Dialog Workspace Generation from Policy Documents
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Solution Overview
Problem
Current human-computer dialog systems lack efficiency in generating personalized and relevant responses to user queries, as they often rely on static FAQ pages and do not effectively utilize policy documents to dynamically create dialog flows.
Innovation Solution
The method involves obtaining policy documents and initial questions, identifying relevant documents, generating follow-up questions and candidate answers, creating a dialog tree, and translating this into a dialog workspace within an intelligent dialog system, allowing for dynamic and personalized user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static FAQ pages are used for dialog responses, then system simplicity is maintained, but response personalization and relevance are poor
Solution Approach 1:
The patent transforms static FAQ pages into dynamic dialog workspaces that adapt to user context. The system generates follow-up questions and candidate answers based on policy documents, creating a dynamic conversation flow that personalizes responses while maintaining system structure through automated workspace generation.
Solution Approach 2:
The system automatically generates dialog workspaces from policy documents without requiring manual configuration. The automated generation of follow-up questions, candidate answers, and dialog trees enables the system to self-configure personalized responses, reducing the need for manual system adjustments.
2Measurement precision
If policy documents are analyzed to generate follow-up questions and candidate answers, then response relevance is improved, but processing time increases
Solution Approach 1:
The system performs preliminary analysis of policy documents to pre-generate dialog workspaces, follow-up questions, and candidate answers before user interactions occur. This advance preparation stores processed information in structured formats, enabling rapid retrieval and response during actual dialog sessions without re-analyzing documents in real-time.
Solution Approach 2:
The patent replaces manual analysis of policy documents with automated machine learning models. The ML-based generation of follow-up questions and candidate answers significantly reduces processing time compared to manual methods while maintaining or improving response relevance through systematic analysis of policy content.
3Productivity
If manual creation of dialog flows is performed, then control over dialog structure is maintained, but productivity and scalability are reduced
Solution Approach 1:
The system automatically generates complete dialog workspaces from policy documents without requiring manual configuration. The automated processes include extracting policy rules, generating follow-up questions, creating candidate answers, and structuring dialog trees, all performed self-service style to dramatically increase productivity while maintaining sufficient control through the structured output format.
Solution Approach 2:
The system creates standardized dialog workspace templates that can be replicated and adapted for different policy documents. Once a dialog workspace structure is generated for one policy, similar workspaces for other policies follow the same pattern, enabling scalable deployment across multiple domains without repeating manual configuration efforts.
Data Source
AI summary
Methods, systems, and computer program products for generating dialog system workspaces are provided herein. A computer-implemented method includes obtaining (i) a set of policy documents and (ii) a set of initial questions; identifying at least one of the policy documents in the set of policy documents that is relevant to answering a given one of the initial questions in the set of initial questions; generating, based at least in part on an analysis of said identified policy document, (i) at least one follow-up question to said given initial question and (ii) two or more candidate answers to said at least one follow-up question; generating a dialog tree comprising at least (i) a parent node corresponding to the at least one follow-up question and (ii) child nodes corresponding to the two or more candidate answers; translating the dialog tree into a dialog workspace; and deploying the dialog workspace in an intelligent dialog system.