Cognitive Architecture Dynamic Evolution via Third-Party Service Composition
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Solution Overview
Problem
Existing systems lack the ability to dynamically evolve and provide unified, highly variable functionality by combining third-party services in a way that meets end-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 allows contributions from third-party developers, forming intents based on user inputs and creating plans using action and concept objects, enabling the system to synthesize responses to novel queries by selecting and combining multiple action objects and concept objects, and continuously learning and adapting through user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a system is designed with fixed functionality and specific use cases, then the system maintains stability and reliability, but it cannot adapt to novel user requests or dynamically evolve
Solution Approach 1:
The patent implements a dynamic cognitive architecture where the system evolves its functionality over time through learning from user interactions. The architecture transitions from static, pre-defined services to a dynamic system that can adapt to novel requests by combining existing services in new ways, thereby resolving the contradiction between adaptability and complexity through controlled evolution rather than complete redesign
Solution Approach 2:
The system segments functionality into independent, reusable service components that can be individually developed, deployed, and combined. This modular architecture allows the system to maintain stability at the component level while achieving adaptability through flexible composition of services, reducing overall system complexity despite enhanced versatility
2Adaptability or versatility
If an enterprise develops custom services for specific use cases, then the service meets precise user needs, but the system lacks unified functionality and requires multiple separate services
Solution Approach 1:
The patent creates a universal cognitive architecture that can handle diverse use cases through a single unified system. The architecture uses general-purpose cognitive processes (understanding, planning, coordination) that work across different domains and services, eliminating the need for separate custom services while maintaining the ability to meet specific user needs through flexible service composition
3Adaptability or versatility
If software services remain unchanged until the next release, then the system maintains stability, but it cannot provide dynamic evolution or respond to emerging user needs
Solution Approach 1:
The system performs preliminary actions by pre-defining service components and cognitive processes that can be flexibly combined. This allows the system to maintain stable, tested components while achieving dynamic evolution through new combinations and configurations, resolving the contradiction between stability and adaptability
Solution Approach 2:
The cognitive architecture incorporates feedback loops where user interactions and system performance data continuously inform service refinement and evolution. This feedback mechanism allows the system to evolve dynamically while maintaining reliability through iterative improvement and validation against actual usage patterns
Data Source
AI summary
A dynamically evolving cognitive architecture system based on contributions from third-party developers is described. A system receives a span of natural language annotated with an object from a first third-party developer. The system forms an intent based on a user input, which includes a natural language span which corresponds to an action object, a first concept object, and/or a second concept object. The action object, the first concept object, and/or the second concept object is provided by a second third-party developer. The annotating object is the action object, the first concept object, or the second concept object. Forming the intent enables executing the action object to transform the first concept object into the second concept object based on the annotated span of natural language, and also enables outputting a value associated with the second concept object associated with a goal of the intent.