Dialogue Flow Generation from Documents Using Ranked Q&A Pairs
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Solution Overview
Problem
Traditional IT management solutions struggle to intelligently sort significant events from large volumes of data across diverse IT environments, fail to correlate data effectively, and cannot provide real-time insights and predictive analysis to meet user expectations.
Innovation Solution
An AI-driven system that generates and ranks question-answer pairs from source documents using deep-learning models to parse, extract key concepts, generate questions, and rank answers, enabling rapid response to IT issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional IT management solutions are used to process large volumes of data, then data processing capacity is maintained, but the ability to intelligently sort significant events and provide real-time insights deteriorates
Solution Approach 1:
The patent replaces traditional mechanical IT management systems with an AI-based automated system that uses machine learning models to process and analyze IT data. The automated generation of question-answer pairs by AI models substitutes manual or rule-based approaches, enabling intelligent event sorting and real-time insight generation without sacrificing data processing capacity
Solution Approach 2:
The system enables self-service by automatically generating relevant questions and answers from IT documentation without human intervention. The AI models autonomously identify significant events, extract key information, and create structured Q&A pairs that provide real-time insights, allowing the system to serve itself in generating actionable intelligence from raw data
2Stability of the object's composition
If traditional data processing systems are used, then system stability is maintained, but the ability to correlate data across different environments deteriorates
Solution Approach 1:
The patent implements a universal AI-based system that can process and correlate data across multiple IT environments (cloud, on-premises, hybrid) using the same automated question-answer generation framework. The system maintains stability through consistent AI model processing while adapting to different data sources and environments, enabling cross-environment data correlation without requiring environment-specific processing logic
3Measurement precision
If manual methods are used to create question-answer pairs, then answer accuracy is maintained, but the quantity and speed of QA pair generation deteriorates
Solution Approach 1:
The patent replaces manual question-answer pair creation with automated AI models that generate multiple QA pairs rapidly from IT documentation. The system maintains answer accuracy through trained machine learning models while achieving high-speed generation of numerous QA pairs, simultaneously improving both precision and productivity compared to manual methods
Solution Approach 2:
The system performs preliminary actions by pre-processing IT documentation and pre-generating question-answer pairs in advance. The AI models analyze documentation proactively to create a library of relevant QA pairs before they are needed, enabling rapid deployment of accurate answers without sacrificing generation quality or speed
Data Source
AI summary
A computerized method, system and computer program product for building a dialogue flow. One embodiment of the method may comprise receiving an input document, the input document comprising content, and generating, by a question-answer pipeline, a plurality of question-answer pairs from the content of the input document. For each question-answer pair, the method may further comprise feeding the question of the question-answer pair into an intent of a dialogue flow structure, and feeding the answer of the question-answer pair as one response of the intent. The method may further comprise tagging each of the plurality of question-answer pairs with a corresponding document section index, reading, by a conversational agent, the input document to a user, pausing the reading when the conversational agent reaches one of the document section indices in the input document, and in response, reading the question corresponding to the document section indicia to the user.