Cognitive Architecture for Dynamic Service Evolution
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Solution Overview
Problem
Existing software systems are inflexible and fixed in functionality, unable to dynamically adapt to novel user requests or combine services in innovative ways, limiting their ability to provide unified and highly variable user experiences.
Innovation Solution
A dynamically evolving cognitive architecture system that forms intents based on user input, creates plans using multiple action and concept objects, and autonomously learns and evolves by interacting with third-party developers and users, allowing for the creation of new services and functionalities through dynamic combinations of existing services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If software services are built with a specific purpose and pushed to servers unchanged until the next release, then the service maintains stability and reliability, but the system lacks adaptability and cannot dynamically respond to novel user requests
Solution Approach 1:
The patent implements a cognitive architecture that enables services to dynamically evolve at runtime through learning from user interactions and developer contributions. The system transitions from static, pre-deployed code to a dynamic state where services can be modified, combined, and adapted without traditional release cycles, resolving the contradiction between stability and adaptability
Solution Approach 2:
The system enables services to self-evolve by automatically learning from user feedback and interaction patterns. The cognitive architecture allows services to autonomously adapt their behavior and functionality based on observed usage, reducing the need for manual updates while maintaining reliable operation through controlled learning mechanisms
2Adaptability or versatility
If the system combines multiple third-party services to meet user needs, then the functionality and versatility increase, but the system complexity increases
Solution Approach 1:
The patent introduces a cognitive architecture as an intermediary layer between users and multiple third-party services. This mediator automatically plans, coordinates, and manages the combination of services, abstracting away the complexity from users while enabling sophisticated multi-service functionality through intelligent service composition and orchestration
3Productivity
If the system allows autonomous learning and evolution through interactions with developers and users, then the productivity and innovation increase, but the difficulty of controlling and measuring the system increases
Solution Approach 1:
The patent implements comprehensive feedback mechanisms that track user interactions, developer contributions, and system evolution metrics. The cognitive architecture monitors learning processes and evolution trajectories, providing measurable data that enables control and assessment of productivity gains while managing the complexity of autonomous adaptation
Data Source
AI summary
Dynamically evolving cognitive architecture system planning 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.