Inferred Question Generation via User State and Object Characterization
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Solution Overview
Problem
Question answering systems face challenges in generating relevant questions about real-world objects, especially when user data is scarce, and user queries vary based on personal background and physiological or emotional states.
Innovation Solution
A method for generating inferred questions for a question answering system by determining a user's state through context data and extracting characterization information from objects using sensory data, then establishing a relationship between the user state and object to formulate relevant queries using natural language processing and ontology databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional question answering systems are used without user state analysis, then system complexity is reduced, but question relevance and personalization deteriorate
Solution Approach 1:
The system segments the question generation process into distinct modules: user state determination module that analyzes context data, object characterization module that extracts sensory data, relationship determination module that connects user state to objects, and question generation module that formulates queries. This segmentation allows each module to specialize in one aspect, improving overall adaptability while managing complexity through modular design.
Solution Approach 2:
The system performs preliminary analysis of user state and object characterization before question generation. By determining user state from context data and extracting object characteristics in advance, the system prepares personalized parameters that enhance question relevance without adding complexity to the core question answering function.
2Adaptability or versatility
If comprehensive context data and sensory data are collected, then question personalization is improved, but data processing time and computational resources increase
Solution Approach 1:
The system extracts only the essential characteristics needed for personalization from comprehensive context data and sensory data. The object characterization module selectively extracts relevant sensory data and attributes, and the relationship determination module identifies only the critical connections between user state and objects, reducing processing time while maintaining personalization quality.
Solution Approach 2:
The system applies different processing depths to different data elements based on their relevance. High-priority context data and sensory data that directly impact question personalization are processed in detail, while less critical data receives minimal processing. This local quality approach optimizes the balance between personalization and processing efficiency.
Data Source
AI summary
A system and computer implemented method for generating a set of inferred questions for a question answering system is disclosed. The method may include determining, based on context data, a user state. The method may also include extracting characterization information for an object satisfying an attention criteria. The characterization information may be configured to include sensory data. The method may also include determining a relationship between the object and the user state based on the characterization information for the object and the context data of the user state. The method may also include generating, based on the relationship between the object and the user state, a set of inferred questions for a question answering system.