Conversational Data Analysis Using Heuristic Clarification
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Solution Overview
Problem
Conventional data analysis solutions struggle to accurately understand complex user intentions, often resulting in errors or incorrect results for complex data analysis requests, failing to provide users with the desired information.
Innovation Solution
A bi-directional conversational data analysis method and device that generates heuristic information to guide users in clarifying their requests, allowing for supplementary input and providing extended analysis results, thereby improving accuracy and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional single-input-box natural language processing is used, then the system is simple to operate, but it cannot accurately understand complex user intentions and provides incorrect results
Solution Approach 1:
The system employs a bidirectional conversational interface where the data analysis device provides feedback to the user through heuristic information (questions, suggestions, or explanations) about the user's input. This feedback loop allows the user to clarify their intentions by providing supplementary information, thereby improving the accuracy of understanding complex user intentions while maintaining a relatively simple interface structure.
2Reliability
If conventional single-input-box natural language processing is used, then the system has simple interface, but it fails to provide accurate data analysis results for complex requests
Solution Approach 1:
Before executing the data analysis, the system performs preliminary actions by generating heuristic information based on the user's initial input. This preliminary step involves analyzing the input to identify potential ambiguities or missing information, then presenting relevant questions or suggestions to the user beforehand. This allows the user to provide necessary supplementary information in advance, ensuring reliable analysis results while keeping the operation process simple and guided.
3Measurement precision
If bidirectional conversational approach with heuristic information is introduced, then accuracy of data analysis results improves, but the conversation process becomes more complex
Solution Approach 1:
The system applies partial action by generating heuristic information only when necessary - specifically when the initial user input is ambiguous, incomplete, or likely to lead to incorrect analysis results. Rather than always engaging in extended conversation, the system evaluates the input quality and only initiates the bidirectional conversational process when needed. This selective approach improves the precision of data analysis results for complex requests while avoiding unnecessary conversational complexity for simple, clear inputs.
Data Source
AI summary
Implementations of the subject matter described herein relate to conversational data analysis. After a data analysis request is received from a user, heuristic information may be determined based on the data analysis request. The heuristic information mentioned here is not a result for the data analysis request but information which may be used for leading the conversation to proceed. Based on such heuristic information, the user may provide supplementary information associated with the data analysis request, for example, clarify meaning of the data analysis request, submit a relevant further analysis request, and so on. A really desired and meaningful data analysis result can be provided to the user according to the supplementary information provided by the user. Thus, data analysis will become more accurate and effective. While obtaining really helpful information, the user also gains good user experience.