Conversational Agent Unified Knowledge Framework
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Solution Overview
Problem
Existing natural language processing systems face challenges in efficiently identifying objects and understanding language due to the complexity of reasoning from a relational perspective, and previous frameworks for building conversational agents require labor-intensive data collection and maintenance for modular dialog subtasks.
Innovation Solution
A unified framework is introduced for developing conversational agents that uses a centralized knowledge representation to semantically ground dialog subtasks, allowing for end-to-end trainable models and continuous improvement through data-driven evidence, enabling goal-oriented information retrieval tasks over structured knowledge without relying on explicitly encoded rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional natural language processing systems are used to identify objects and understand language, then the system can process basic language tasks, but the system complexity increases and reasoning efficiency decreases due to the complexity of reasoning from a relational perspective
Solution Approach 1:
The patent introduces an intermediary layer (the framework with relationship manager and knowledge representation) between the natural language input and the reasoning engine. This intermediary translates natural language statements into structured relationship representations, simplifying the reasoning process and reducing system complexity while improving understanding efficiency.
Solution Approach 2:
The patent segments the natural language processing task into distinct components: entity identification, relationship extraction, and knowledge representation. This segmentation allows each component to be handled separately and efficiently, reducing overall system complexity while improving productivity.
2Adaptability or versatility
If previous frameworks for building conversational agents are used with modular dialog subtasks, then the system can handle specific dialog functions, but labor-intensive data collection and maintenance are required
Solution Approach 1:
The patent merges multiple dialog subtasks into a unified framework that processes entity identification, relationship extraction, and response generation in an integrated manner. This consolidation reduces the need for separate data collection and maintenance efforts for each subtask, significantly reducing time investment while maintaining versatile dialog functionality.
Solution Approach 2:
The framework enables the conversational agent to automatically learn and adapt from interactions without requiring manual data collection and maintenance. The system self-improves by processing user interactions through the unified framework, reducing labor-intensive activities while maintaining high adaptability.
3Reliability
If explicitly encoded rules are used for goal-oriented information retrieval tasks, then the system can achieve precise control over dialog flow, but the system cannot adapt to new domains without manual rule encoding
Solution Approach 1:
The patent transitions from static explicitly encoded rules to a dynamic knowledge representation system that automatically adapts to different domains. The relationship manager dynamically extracts relationships from natural language statements and the knowledge representation adapts to new domains without manual rule encoding, maintaining reliable control while achieving high adaptability.
Data Source
AI summary
Embodiments relate to a system, program product, and method directed at natural language (NL) and a virtual dialog platform. An NL statement is detected and analyzed to identify one or more entities expressed in the statement. The identified entities are leveraged to parse the statement into keywords. The intent of the received statement is represented as a relationship between two or more of the keywords. A knowledge representation is identified to represent the statement with respect to a formatted module having two or more components and a component relationship structure. Each statement keyword is assigned to a designated module component based on an alignment of the component relationship with the keyword relationship. The statement intent is expressed based on the relationship between the keywords, and a statement response is inferred. The inferred statement is communicated to the virtual dialog platform.