Conversational Aggregate Question Ranking for Data Exploration
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Solution Overview
Problem
Existing data retrieval systems struggle to effectively explore interesting data patterns in structured tables for non-technical users, as they lack the ability to automatically generate relevant questions based on user intent and adapt to user feedback.
Innovation Solution
An AI-enabled system that recommends aggregate questions in a conversational data exploration by selecting interesting features and operators, calculating importance scores, generating questions based on user persona and feedback, and dynamically adapting to user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional data retrieval systems are used, then data can be accessed, but non-technical users cannot effectively explore interesting data patterns or generate relevant questions
Solution Approach 1:
The patent introduces an intermediary system that includes a question generator, ranking module, and feedback mechanism. This intermediary translates complex data patterns into natural language questions that non-technical users can understand and interact with, bridging the gap between raw data and user comprehension without requiring technical expertise
Solution Approach 2:
The system implements a feedback loop where user interactions with generated questions are continuously monitored and used to refine future question generation. This feedback mechanism enables the system to learn from user preferences and improve its ability to surface interesting data patterns over time, directly addressing the loss of information problem
2Measurement precision
If manual data analysis is performed, then users can explore data, but it requires significant time and expertise
Solution Approach 1:
The system performs preliminary actions by automatically generating multiple candidate questions and pre-ranking them based on importance scores before user interaction. This preliminary processing eliminates the need for users to manually analyze data patterns from scratch, significantly reducing exploration time while maintaining detection accuracy through pre-computed rankings
Solution Approach 2:
The system enables self-service data exploration by autonomously generating and ranking questions without requiring user expertise or manual intervention. The automated question generation and ranking processes allow the system to serve itself in identifying interesting patterns, freeing users from time-consuming manual analysis while preserving measurement precision
3Adaptability or versatility
If aggregate questions are generated without ranking, then all possible questions are available, but users are overwhelmed by too many options
Solution Approach 1:
The patent segments the large set of generated aggregate questions into ranked subsets based on importance scores. Instead of presenting all questions simultaneously, the system divides them into prioritized groups, allowing users to focus on the most relevant questions first while maintaining access to the full range of questions if needed, thus reducing interface complexity without sacrificing question coverage
Solution Approach 2:
The system changes the parameter of question presentation from unranked to ranked based on computed importance scores. This parameter transformation organizes the vast number of possible aggregate questions into a manageable hierarchy, enabling users to navigate complex data landscapes efficiently while the system maintains adaptability by adjusting rankings based on user feedback and changing query contexts
Data Source
AI summary
Embodiments of the present invention provide an approach for exploring interesting data patterns in structured tables through recommending aggregate questions in a conversational data exploration. Specially, interesting features and operators are selected that are used to frame aggregate questions based on user intent and the data. The aggregate questions are ranked based on user persona and interestingness of the questions. The approach dynamically adapts and improves the recommendation of interesting and relevant aggregate questions for the user based on user feedback iteratively.