Analysis Device Q&A Model for Faster Manual Information Retrieval
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Solution Overview
Problem
Existing analysis device manuals and question-answer collections often contain vast amounts of information, leading to inefficiencies in finding specific information due to fragmentation and spelling inconsistencies, requiring users to repeatedly search and use multiple keywords.
Innovation Solution
A method and system utilizing a trained answer inferring model generated by machine learning, which converts manual data and question-answer data into a distributed representation to provide accurate answers to user questions about analysis devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual data and question-answer collection data are used to provide information about analysis devices, then comprehensive information coverage is improved, but information retrieval efficiency deteriorates due to enormous information volume and spelling inconsistencies
Solution Approach 1:
The patent replaces manual mechanical search operations with an automated natural language processing system. The question-answer model automatically processes user queries, extracts key information, and retrieves relevant answers from manual data and question-answer collections, eliminating the need for users to manually search through enormous information volumes and handle spelling inconsistencies.
Solution Approach 2:
The patent introduces a natural language processing system as an intermediary between users and the knowledge base. This intermediary automatically understands user questions, matches them with relevant information in manual data and question-answer collections, and provides accurate answers, thereby resolving spelling inconsistencies and reducing search time while maintaining comprehensive information coverage.
2Speed
If question-answer collection data is used to quickly answer user questions, then response speed is improved, but answer completeness deteriorates due to fragmentary information
Solution Approach 1:
The patent merges question-answer collection data with manual data to create a unified knowledge base. The system combines the speed advantages of pre-prepared question-answer pairs with the comprehensive procedural information in manual data, allowing it to quickly retrieve complete and accurate answers by leveraging both data sources simultaneously.
Solution Approach 2:
The patent creates a multi-functional information retrieval system that can handle both simple factual queries (using question-answer collections) and complex procedural questions (using manual data). The system automatically selects and integrates information from both sources based on the user's question, providing both speed and completeness.
3Measurement precision
If spelling consistency is maintained across manual data and question-answer collections, then search accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent replaces manual data processing and spelling standardization operations with automated natural language processing. The system automatically normalizes text, handles spelling variations, and matches queries with relevant information using semantic understanding, thereby maintaining search accuracy without requiring complex manual data processing procedures.
Data Source
AI summary
An appropriate answer to a question about an analysis device is automatically provided. A system generates an answer to a question about an analysis device. The system includes: a terminal device; and a server device. The terminal device receives an input of the question. The server device receives the question from the terminal device and transmits the answer to the terminal device. The server device includes an inference unit. The inference unit infers the answer from the question by using a trained answer inferring model that can generate a distributed representation of a specific natural language corresponding to manual data including a procedure about the analysis device. The trained answer inferring model is generated by machine learning that uses the manual data and question-answer data, the question-answer data being a combination of questions and answers about the analysis device.