Conversation Scenario Editor for Natural Dialogue Generation
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Solution Overview
Problem
Conventional automatic conversation systems struggle to establish natural conversations with users, as they require expertise to create language models from scenarios, making it difficult for non-experts to generate adequate responses based on precise speech recognition.
Innovation Solution
A conversation scenario editing device that allows non-experts to create conversation scenarios using objects and morphisms, enabling the generation of language models for automatic conversation systems to provide more adequate responses through precise speech recognition, with features like object citation, state transition relations, and dynamic knowledge generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a conventional database storing pairs of user utterances and replies is used, then the system can output replies based on one-on-one matching, but it cannot establish natural conversations or provide step-by-step story content
Solution Approach 1:
The patent segments the conversation system into distinct components: a scenario definition file that stores structured conversation scenarios with step-by-step content, a speech recognition unit, and a reply generation unit. This segmentation allows the system to separate the narrative flow (scenario) from the matching mechanism, enabling natural conversations while maintaining manageable system complexity.
Solution Approach 2:
The patent implements preliminary action by pre-defining conversation scenarios in a structured format before actual conversation occurs. The scenario definition file contains pre-prepared story content, explanations, and flow control instructions that are executed step-by-step during conversation, allowing the system to provide structured narrative content without requiring complex real-time generation.
2Adaptability or versatility
If a scenario-based conversation system is implemented, then natural conversations can be established, but language models cannot be created without Knowledge Base engineer expertise
Solution Approach 1:
The patent enables self-service by allowing scenario creators to define conversation scenarios using a simple, structured format (scenario definition file) that does not require expert knowledge of language models or knowledge bases. The system automatically processes this definition file to generate the necessary language models and integration code, making scenario creation accessible to non-experts while maintaining conversation naturalness.
3Reliability
If the conversation engine is integrated with Knowledge Base, then the system can execute conversations based on knowledge, but even KB creators cannot comprehend the entire scenario
Solution Approach 1:
The patent extracts the scenario definition from the integrated conversation engine and knowledge base, placing it in a separate, standalone scenario definition file. This extraction makes the scenario structure visible and comprehensible to creators while the system handles the complex integration automatically. The scenario file contains clear, human-readable definitions that creators can understand and modify without needing to comprehend the entire integrated system.
4Productivity
If speech recognition is used to generate input sentences, then the system can process user utterances, but adequate responses cannot be provided without expert-level language models
Solution Approach 1:
The patent introduces an intermediary component that bridges speech recognition and reply generation. The scenario definition file acts as a mediator, containing pre-structured response templates and flow control rules that the reply generation unit uses to construct adequate responses. This intermediary layer eliminates the need for expert-level language models by providing structured guidance for response generation based on recognized input.
Data Source
AI summary
A conversation scenario editor generates/edits a conversation scenario for an automatic conversation system. The system includes a conversation device and a conversation server. The conversation device generates an input sentence through speech recognition of an utterance by a user. The conversation server determines the reply sentence based on the conversation scenario when a reply sentence to the input sentence is requested from the conversation device. The editor includes a language model generator for generating a language model to be used for the speech recognition based on the conversation scenario. According to the editor, a non-expert can generate the language model to provide an adequate conversation based on the speech recognition.