Conversational Interface Data Visualization Generation
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Solution Overview
Problem
Conversational interfaces lack integration of visualizations and natural language processing, making it difficult to effectively respond to data-related questions and varying user preferences for presentation formats.
Innovation Solution
A method that automatically generates and displays data visualizations in conversational interfaces by analyzing user inputs, determining question types, and adapting responses based on user preferences for text and visualizations, including generating charts and summaries for comparative, superlative, and trend questions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If data visualizations are integrated into conversational interfaces, then the ability to effectively respond to data-related questions is improved, but the complexity of the system increases
Solution Approach 1:
The patent combines natural language processing capabilities with data visualization generation within a single conversational interface system. The system merges text-based question answering with automatic chart generation, allowing users to interact with data through natural language while receiving both textual and visual responses integrated in one interface.
Solution Approach 2:
The conversational interface is designed to handle multiple types of data-related questions (comparative, superlative, trend questions) and automatically select appropriate visualization types. The system serves multiple functions including natural language understanding, database querying, visualization selection, and chart generation within a single unified interface.
2Ease of operation
If the system adapts responses based on user preferences for text and visualizations, then user experience is improved, but the processing complexity increases
Solution Approach 1:
The system incorporates user preference feedback by analyzing user interactions and adjusting the balance between text and visualization in responses. The system learns from user behavior patterns to automatically adapt the format and content of future responses, providing personalized user experiences while managing processing complexity through efficient feedback loops.
3Loss of information
If the system generates both text summaries and data visualizations, then information completeness is improved, but the response time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing user questions to determine the appropriate visualization type and preparing relevant data queries before actual data retrieval. The system analyzes the question structure and selects visualization templates in advance, which accelerates the overall response time while maintaining information completeness through coordinated text and visual generation.
Data Source
AI summary
A method incorporates data visualization into conversational interfaces. The method receives a user input specifying a natural language command via a conversational interface. The method analyzes the natural language command to determine the type of question. The method also obtains a user preference for viewing responses based on text and/or visualizations. When the user preference includes visualizations and the type of question is answerable using data visualizations, the method: (i) extracts one or more independent analytic phrases from the natural language command; (ii) queries a database using a set of queries based on the extracted analytic phrases, thereby retrieving a data set; and (iii) generates and displays, in the conversational interface, a response incorporating one or more data visualizations, based on the type of question, using the data set.