose service combinations.
2Adaptability or versatility
If a system combines multiple third-party services to provide unified functionality, then the system offers highly variable user experiences, but it becomes difficult to maintain and update the service combinations
Solution Approach 1:
The patent segments the system into distinct modular components: concept objects, action objects, services, and the cognitive architecture itself. Each third-party service is encapsulated as independent units that can be individually contributed, maintained, and updated. This segmentation allows the system to combine multiple services in flexible ways while maintaining ease of repair, as each component can be independently managed without affecting the entire system.
Solution Approach 2:
The cognitive architecture acts as an intermediary layer between third-party services and user requests. It provides a standardized interface for service integration and composition, managing the complexity of combining multiple services. The cognitive architecture translates diverse service capabilities into unified cognitive models, making it easier to maintain and update service combinations without directly managing each service's internal complexity.
3Adaptability or versatility
If software services are pushed to servers and remain unchanged until the next release, then the system maintains stability, but it cannot provide real-time adaptation or continuous evolution
Solution Approach 1:
The system enables self-service through automated learning mechanisms that continuously improve its functionality without requiring manual updates or releases. The cognitive architecture learns from user interactions and automatically adapts its behavior, while third-party developers can contribute new capabilities that are immediately integrated. This self-service capability allows real-time adaptation while maintaining stability through the structured cognitive framework that guides learning and integration processes.
Solution Approach 2:
The system implements continuous feedback loops where user interactions are analyzed and used to improve service combinations and cognitive models in real-time. The cognitive architecture receives feedback from service execution outcomes and user behavior, automatically adjusting its strategies and compositions. This feedback mechanism enables real-time adaptation while maintaining system stability through controlled, data-driven evolution rather than arbitrary changes.
Data Source
AI summary
A dynamically evolving cognitive architecture system based on training by third-party developers 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.