Natural Language Interface for Dynamic Data Visualization
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Solution Overview
Problem
Current data visualization tools are often complex and difficult to navigate, requiring expert modeling or simple closed-ended questions to provide limited interactive visualizations, failing to effectively utilize their functionality for users.
Innovation Solution
Implementing a natural language interface within data visualization applications that allows users to input ordinary questions or commands, extracting analytic phrases, computing semantic relatedness, and requerying databases to provide graphical answers and update visualizations dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a natural language interface is implemented to simplify user interaction, then ease of operation is improved, but device complexity increases due to the need for semantic processing and neural network models
Solution Approach 1:
The patent introduces a natural language interface as an intermediary layer between the user and the complex data visualization system. This interface includes a language processing module that translates natural language commands into system commands, and a command generation module that converts them into executable queries. This mediator handles the complexity internally while presenting a simple interface to users, resolving the contradiction between ease of operation and device complexity.
2Adaptability or versatility
If semantic relatedness computation using neural network models is implemented, then adaptability is improved, but loss of time increases due to training and processing requirements
Solution Approach 1:
The patent implements preliminary action by training neural network models (such as word2vec with continuous bag of words or skip-gram models) in advance to create pre-computed semantic representations and word embeddings. These pre-trained models store semantic relationships that can be quickly queried during runtime without requiring real-time training. This allows the system to maintain high adaptability in understanding natural language while significantly reducing the time required for processing user commands.
3Ease of operation
If complex functionality is made accessible through natural language, then ease of operation is improved, but device complexity increases due to multiple processing modules
Solution Approach 1:
The patent segments the complex functionality into distinct modular components: a language processing module for semantic analysis, a command generation module for query formulation, and execution modules for data retrieval and visualization. Each module has a specific responsibility and processes data in a pipeline fashion. This segmentation allows the system to provide comprehensive functionality through natural language while managing complexity through clear module boundaries and specialized functions.
Data Source
AI summary
A method uses natural language processing for visual analysis of a dataset by a computer. The computer displays a data visualization based on a dataset retrieved from a database using a set of one or more queries. The computer receives user input (e.g., keyboard or voice) to specify a natural language command related to the displayed data visualization. Based on the displayed data visualization, the computer extracts one or more independent analytic phrases from the natural language command. The computer further computes semantic relatedness of extracted analytic phrases, and computes analytical functions associated with the extracted analytic phrases, thereby creating functional phrases. The computer requeries the database using an updated set of one or more queries based on the created functional phrases, thereby retrieving an updated dataset. The computer then displays an updated data visualization using the updated dataset.