Natural Language Interface for Dynamic Data Visualization
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Solution Overview
Problem
Current data visualization tools are difficult to use for complex datasets, requiring expert modeling and limited to basic interactive visualizations, and often only provide static responses to queries, failing to effectively utilize their functionality.
Innovation Solution
A natural language interface is integrated into data visualization applications to enable users to interact with datasets using conversational queries, applying pragmatics principles to understand and respond to user commands, providing graphical answers and updating visualizations dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a natural language interface is integrated to enable conversational queries, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The patent introduces a natural language processing system as an intermediary layer between the user and the complex data visualization backend. This mediator translates conversational queries into structured analytical operations, shielding users from underlying system complexity while enabling sophisticated data exploration through simple natural language commands.
2Productivity
If dynamic updates are implemented based on sequential commands, then productivity is improved, but loss of information increases due to context management requirements
Solution Approach 1:
The system maintains continuous context across sequential natural language commands by implementing a persistent conversation state. Each command builds upon previous interactions, allowing users to explore data dynamically without repeating information. The context is preserved and updated continuously, enabling fluid multi-turn conversations that enhance productivity while retaining all relevant information.
3Ease of operation
If complex functionality is made accessible through natural language, then ease of operation is improved, but reliability decreases due to ambiguity in user input
Solution Approach 1:
The system implements feedback mechanisms that monitor and evaluate the interpretation of natural language commands. When ambiguity is detected, the system provides clarifying questions or multiple interpretation options to the user, ensuring that the intended meaning is confirmed before executing complex operations. This feedback loop maintains high reliability while preserving ease of operation.
Data Source
AI summary
A method uses natural language for visual analysis of a dataset. A data visualization is displayed based on a first dataset. The method then extracts analytic phrases from a natural language command related to the data visualization. The method computes conversation centers based on the analytic phrases and computes analytical functions associated with the conversation centers, thereby creating functional phrases. The method updates the data visualization according to the functional phrases. The method then extracts analytic phrases from a second natural language command related to the updated data visualization, and computes temporary conversation centers from these analytic phrases. The method then computes cohesion between the first analytic phrases and the second analytic phrases to build a set of conversation centers, and computes analytical functions from this set of conversation centers, thereby creating functional phrases. The method updates the data visualization based on the created functional phrases.