Modular Dialogue System Architecture for Non-Expert Expansion
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Solution Overview
Problem
Current dialogue systems require technical expertise and programming knowledge to expand their capabilities, making it difficult for non-engineer administrators to enhance and maintain them, and they often burden annotators with complex data annotation tasks.
Innovation Solution
A dialogue system architecture that allows non-experts to expand capabilities without programming or AI knowledge, using a modular design with a knowledge system, user interface, and learning model that enables easy annotation and data decoupling, allowing for scalable data collection and minimal annotator burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current dialogue system architecture is used, then system functionality is maintained, but technical expertise and programming knowledge are required to expand capabilities
Solution Approach 1:
The patent introduces a domain expert as an intermediary between the dialogue system and the expansion process. The domain expert provides knowledge through simple annotations rather than programming, mediating the capability expansion without requiring technical expertise. This resolves the contradiction by enabling versatility improvement while maintaining ease of operation through the expert's involvement.
Solution Approach 2:
The system enables self-service capability expansion by allowing domain experts to annotate data and extend functionality without external technical assistance. The modular architecture and pre-built tools allow experts to independently expand system capabilities through annotation, eliminating the need for programming knowledge while maintaining adaptability.
2Reliability
If complex data annotation tasks are used, then data consistency is maintained, but annotator burden increases
Solution Approach 1:
The patent segments the annotation process into structured, modular tasks that domain experts can perform independently. By breaking down complex annotation into discrete, manageable units with clear guidelines, the system maintains data consistency through standardized segments while reducing overall annotator burden through task simplicity and modularity.
Solution Approach 2:
The system changes the parameters of the annotation task by providing pre-defined schemas, validation rules, and automated checking mechanisms. This transforms complex open-ended annotation into constrained parameter-based tasks that are easier to perform while maintaining consistency through systematic parameter validation and standardization.
3Productivity
If modular design with decoupled data collection is implemented, then scalability is improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the dialogue system into distinct modular components: data collection module, annotation module, training module, and deployment module. Each module operates independently with well-defined interfaces, enabling scalability through selective enhancement of individual components while managing overall system complexity through clear separation of concerns.
Solution Approach 2:
The modular architecture implements universality by designing components that can serve multiple functions and be reused across different domains and applications. The decoupled data collection and annotation frameworks can be universally applied to various dialogue systems, improving scalability while the standardized interfaces manage complexity through reusability rather than proliferation of specialized components.
Data Source
AI summary
An automated natural dialogue system provides a combination of structure and flexibility to allow for ease of annotation of dialogues as well as learning and expanding the capabilities of the dialogue system based on natural language interactions.